Towards Human-Robot Collaboration with Parallel Robots by Kinetostatic Analysis, Impedance Control and Contact Detection

Parallel robots provide the potential to be lever-aged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving masses. However, the risk of unintended contact with the leg chains increases compared to the structure of serial robots. As a first step towards HRC, contact cases on the whole parallel robot structure are investigated and a disturbance observer based on generalized momenta and measurements of motor current is applied. In addition, a Kalman filter and a second-order sliding-mode observer based on generalized momenta are compared in terms of error and detection time. Gearless direct drives with low friction improve external force estimation and enable low impedance. The experimental validation is performed with two force-torque sensors and a kinetostatic model. This allows a new identification method of the motor torque constant of an assembled parallel robot to estimate external forces from the motor current and via a dynamics model. A Cartesian impedance control scheme for compliant robot-environmental dynamics with stiffness from 0.1-2N/mm and the force observation for low forces over the entire structure are validated. The observers are used for collisions and clamping at velocities of 0.4-0.9 m/s for detection within 9–58 ms and a reaction in the form of a zero-g mode.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Contact force and torque estimation for collaborative manipulators based on an adaptive Kalman filter with variable time period.

Contact force and torque sensing approaches enable manipulators to cooperate with humans and to interact appropriately with unexpected collisions. In this thesis, various moving averages are investigated and Weighted Moving Averages and Hull Moving Average are employed to generate a mode-switching moving average to support force sensing. The proposed moving averages with variable time period were used to reduce the effects of measured motor current noise and thus provide improved confidence in joint output torque estimation. The time period of the filter adapts continuously to achieve an optimal trade-off between response time and precision of estimation in real-time. An adaptive Kalman filter that consists of the proposed moving averages and the conventional Kalman filter is proposed. Calibration routines for the adaptive Kalman filter interpret the measured motor current noise and errors in the speed data from the individual joints into. The combination of the proposed adaptive Kalman filter with variable time period and its calibration method facilitates force and torque estimation without direct measurement via force/torque sensors. Contact force/torque sensing and response time assessments from the proposed approach are performed on both the single Universal Robot 5 manipulator and the collaborative UR5 arrangement (dual-arm robot) with differing unexpected end effector loads. The combined force and torque sensing method leads to a reduction of the estimation errors and response time in comparison with the pioneering method (55.2% and 20.8 %, respectively), and the positive performance of the proposed approach is further improved as the payload rises. The proposed method can potentially be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available. Consequently the cost of implementation will be significantly lower than methods that require load cells

State estimators in soft sensing and sensor fusion for sustainable manufacturing

State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables which cannot be measured directly (’soft sensing’) or for which only noisy, intermittent, delayed, indirect or unreliable measurements are available, perhaps from multiple sources (’sensor fusion’). In this paper we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes. It is shown that state estimation algorithms can play a key role in manufacturing systems to accurately monitor and control processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and control of distributed manufacturing systems

Speed sensorless and MPPT control of IPM synchronous generator for wind energy conversion system

The popularity of renewable energy has experienced significant growth recently due to the foreseeable exhaustion of conventional fossil fuel power generation methods and increasing realization of the adverse effects that conventional fossil fuel power generation has on the environment. Among the renewable energy sources, wind power generation is rapidly becoming competitive with conventional fossil fuel sources. The wind turbines in the market have a variety of innovative concepts, with proven technology for both generators and power electronics interfaces. Recently, variable-speed permanent magnet synchronous generator (PMSG) based wind energy conversion systems (WECS) is becoming more attractive in comparison to the fixed-speed WECS. In the variable-speed generation system, the wind turbine can be operated at maximum power operating points over a wide speed range by adjusting the shaft speed optimally. This thesis presents both wind and rotor speed sensorless control for the direct-drive interior permanent magnet synchronous generator (IPMSG) with maximum power point tracking (MPPT) algorithm. The proposed method, without requiring the knowledge of wind speed, air density or turbine parameters, generates optimum speed command for speed control loop of vector controlled machine side converter. The MPPT algorithm based on perturbation and observation uses only estimated active power as its input to track peak output power points in accordance with wind speed change and incorporates proposed sensorless control to transfer maximum dc-link power from generator. In this work for the IPMSG, the rotor position and speed are estimated based on model reference adaptive system. Additionally, it incorporates flux weakening controller (FWC) for wide operating speed range at various wind speed and other disturbances. Matlab/Simulink based simulation model of the proposed sensorless MPPT control of IPMSG based WECS is built to verify the effectiveness of the system. The MPPT controller has been tested for variable wind speed conditions. The performance of the proposed WECS is also compared with the conventional control of WECS system. The proposed IPMSG based WECS incorporating the MPPT and sensorless algorithms is successfully implemented in real-time using the digital signal processor (DSP) board DS1104 for a laboratory 5 hp machine. A 5 hp DC motor is used as wind turbine to drive the IPMSG. The speed tracking performance and maximum power transfer capability of the proposed WECS are verified by both simulation and experimental results at different speed conditions

Peripheral control tools for a run-of-mine ore milling circuit

Run-of-mine ore milling circuits are generally difficult to control owing to the presence of strong external disturbances, poor process models and the unavailability of important process variable measurements. These shortcomings are common for processes in the mineral-processing industry. For processes that fall into this class, the peripheral control tools in the control loop are considered to be as important as the controller itself. This work addresses the implementation of peripheral control tools on a run-of-mine ore milling circuit to help overcome the deteriorated control performance resulting from the aforementioned shortcomings. The effects of strong external disturbances are suppressed through the application of a disturbance observer. A fractional order disturbance observer is also implemented and a novel Bode ideal cutoff disturbance observer is introduced. The issue of poor process models is addressed through the detection of significant mismatch between the actual plant and the available model from process data. A closed-form expression is given for the case where the controller has a transfer function. If the controller does not have a transfer function, a partial correlation analysis is used to detect the transfer function elements in the model transfer function matrix that contain significant mismatch. The mill states and important mill parameters are estimated with the use of particle filters. Simultaneous state and parameter estimation is compared with a novel dual particle filtering scheme. A sensitivity analysis shows the class of systems for which dual estimation would provide superiorestimation accuracy over simultaneous estimation. The implemented peripheral control tools show promise for current milling circuits where proportional-integral-derivative (PID) control is prevalent, and also for advanced control strategies, such as model predictive control, which are expected to become more common in the future. AFRIKAANS : Maalkringe wat onbehandelde erts maal is oor die algemeen moeilik om te beheer as gevolg van die teenwoordigheid van sterk eksterne steurings, onakkurate aanlegmodelle en metings van belangrike prosesveranderlikes wat ontbreek. Hierdie probleme is algemeen vir aanlegte in die mineraalprosesseringsbedryf. Vir aanlegte in hierdie klas word die randbeheerinstrumente as net so belangrik as die beheerder beskou. Hierdie verhandeling beskryf die implementering van randbeheerinstrumente vir ’n maalkring wat onbehandelde erts maal, om die verswakte beheerverrigting teen te werk wat veroorsaak word deur bogenoemde probleme. Die impak van sterk eksterne steurings word teengewerk deur die implementering van ’n steuringsafskatter. ’n Breuk-orde-steuringsafskatter is ook geïmplementeer en ’n nuwe Bode ideale afsnysteuringsafskatter word voorgestel. Die kwessie van onakkurate aanlegmodelle word hanteer deur van die aanlegdata af vas te stel of daar ’n verskil is tussen die aanleg en die beskikbare model van die aanleg. ’n Uitdrukking word gegee vir hierdie verskil vir die geval waar die beheerder met ’n oordragsfunksie voorgestel kan word. Indien die beheerder nie ’n oordragsfunksie het nie, word van ‘n parsiële korrelasie-analise gebruik gemaak om die element, of elemente, in die aanleg se oordragsfunksiematriks te identifiseer wat van die werklike aanleg verskil. Die toestande en belangrike parameters in die meul word beraam deur van partikel-filters gebruikte maak. Gelyktydige toestand- en parameter-beraming word vergelyk met ’n nuwe dubbel-partikelfilter skema. ’n Sensitiwiteitsanalise wys die klas van stelsels waarvoor dubbel-afskatting meer akkurate waardes sal gee as gelyktydige afskatting. Die voorgestelde randbeheerinstrumente is toepaslik vir huidige maalkringe waar PID-beheer algemeen is, asook vir gevorderde beheerstrategieë, soos model-voorspellende beheer, wat na verwagting in die toekoms meer algemeen sal word. CopyrightDissertation (MEng)--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte

Analysis of closed - loop therapy in type ii diabetes.

La diabetes mellitus es una pandemia mundial, la cual ha incrementado su prevalencia en los últimos años. Esta enfermedad es principalmente caracterizada por el aumento de los niveles basales de glucosa en sangre, llamado hiperglicemia. De acuerdo a las causas, la diabetes mellitus puede ser clasificada en tres tipos: i) gestacional, relacionada con desbalances hormonales y metabólicos durante el ´ embarazo, ii) tipo 1, la cual es una enfermedad autoinmune caracterizada por la muerte progresiva de las celulas beta pancreaticas, las cuales se encargan de producir insulina, la hormona principal en el metabolismo de glucosa; y iii) tipo 2, la cual es una enfermedad metabolica caracterizada por el uso y producción disfuncional de la insulina. La diabetes mellitus tipo 2 es la mas recurrente, incluyendo mas del 95 % de casos clınicos. Algunos estudios recientes han mostrado que la implementación de la automatización en la dosificación de insulina para ´ pacientes con diabetes tipo 2 puede mejorar su tratamiento. Basándose en este ´ problema, el principal objetivo de esta tesis es desarrollar una metodología para analizar la viabilidad de tener una terapia en lazo cerrado para diabetes tipo 2. El estudio incluye la revisión de dos modelos matematicos del metabolismo de glucosa en sangre de gran utilidad para la síntesis de esquemas de control. Además, se presenta una metodología para personalizar este tipo de modelos basados en informació metabolica proveniente del monitoreo continuo de glucosa en pacientes con diabetes tipo 2. Después, se muestra el analisis de algunas caracterısticas de los modelos, su rol en la terapia de lazo cerrado y un caso de estudio usando un control convencional. ABSTRACT Diabetes mellitus is a worldwide pandemic, which prevalence has increased in the last years. This disease is mainly characterized by increased basal blood glucose levels, called hyperglycemia. According to the causes, diabetes mellitus can be classified in three types: i) gestational, related to hormonal and metabolic imbalances during pregnancy, ii) type 1 diabetes, which is an immune disease characterized by the progressive death of pancreatic βcells, that produce insulin, the principal hormone in glucose metabolism; and iii) type 2 diabetes, which is a metabolic disease characterized by dysfunction use and production of insulin. Type 2 diabetes mellitus is the most recurrent one, including more than 95 % of the clinical cases. Some recent studies have shown that the implementation of the automation of insulin dosage for type 2 diabetes patients can improve their treatment. Based on this problem, the main objective of this thesis is to develop a methodology to analyze the viability of having a closed-loop therapy for type 2 diabetes mellitus. The study includes the revision on two mathematical models of blood glucose metabolism useful to synthesize control schemes. Moreover, a methodology to personalize this kind of models based on metabolic data from continuous glucose monitoring of type 2 diabetic patients is presented. After that, the analysis of some characteristics of the models, their role in closed-loop therapy and a case of study using a conventional control scheme are presented

Impact of Ear Occlusion on In-Ear Sounds Generated by Intra-oral Behaviors

We conducted a case study with one volunteer and a recording setup to detect sounds induced by the actions: jaw clenching, tooth grinding, reading, eating, and drinking. The setup consisted of two in-ear microphones, where the left ear was semi-occluded with a commercially available earpiece and the right ear was occluded with a mouldable silicon ear piece. Investigations in the time and frequency domains demonstrated that for behaviors such as eating, tooth grinding, and reading, sounds could be recorded with both sensors. For jaw clenching, however, occluding the ear with a mouldable piece was necessary to enable its detection. This can be attributed to the fact that the mouldable ear piece sealed the ear canal and isolated it from the environment, resulting in a detectable change in pressure. In conclusion, our work suggests that detecting behaviors such as eating, grinding, reading with a semi-occluded ear is possible, whereas, behaviors such as clenching require the complete occlusion of the ear if the activity should be easily detectable. Nevertheless, the latter approach may limit real-world applicability because it hinders the hearing capabilities.</p

A synchronised multi-motor control system using hybrid sensorless induction motor drives

The main aim of this project was to research, develop and test an induction motor drive not requiring a speed encoder, but which could be considered commercially viable by motor drives manufacturers, and which should aim to meet the follow requirements: • Dynamic torque performance and steady state speed-holding accuracy to be comparable with encodered vector controlled drives • Extensive and highly accurate knowledge of electrical and mechanical parameters of the motor and load not to be required • Extensive commissioning from an expert engineer not to be necessary • Algorithm not to rely on excessive computational capability being available The drive was to operate, in a stable manner, over speed and load ranges at least comparable with commercially available sensorless induction motor drives. The above requirements were set such that the developed sensorless technique may be considered for synchronised multi-motor process applications, where the advantages of a sensorless system could be exploited for hazardous, damp and hot conditions. The solution developed consists of a leading model-based sensorless method augmented with a speed estimator that tracks harmonics, seen in the stator terminal quantities, due to rotor slotting. The model-based scheme facilitates field-orientated control for dynamic performance. The slot harmonic speed estimator tunes the model for speed accuracy. Slot harmonics are identified using a recursive signal processing method termed the Recursive Maximum Likelihood - Adaptive Tracking Filter. This work is the first example of the method being developed into a practical sensorless drive system and the complete speed identifier is described, including set-up, pre-filtering and the minimal parameter considerations. Being recursive the method is computationally efficient, yet has accuracy comparable with that of FFT identifiers used in other work. The developed sensorless strategy was implemented practically on two motor drive systems. The performance of the scheme is shown to give encoder like speed holding accuracy and field-orientated dynamic performance. The two drives were also configured and tested as a speed synchronised pair, using applicable multi-motor control techniques, themselves compared and contrasted. The sensorless performance is demonstrated, alongside an encodered version acting as a benchmark, and the performance of the two schemes is shown to be highly comparable. The author has found no other example of sensorless techniques considered for use in multi-motor applications. The use of such a technique brings established advantages associated with encoder removal and allows multi-axis electronic synchronisation to be considered for parts of a process where an encoder may not be appropriate

Robust control techniques for DFIG driven WECS with improved efficiency

Wind energy has emerged as one of the fastest growing renewable energy sources since mid-80‘s due to its low cost and environmentally friendly nature compared to conventional fossil fuel based power generation. Current technologies for the design and implementation of wind energy conversion systems (WECSs) include induction generator and synchronous generator based units. The doubly fed induction generator (DFIG) is chosen in this thesis because of its economic operation, ability to regulate in sub-synchronous or super-synchronous speed and decoupled control of active and reactive powers. Among the major challenges of wind energy conversion system, extraction of maximum power from intermittent generation and supervision on nonlinear system dynamics of DFIG-WECS are of critical importance. Maximization of the power produced by wind turbine is possible by optimizing tip-speed ratio (TSR), turbine rotor speed or torque and blade angle. The literature reports that a vast number of investigations have been conducted on the maximum power point tracking (MPPT) of wind turbines. Among the reported MPPT control algorithms, the hill climb search (HCS) method is typically preferred because of its simple implementation and turbine parameter-independent scheme. Since the conventional HCS algorithm has few drawbacks such as power fluctuation and speed-efficiency trade-off, a new adaptive step size based HCS controller is developed in this thesis to mitigate its deficiencies by incorporating wind speed measurement in the controller. In addition, a common practice of using linear state-feedback controllers is prevalent in speed and current control of DFIG-based WECS. Traditional feedback linearization controllers are sensitive to system parameter variations and disturbances on grid-connected WECS, which demands advanced control techniques for stable and efficient performance considering the nonlinear system dynamics. An adaptive backstepping based nonlinear control (ABNC) scheme with iron-loss minimization algorithm for RSC control of DFIG is developed in this research work to obtain improved dynamic performance and reduced power loss. The performance of the proposed controller is tested and compared with the benchmark tuned proportional-integral (PI) controller under different operating conditions including variable wind speed, grid voltage disturbance and parameter uncertainties. Test results demonstrate that the proposed method exhibits excellent performance on the rotor side and grid side converter control. In addition, the compliance with the modern grid-code requirements is achieved by featuring a novel controller with disturbance rejection mechanism. In order to reduce the dependency on system‘s mathematical model, a low computational adaptive network fuzzy interference system (ANFIS) based neuro-fuzzy logic controller (NFC) scheme is developed for DFIG based WECS. The performance of the proposed NFC based DFIG-WECS is tested in simulation to regulate both grid and rotor side converters under normal and voltage dip conditions. Furthermore, a new optimization technique known as grey wolf optimization (GWO) is also designed to regulate the battery power for DFIG driven wind energy system operating in standalone mode. In order to verify the effectiveness of the proposed control schemes, simulation models are designed using Matlab/Simulink. The proposed model for MPPT and nonlinear control of grid-connected mode and GWO based power control of standalone DFIG-WECS has been successfully implemented in the real-time environment using DSP controller board DS1104 for a laboratory 480 VA DFIG. The comparison among different controllers suggests that each control technique has its own specialty in wind power control application with specific merits and shortcomings. However, the PI controller provides fast convergence, the ANFIS based NFC controller has better adaptability under grid disturbances and ABNC has moderate performance. Overall, the thesis provides a detailed overview of different robust control techniques for DFIG driven WECS in grid-connected and standalone operation mode with practical implementation