An Energy Efficient non-volatile FPGA Digital Processor for Brain Neuromodulation

PhD ThesisBrain stimulation technologies have the potential to provide considerable clinical benefits for people with a range of neurological disorders. Recent neuroscience studies have shown that considerable information of brain states is contained in the low frequency local field potential (If-LFP; below 5Hz) recordings with application in real-time closed-loop neurostimulation for treating neurological disorders. Given these signals can be sampled at low sampling rate and hence provide sparse data streams, there is an opportunity to design implantable neuroprosthesis with long battery lifecycles which enables enough processing power to implement long-term, real-time closed loop control algorithms. In this thesis, a closed-loop embedded digital processor has been created for use in rodent neuroscience experiments. The first contribution of this work is to develop a mathematical analytical design approach of feedback controller for suppressing high-amplitude epileptic activity in the neuron mass model to form a better understanding of how to perform a better closed-loop stimulation to control seizures. The second contribution and the third contribution are combined to present an exploratory energy-efficient digital processor architecture built with commercial off-the-shelf non-volatile FPGAs and microcontroller for sparse data processing of brain neuromodulation. A digital hardware design of an exemplar PID control algorithm has been implemented on this proposed digital architecture. A new power computing diagram of this time-driven approach significantly reduced the power consumption which suggests that a digital combined control system of non-volatile FPGAs and microcontroller outweighs a digital control system of microcontroller with microcontroller regarding computing time cost and energy consumption supposing one microcontroller is always required. Taken together, this digital energy-efficient processor architecture gives important insights and viewpoints for the further advancements of neuroprosthesis for brain neurostimulation to achieve lower power consumption for sparse sampling data rate

The neural engine: a reprogrammable low power platform for closed-loop optogenetics

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. By integrating with custom designed brain implant chip, we have demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system long-term recording performance. The overall system consumes only 2.93mA during operation with a biological recording frequency 50Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units

Investigation of high bandwith biodevices for transcutaneous wireless telemetry

PhD ThesisBIODEVICE implants for telemetry are increasingly applied today in various areas applications. There are many examples such as; telemedicine, biotelemetry, health care, treatments for chronic diseases, epilepsy and blindness, all of which are using a wireless infrastructure environment. They use microelectronics technology for diagnostics or monitoring signals such as Electroencephalography or Electromyography. Conceptually the biodevices are defined as one of these technologies combined with transcutaneous wireless implant telemetry (TWIT). A wireless inductive coupling link is a common way for transferring the RF power and data, to communicate between a reader and a battery-less implant. Demand for higher data rate for the acquisition data returned from the body is increasing, and requires an efficient modulator to achieve high transfer rate and low power consumption. In such applications, Quadrature Phase Shift Keying (QPSK) modulation has advantages over other schemes, and double the symbol rate with respect to Binary Phase Shift Keying (BPSK) over the same spectrum band. In contrast to analogue modulators for generating QPSK signals, where the circuit complexity and power dissipation are unsuitable for medical purposes, a digital approach has advantages. Eventually a simple design can be achieved by mixing the hardware and software to minimize size and power consumption for implantable telemetry applications. This work proposes a new approach to digital modulator techniques, applied to transcutaneous implantable telemetry applications; inherently increasing the data rate and simplifying the hardware design. A novel design for a QPSK VHDL modulator to convey a high data rate is demonstrated. Essentially, CPLD/FPGA technology is used to generate hardware from VHDL code, and implement the device which performs the modulation. This improves the data transmission rate between the reader and biodevice. This type of modulator provides digital synthesis and the flexibility to reconfigure and upgrade with the two most often languages used being VHDL and Verilog (IEEE Standard) being used as hardware structure description languages. The second objective of this thesis is to improve the wireless coupling power (WCP). An efficient power amplifier was developed and a new algorithm developed for auto-power control design at the reader unit, which monitors the implant device and keeps the device working within the safety regulation power limits (SAR). The proposed system design has also been modeled and simulated with MATLAB/Simulink to validate the modulator and examine the performance of the proposed modulator in relation to its specifications.Higher Education Ministry in Liby

Real-time neural signal processing and low-power hardware co-design for wireless implantable brain machine interfaces

Intracortical Brain-Machine Interfaces (iBMIs) have advanced significantly over the past two decades, demonstrating their utility in various aspects, including neuroprosthetic control and communication. To increase the information transfer rate and improve the devices’ robustness and longevity, iBMI technology aims to increase channel counts to access more neural data while reducing invasiveness through miniaturisation and avoiding percutaneous connectors (wired implants). However, as the number of channels increases, the raw data bandwidth required for wireless transmission also increases becoming prohibitive, requiring efficient on-implant processing to reduce the amount of data through data compression or feature extraction. The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following: Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30% with minor decoding performance degradation. In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget. The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following: Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30\% with only minor decoding performance degradation. In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget.Open Acces

An efficient telemetry system for restoring sight

PhD ThesisThe human nervous system can be damaged as a result of disease or trauma, causing conditions such as Parkinson’s disease. Most people try pharmaceuticals as a primary method of treatment. However, drugs cannot restore some cases, such as visual disorder. Alternatively, this impairment can be treated with electronic neural prostheses. A retinal prosthesis is an example of that for restoring sight, but it is not efficient and only people with retinal pigmentosa benefit from it. In such treatments, stimulation of the nervous system can be achieved by electrical or optical means. In the latter case, the nerves need to be rendered light sensitive via genetic means (optogenetics). High radiance photonic devices are then required to deliver light to the target tissue. Such optical approaches hold the potential to be more effective while causing less harm to the brain tissue. As these devices are implanted in tissue, wireless means need to be used to communicate with them. For this, IEEE 802.15.6 or Bluetooth protocols at 2.4GHz are potentially compatible with most advanced electronic devices, and are also safe and secure. Also, wireless power delivery can operate the implanted device. In this thesis, a fully wireless and efficient visual cortical stimulator was designed to restore the sight of the blind. This system is likely to address 40% of the causes of blindness. In general, the system can be divided into two parts, hardware and software. Hardware parts include a wireless power transfer design, the communication device, power management, a processor and the control unit, and the 3D design for assembly. The software part contains the image simplification, image compression, data encoding, pulse modulation, and the control system. Real-time video streaming is processed and sent over Bluetooth, and data are received by the LPC4330 six layer implanted board. After retrieving the compressed data, the processed data are again sent to the implanted electrode/optrode to stimulate the brain’s nerve cells

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

Developing preclinical devices for neuroscience research in the fields of animal tracking, fMRI acquisition, and 3D histology cutting

[ES] La neurociencia es un campo que abarca muchas especialidades. El objetivo de esta tesis es subsanar algunas carencias tecnológicas que existen en los métodos actuales de experimentación animal en neurociencia. En esta tesis, se presentan seis proyectos, que tendrán como objetivo mejorar el "Principio de las tres R", el cual fue enunciado por los biólogos ingleses W. M. S. Russell y R. L. Burch, durante la experimentación animal. El comportamiento es uno de los aspectos más importantes de la vida animal. Depende de los vínculos entre los animales, sus sistemas nerviosos y sus entornos. Para estudiar el comportamiento de los animales de laboratorio, se necesitan varias herramientas, pero una herramienta de seguimiento es esencial para llevar a cabo un estudio de comportamiento exhaustivo. Varias herramientas de seguimiento visual están actualmente disponibles. Sin embargo, todas tienen algunos inconvenientes. Por ejemplo, en una situación en la que un animal está dentro de una madriguera o cerca de otros animales, las cámaras de rastreo (tracking) no siempre pueden detectar la ubicación precisa o el movimiento del animal. Por esta razón, los entornos enriquecidos para intentar recrear el hábitat natural de los animales en experimentación no pueden utilizarse, ya que los datos recopilados son insuficientes/inexactos. Con la finalidad de mejorar los experimentos de tracking RFID Assisted Tracking Tile (RATT) es presentado en esta tesis. RATT es un sistema de seguimiento basado en tecnología de identificación pasiva de radiofrecuencia (RFID) y está compuesto por baldosas electrónicas con las que se puede construir una gran superficie, sobre la cual los animales pueden moverse libremente. Esto permite la identificación más precisa de los animales, así como el seguimiento de sus movimientos. Este sistema, que también se puede combinar con un sistema de seguimiento con cámaras, allana el camino para estudios completos de comportamiento en entornos enriquecidos. Dada la capacidad de rastrear animales y, por lo tanto, realizar experimentos de comportamiento exhaustivos, es posible observar cómo se comportan los sujetos desde un punto de vista externo. Sin embargo, si queremos comprender lo que sucede en el cerebro de estos sujetos, es necesario aplicar otras técnicas de análisis, por ejemplo, el estudio de señales dependientes del nivel de oxígeno en la sangre (BOLD, por sus siglas en inglés). Las señales BOLD se basan en las respuestas vasculares a la activación neuronal y se utilizan ampliamente en estudios de investigación clínicos y preclínicos. En entornos preclínicos, los animales suelen ser anestesiados. Sin embargo, los anestésicos causan cambios en la fisiología de los animales, p. Ej. hipotermia, y esto tiene el potencial de alterar las señales funcionales de MRI (fMRI). Para evitar la hipotermia en roedores anestesiados, se presenta TherMouseDuino. Este es un sistema de control automático de temperatura de código abierto, que reduce las fluctuaciones de la temperatura, lo que proporciona condiciones sólidas para realizar experimentos de resonancia magnética funcional. En los cursos de biología y neurociencia, la anatomía del cerebro se enseña generalmente utilizando imágenes de resonancia magnética (IRM) o secciones histológicas de diferentes planos. Estos muestran las áreas macroscópicas más importantes en el cerebro de un animal. Sin embargo, este método no es dinámico ni intuitivo. En esta tesis se presenta un cerebro de rata impreso en 3D con fines educativos. La manipulación manual de la estructura, facilitada por la ampliación de sus dimensiones, junto con la capacidad de desmontar el "cerebro" en algunas de sus partes principales, facilita la comprensión de la organización 3D del sistema nervioso. Este es un método alternativo y mejorado para enseñar a los estudiantes en general y a los biólogos, en particular, la anatomía del cerebro de rata.[CA] La neurociència és un camp que abasta moltes especialitats. L'objectiu d'aquesta tesi és esmenar algunes manques tecnològiques que existeixen en els mètodes actuals d'experimentació animal en neurociència. En aquesta tesi, es presenten sis projectes, que tindran com a objectiu millorar el "Principi de les tres R", el qual va ser enunciat pels biòlegs anglesos W. M. S. Russell i R. L. Burch, durant l'experimentació animal. El comportament és un dels aspectes m'és importants de la vida animal. Depèn dels vincles entre els animals, els seus sistemes nerviosos i els seus entorns. Per estudiar el comportament dels animals de laboratori, es necessiten diverses eines, però` una eina de seguiment és essencial per a dur a terme un estudi de comportament exhaustiu. Diverses eines de seguiment visual estan actualment disponibles. No obstant això, totes tenen alguns inconvenients. Per exemple, en una situació en la qual un animal esta` dins d'un cau o prop d'altres animals, les cambres de rastreig (tracking) no sempre poden detectar la ubicació precisa o el moviment de l'animal. Per aquesta raó, els entorns enriquits per a intentar recrear l'hàbitat natural dels animals en experimentació no poden utilitzar-se, ja que les dades recopilades són insuficients/inexactes. Amb la finalitat de millorar els experiments de tracking/seguiment RFID Assisted Tracking Tile (RATT) és presentat en aquesta tesi. RATT es un sistema de seguiment basat en tecnologia d'identificació passiva de radiofreqüència (RFID) i esta` compost per rajoles electròniques amb les quals es pot construir una gran superfície, sobre la qual els animals poden moures lliurement. Això permet la identificació més precisa dels animals, així com el seguiment dels seus moviments. Aquest sistema, que també es pot combinar amb un sistema de seguiment amb cambres, aplana el camí per a estudis complets de comportament en entorns enriquits. Donada la capacitat de rastrejar animals i, per tant, realitzar experiments de comportament exhaustius, és possible observar com es comporten els subjectes des d'un punt de vista extern. No obstant això, si volem comprendre el que succeeix en el cervell d'aquests subjectes, és necessari aplicar altres tècniques d'anàlisis, per exemple, l'estudi de senyals dependents del nivell d'oxigen en la sang (BOLD, per les seues sigles en anglès). Els senyals BOLD es basen en les respostes vasculars a l'activació neuronal i s'utilitzen àmpliament en estudis d'investigació clínics i preclínics. En entorns preclínics, els animals solen ser anestesiats. No obstant això, els anestèsics causen canvis en la fisiologia de els animals, per exemple hipotèrmia, i això te el potencial d'alterar els senyals funcionals de MRI (fMRI). Per a evitar la hipotèrmia en rosegadors anestesiats, es presenta TherMouseDuino. Aquest és un sistema de control automàtic de temperatura de codi obert, que redueix les fluctuacions de la temperatura, la qual cosa proporciona condicions solides per a realitzar experiments de ressonància magnètica funcional. En els cursos de biologia i neurociència, l'anatomia del cervell s'ensenya generalment utilitzant imatges de ressonància magnètica (IRM) o seccions histològiques de diferents plans. Aquests mostren les àrees macroscòpiques més importants en el cervell de un animal. No obstant això, aquest mètode no és dinàmic ni intuïtiu. En aquesta tesi es presenta un cervell de rata imprès en 3D amb finalitats educatius. La manipulació manual de l'estructura, facilitada per l'ampliació de les seues dimensions, juntament amb la capacitat de desmuntar el "cervell" en algunes de les seues parts principals, facilita la comprensió de l'organització 3D del sistema nerviós. Aquest és un mètode alternatiu i millorat per a ensenyar a els estudiants en general i als biòlegs, en particular, l'anatomia del cervell de rata.[EN] Neuroscience is a field that covers many specialties. The objective of this thesis is to correct some technological deficiencies that exist in current methods of animal experimentation in neuroscience. In this thesis, six projects are presented, which will aim to improve the "Principle of the three Rs" in animal experimentation enunciated by the English biologists W. M. S. Russell and R. L. Burch. In the present era of impressive progress in neuroscience, it is still not arguable that a complete understanding of the brain cannot be possible without a comparable understanding of animal behavior. In order to study the behavior of laboratory animals, various tools are needed, being a reliable tracking system one of the most important to follow large populations of individual subjects that interact in complex manners. Several visual tracking tools are currently available. However, they all have some drawbacks. For example, in a situation where an animal is inside a cave, or is in close proximity to other animals, tracking cameras cannot always detect the precise location or movement of the animal. For this reason, environments that have been enriched in order to attempt to recreate the natural habitat of the animals under experiment, cannot be used, as the data gathered is insufficient/inaccurate. In order to improve the current tracking systems , the RATT is presented. RATT is a tracking system based on passive RFID technology and it is composed of electronic tiles. Using several tiles, a large surface area, on which the animals can move freely, can be built. This enables the more accurate identification of the animals, as well as the tracking of their movements. This system, which can also be combined with a visual tracking system, paves the way for complete behavioral studies in enriched environments. Given the ability to track animals and thus conduct thorough behavioral experiments, it is possible to observe how the subjects behave from an external viewpoint. However, if we want to understand what is going on in the brains of these subjects, it is necessary to apply other analysis techniques, for example the study of BOLD signals. BOLD signals are based on vascular responses to neuronal activation and are used extensively in clinical and preclinical research studies. In preclinical settings, animals are usually anesthetized. However, anesthetics cause changes in the physiology of the animals, e.g. hypothermia, and this has the potential to disrupt fMRI signals. In order to avoid hypothermia in anesthetized rodents, TherMouseDuino is presented. This is an Open-Source automatic temperature control system, which reduces temperature fluctuations, thus providing robust conditions in which to perform fMRI experiments. In biology and neuroscience courses, brain anatomy is generally taught using MRI or histological sections of different planes. These show the most important macroscopic areas in an animals' brain. However, this method is neither dynamic nor intuitive. An anatomical 3D printed rat brain with educative purposes is presented in this thesis. Hand manipulation of the structure, facilitated by the scaling up of its dimensions, together with the ability to dismantle the "brain" into some of main its constituent parts, facilitates the understanding of the 3D organization of the nervous system. This is an alternative and improved method for teaching students in general and biologists, in particular, the rat brain anatomy.This work was supported in part by the Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-2-R (D.M.) and BFU2015-64380-C2-1-R, by EU Horizon 2020 Program 668863-SyBil-AA grant (S.C.). S.C. acknowledges financial support from the Spanish State Research Agency, through the “Severo Ochoa” Programme for Centres of Excellence in R&D (ref. SEV-2013-0317) and by a grant “Ayudas para la formación de personal investigador (FPI)” from the Vicerrectorado de Investigación, Innovación y Transferencia of the Universitat Politècnica de València.Quiñones Colomer, DR. (2019). Developing preclinical devices for neuroscience research in the fields of animal tracking, fMRI acquisition, and 3D histology cutting [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/118795TESI

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology

Design, control and error analysis of a fast tool positioning system for ultra-precision machining of freeform surfaces

This thesis was previously held under moratorium from 03/12/19 to 03/12/21Freeform surfaces are widely found in advanced imaging and illumination systems, orthopaedic implants, high-power beam shaping applications, and other high-end scientific instruments. They give the designers greater ability to cope with the performance limitations commonly encountered in simple-shape designs. However, the stringent requirements for surface roughness and form accuracy of freeform components pose significant challenges for current machining techniques—especially in the optical and display market where large surfaces with tens of thousands of micro features are to be machined. Such highly wavy surfaces require the machine tool cutter to move rapidly while keeping following errors small. Manufacturing efficiency has been a bottleneck in these applications. The rapidly changing cutting forces and inertial forces also contribute a great deal to the machining errors. The difficulty in maintaining good surface quality under conditions of high operational frequency suggests the need for an error analysis approach that can predict the dynamic errors. The machining requirements also impose great challenges on machine tool design and the control process. There has been a knowledge gap on how the mechanical structural design affects the achievable positioning stability. The goal of this study was to develop a tool positioning system capable of delivering fast motion with the required positioning accuracy and stiffness for ultra-precision freeform manufacturing. This goal is achieved through deterministic structural design, detailed error analysis, and novel control algorithms. Firstly, a novel stiff-support design was proposed to eliminate the structural and bearing compliances in the structural loop. To implement the concept, a fast positioning device was developed based on a new-type flat voice coil motor. Flexure bearing, magnet track, and motor coil parameters were designed and calculated in detail. A high-performance digital controller and a power amplifier were also built to meet the servo rate requirement of the closed-loop system. A thorough understanding was established of how signals propagated within the control system, which is fundamentally important in determining the loop performance of high-speed control. A systematic error analysis approach based on a detailed model of the system was proposed and verified for the first time that could reveal how disturbances contribute to the tool positioning errors. Each source of disturbance was treated as a stochastic process, and these disturbances were synthesised in the frequency domain. The differences between following error and real positioning error were discussed and clarified. The predicted spectrum of following errors agreed with the measured spectrum across the frequency range. It is found that the following errors read from the control software underestimated the real positioning errors at low frequencies and overestimated them at high frequencies. The error analysis approach thus successfully revealed the real tool positioning errors that are mingled with sensor noise. Approaches to suppress disturbances were discussed from the perspectives of both system design and control. A deterministic controller design approach was developed to preclude the uncertainty associated with controller tuning, resulting in a control law that can minimize positioning errors. The influences of mechanical parameters such as mass, damping, and stiffness were investigated within the closed-loop framework. Under a given disturbance condition, the optimal bearing stiffness and optimal damping coefficients were found. Experimental positioning tests showed that a larger moving mass helped to combat all disturbances but sensor noise. Because of power limits, the inertia of the fast tool positioning system could not be high. A control algorithm with an additional acceleration-feedback loop was then studied to enhance the dynamic stiffness of the cutting system without any need for large inertia. An analytical model of the dynamic stiffness of the system with acceleration feedback was established. The dynamic stiffness was tested by frequency response tests as well as by intermittent diamond-turning experiments. The following errors and the form errors of the machined surfaces were compared with the estimates provided by the model. It is found that the dynamic stiffness within the acceleration sensor bandwidth was proportionally improved. The additional acceleration sensor brought a new error source into the loop, and its contribution of errors increased with a larger acceleration gain. At a certain point, the error caused by the increased acceleration gain surpassed other disturbances and started to dominate, representing the practical upper limit of the acceleration gain. Finally, the developed positioning system was used to cut some typical freeform surfaces. A surface roughness of 1.2 nm (Ra) was achieved on a NiP alloy substrate in flat cutting experiments. Freeform surfaces—including beam integrator surface, sinusoidal surface, and arbitrary freeform surface—were successfully machined with optical-grade quality. Ideas for future improvements were proposed in the end of this thesis.Freeform surfaces are widely found in advanced imaging and illumination systems, orthopaedic implants, high-power beam shaping applications, and other high-end scientific instruments. They give the designers greater ability to cope with the performance limitations commonly encountered in simple-shape designs. However, the stringent requirements for surface roughness and form accuracy of freeform components pose significant challenges for current machining techniques—especially in the optical and display market where large surfaces with tens of thousands of micro features are to be machined. Such highly wavy surfaces require the machine tool cutter to move rapidly while keeping following errors small. Manufacturing efficiency has been a bottleneck in these applications. The rapidly changing cutting forces and inertial forces also contribute a great deal to the machining errors. The difficulty in maintaining good surface quality under conditions of high operational frequency suggests the need for an error analysis approach that can predict the dynamic errors. The machining requirements also impose great challenges on machine tool design and the control process. There has been a knowledge gap on how the mechanical structural design affects the achievable positioning stability. The goal of this study was to develop a tool positioning system capable of delivering fast motion with the required positioning accuracy and stiffness for ultra-precision freeform manufacturing. This goal is achieved through deterministic structural design, detailed error analysis, and novel control algorithms. Firstly, a novel stiff-support design was proposed to eliminate the structural and bearing compliances in the structural loop. To implement the concept, a fast positioning device was developed based on a new-type flat voice coil motor. Flexure bearing, magnet track, and motor coil parameters were designed and calculated in detail. A high-performance digital controller and a power amplifier were also built to meet the servo rate requirement of the closed-loop system. A thorough understanding was established of how signals propagated within the control system, which is fundamentally important in determining the loop performance of high-speed control. A systematic error analysis approach based on a detailed model of the system was proposed and verified for the first time that could reveal how disturbances contribute to the tool positioning errors. Each source of disturbance was treated as a stochastic process, and these disturbances were synthesised in the frequency domain. The differences between following error and real positioning error were discussed and clarified. The predicted spectrum of following errors agreed with the measured spectrum across the frequency range. It is found that the following errors read from the control software underestimated the real positioning errors at low frequencies and overestimated them at high frequencies. The error analysis approach thus successfully revealed the real tool positioning errors that are mingled with sensor noise. Approaches to suppress disturbances were discussed from the perspectives of both system design and control. A deterministic controller design approach was developed to preclude the uncertainty associated with controller tuning, resulting in a control law that can minimize positioning errors. The influences of mechanical parameters such as mass, damping, and stiffness were investigated within the closed-loop framework. Under a given disturbance condition, the optimal bearing stiffness and optimal damping coefficients were found. Experimental positioning tests showed that a larger moving mass helped to combat all disturbances but sensor noise. Because of power limits, the inertia of the fast tool positioning system could not be high. A control algorithm with an additional acceleration-feedback loop was then studied to enhance the dynamic stiffness of the cutting system without any need for large inertia. An analytical model of the dynamic stiffness of the system with acceleration feedback was established. The dynamic stiffness was tested by frequency response tests as well as by intermittent diamond-turning experiments. The following errors and the form errors of the machined surfaces were compared with the estimates provided by the model. It is found that the dynamic stiffness within the acceleration sensor bandwidth was proportionally improved. The additional acceleration sensor brought a new error source into the loop, and its contribution of errors increased with a larger acceleration gain. At a certain point, the error caused by the increased acceleration gain surpassed other disturbances and started to dominate, representing the practical upper limit of the acceleration gain. Finally, the developed positioning system was used to cut some typical freeform surfaces. A surface roughness of 1.2 nm (Ra) was achieved on a NiP alloy substrate in flat cutting experiments. Freeform surfaces—including beam integrator surface, sinusoidal surface, and arbitrary freeform surface—were successfully machined with optical-grade quality. Ideas for future improvements were proposed in the end of this thesis