Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA

On Increasing the Automation Level of Heavy-Duty Hydraulic Manipulators with Condition Monitoring of the Hydraulic System and Energy-Optimised Redundancy Resolution

Hydraulic manipulators on mobile machines are predominantly used for excavation and lifting applications at construction sites and for heavy-duty material handling in the forest industry due to their superior power-density and rugged nature. These manipulators are conventionally open-loop controlled by human operators who are sufficiently skilled to operate the machines. However, in the footsteps of pioneering original equipment manufacturers (OEMs) and to keep up with the intensifying demand for innovation, more and more mobile machine OEMs have a major interest in significantly increasing the automation level of their hydraulic manipulators and improving the operation of manipulators. In this thesis, robotic software-based functionalities in the form of modelbased condition monitoring and energy-optimal redundancy resolution which facilitate increased automation level of hydraulic manipulators are proposed.A condition monitoring system generally consists of software modules and sensors which co-operate harmonically and monitor the hydraulic system’s health in real-time based on an indirect measure of this system’s health. The premise is that when this condition monitoring system recognises that the system’s health has deteriorated past a given threshold (in other words, when a minor fault is detected, such as a slowly increasing internal leakage of the hydraulic cylinder), the condition monitoring module issues an alarm to warn the system operator of the malfunction, and the module could ideally diagnose the fault cause. In addition, when faced with severe faults, such as an external leakage or an abruptly increasing internal leakage in the hydraulic system, an alarm from the condition monitoring system ensures that the machine is quickly halted to prevent any further damage to the machine or its surroundings.The basic requirement in the design of such a condition monitoring system is to make sure that this system is robust and fault-sensitive. These properties are difficult to achieve in complex mobile hydraulic systems on hydraulic manipulators due to the modelling uncertainties affecting these systems. The modelling uncertainties affecting mobile hydraulic systems are specific compared with many other types of systems and are large because of the hydraulic system complexities, nonlinearities, discontinuities and inherently time-varying parameters. A feasible solution to this modelling uncertainty problem would be to either attenuate the effect of modelling errors on the performance of model-based condition monitoring or to develop improved non-model-based methods with increased fault-sensitivity. In this research work, the former model-based approach is taken. Adaptation of the model residual thresholds based on system operating points and reliable, load-independent system models are proposed as integral parts of the condition monitoring solution to the modelling uncertainty problem. These proposed solutions make the realisation of condition monitoring solutions more difficult on heavy-duty hydraulic manipulators compared with fixed-load manipulators, for example. These solutions are covered in detail in a subset of the research publications appended to this thesis.There is wide-spread interest from hydraulic manipulator OEMs in increasing the automation level of their hydraulic manipulators. Most often, this interest is related to semi-automation of repetitive work cycles to improve work productivity and operator workload circumstances. This robotic semi-automated approach involves resolving the kinematic redundancy of hydraulic manipulators to obtain motion references for the joint controller to enable desirable closed-loop controlled motions. Because conventional redundancy resolutions are usually sub-optimal at the hydraulic system level, a hydraulic energy-optimised, global redundancy resolution is proposed in this thesis for the first time. Kinematic redundancy is resolved energy optimally from the standpoint of the hydraulic system along a prescribed path for a typical 3-degrees-of-freedom (3-DOF) and 4-DOF hydraulic manipulator. Joint motions are also constrained based on the actuators’ position, velocity and acceleration bounds in hydraulic manipulators in the proposed solution. This kinematic redundancy resolution topic is discussed in the last two research papers. Overall, both designed manipulator features, condition monitoring and energy-optimised redundancy resolution, are believed to be essential for increasing the automation of hydraulic manipulators

High performance disturbance observer based control system design for permanent magnet synchronous AC machine applications

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAn electrical machine is one of the main workforces in different industries and serves them in various applications. Machine drive control design involves many technical issues for efficient and robust exploitation. Over several decades, Permanent Magnet Synchronous Motor (PMSM) is getting preferred for industrial applications over its counterpart Squirrel Cage Induction Motor (SCIM) drive, because of their higher efficiency, power density, and higher torque to inertia ratio. In the prospective that PMSM drives are considered the drives of the future, there are still technical challenges and issues related to PMSM control. Many studies have been devoted to PMSM control in the past, but there are still some open research areas that bring worldwide researchers’ interests back to PMSM drive control. One of the approaches that may facilitate better performance, higher efficiency, and robust and reliable work of the control system is the disturbance observer-based control (DOBC) with linear and nonlinear output feedback control for PM synchronous machine applications. DOBC is adopted due to its ability to reject external and internal disturbances with improving tracking performance in the variable speed wind energy conversion system (WECS) to maximize power extraction. The high order disturbance observer (HODO) is utilized to estimate the aerodynamic torque-based wind speed without the use of a traditional anemometer, which reduces the overall cost and improves the reliability of the whole system. Also, this method has been designed to improve the angular shaft speed tracking of the PMSM system under load torque disturbance and speed variations. The model-based linear and nonlinear feedback control are used in the proposed control systems. The sliding mode control (SMC) with switching output feedback control law and integral SMC with linear feedback and state-dependent Riccati equation (SDRE) based approaches have been designed for the systems. The SDRE control accounts for the nonlinear multivariable structure of the WECS and is approximated with Taylor series expansion terms. The chattering inherited from SMC is eliminated by the continuous approximation technique. The sliding mode is guaranteed by eliminating the reaching mode in the proposed integral SMC. The model-free cascaded linear feedback control system based on the proportional-integral (PI) controllers use a back-calculation algorithm anti-windup scheme. The proposed speed controllers are synthesized with HODO to compensate for the external disturbance, model uncertainty, noise, and modelling errors. Moreover, servomechanism-based SDRE control, a near-optimal control system is designed to suppress the model uncertainty and noise without the use of disturbance observers. The proposed control systems for PMSM speed regulation have demonstrated a significant improvement in the angular shaft speed-tracking performance at the transients. Their performances have been tested under speed, load torque variations, and model uncertainty. For example, HODO-based SMC with switching output feedback control law (SOFCL) has demonstrated improvement by more than 78% than the PI-PI control system of the PMSM. The performance of the HODOs-based Integral SMC with SDRE nonlinear feedback is improved by 80.5% under external disturbance, model uncertainty, and noise than Integral SMC with linear feedback in the WECS. The HODO-based SDRE control with servomechanism has shown an 80.2% improvement of mean absolute percentage error under disturbances than Integral SMC with linear feedback in the WECS. The PMSM speed tracking performance of the proposed HODO-based discrete-time PI-PI control system with back-calculation algorithm anti-windup scheme is improved by 87.29% and 90.2% in the speed commands and load torque disturbance variations scenarios respectively. The simulations for testing the proposed control system of the PMSM system and WECS have been implemented in Matlab/Simulink environment. The PMSM speed control experimental results have been obtained with Lucas-Nuelle DSP-based rapid control prototyping kit.Center for International Program “Bolashak” of the Ministry of Education and Science Republic of Kazakhsta

Data-based mechanistic modelling, forecasting, and control.

This article briefly reviews the main aspects of the generic data based mechanistic (DBM) approach to modeling stochastic dynamic systems and shown how it is being applied to the analysis, forecasting, and control of environmental and agricultural systems. The advantages of this inductive approach to modeling lie in its wide range of applicability. It can be used to model linear, nonstationary, and nonlinear stochastic systems, and its exploitation of recursive estimation means that the modeling results are useful for both online and offline applications. To demonstrate the practical utility of the various methodological tools that underpin the DBM approach, the article also outlines several typical, practical examples in the area of environmental and agricultural systems analysis, where DBM models have formed the basis for simulation model reduction, control system design, and forecastin

Performance Analysis of Flexible A.C. Transmission System Devices for Stability Improvement of Power System

When large power systems are interconnected by relatively weak tie line, low-frequency oscillations are observed. Recent developments in power electronics have led to the development of the Flexible AC Transmission Systems (FACTS) devices in power systems. FACTS devices are capable of controlling the network condition in a very fast manner and this feature of FACTS can be exploited to improve the stability of a power system. To damp electromechanical oscillations in the power system, the supplementary controller can be applied with FACTS devices to increase the system damping. The supplementary controller is called damping controller. The damping controllers are designed to produce an electrical torque in phase with the speed deviation. The objective of this thesis is to develop some novel control techniques for the FACTS based damping controller design to enhance power system stability. Proper selection of optimization techniques plays an important role in for the stability enhancement of power system. In the present thesis Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Gravitational search algorithm (GSA) along with their hybrid form have been applied and compared for a FACTS based damping controller design. Important conclusions have been drawn on the suitability of optimization technique. The areas of research achieved in this thesis have been divided into two parts: The aim of the first part is to develop the linearized model (Philip-Hefron model) of a single machine infinite bus power system installed with FACTS devices, such as Static Synchronous Series Compensator (SSSC) and Unified Power Flow Controller (UPFC). Different Damping controller structures have been used and compared to mitigate the system damping by adding a component of additional damping torque proportional to speed change through the excitation system. The various soft-computing techniques have been applied in order to find the controller parameters. The recently developed Gravitational Search Algorithm (GSA) based SSSC damping controller, and a new hybrid Genetic Algorithm and Gravitational Search Algorithm (hGA-GSA) based UPFC damping controller seems to the most effective damping controller to mitigate the system oscillation. The aim of second part is to develop the Simulink based model (to over-come the problem associated with the linearized model) for an SMIB as well as the multi-machine power system. Coordinated design of PSS with various FACTS devices based damping controllers are carried out considering appropriate time delays due to sensor time constant and signal transmission delays in the design process. A hybrid Particle Swarm Optimization and Gravitational Search Algorithm (hPSO-GSA) technique is employed to optimally and coordinately tune the PSS and SSSC based controller parameters and has emerged as the most superior method of coordinated controller design considered for both single machine infinite bus power system as well as a multi-machine power system. Finally, the damping capabilities of SSSC based damping controllers are thoroughly investigated by considering a new derived modified signal known as Modified Local Input Signal which comprises both the local signal (speed deviation) and remote signal (line active power). Appropriate time delays due to sensor time constant and signal transmission delays are considered in the design process. The hybrid Particle Swarm Optimization and Gravitational Search Algorithm (hPSO-GSA) technique is used to tune the damping controller parameters. It is observed that the new modified local input signal based SSSC controller provides the best system performance compared to other alternatives considered for a single machine infinite bus power system and multi-machine power system

Machine Learning assisted Digital Twin for event identification in electrical power system

The challenges of stable operation in the electrical power system are increasing with the infrastructure shifting of the power grid from the centralized energy supply with fossil fuels towards sustainable energy generation. The predominantly RES plants, due to the non-linear electronic switch, have brought harmonic oscillations into the power grid. These changes lead to difficulties in stable operation, reduction of outages and management of variations in electric power systems. The emergence of the Digital Twin in the power system brings the opportunity to overcome these challenges. Digital Twin is a digital information model that accurately represents the state of every asset in a physical system. It can be used not only to monitor the operation states with actionable insights of physical components to drive optimized operation but also to generate abundant data by simulation according to the guidance on design limits of physical systems. The work addresses the topic of the origin of the Digital Twin concept and how it can be utilized in the optimization of power grid operation.Die Herausforderungen für den zuverfässigen Betrieb des elektrischen Energiesystems werden mit der Umwandlung der Infrastruktur in Stromnetz von der zentralen Energieversorgung mit fossilen Brennstoffen hin zu der regenerativen Energieeinspeisung stetig zugenommen. Der Ausbau der erneuerbaren Energien im Zuge der klimapolitischen Zielsetzung zur CO²-Reduzierung und des Ausstiegs aus der Kernenergie wird in Deutschland zügig vorangetrieben. Aufgrund der nichtlinearen elektronischen Schaltanlagen werden die aus EE-Anlagen hervorgegangenen Oberschwingungen in das Stromnetz eingebracht, was nicht nur die Komplexität des Stromnetzes erhöht, sondern auch die Stabilität des Systems beeinflusst. Diese Entwicklungen erschweren den stabilen Betrieb, die Verringerung der Ausfälle und das Management der Netzschwankungen im elektrischen Energiesystem. Das Auftauchen von Digital Twin bringt die Gelegenheit zur Behebung dieser Herausforderung. Digital Twin ist ein digitales Informationsmodell, das den Zustand des physikalischen genau abbildet. Es kann nicht nur zur Überwachung der Betriebszustände mit nachvollziehbarem Einsichten über physischen Komponenten sondern auch zur Generierung der Daten durch Simulationen unter der Berücksichtigung der Auslegungsgrenze verwendet werden. Diesbezüglich widmet sich die Arbeit zunächste der Fragestellung, woher das Digital Twin Konzept stammt und wie das Digitan Twin für die Optimierung des Stromnetzes eingesetzt wird. Hierfür werden die Perspektiven über die dynamische Zustandsschätzung, die Überwachung des des Betriebszustands, die Erkennung der Anomalien usw. im Stromnetz mit Digital Twin spezifiziert. Dementsprechend wird die Umsetzung dieser Applikationen auf dem Lebenszyklus-Management basiert. Im Rahmen des Lebenszyklusschemas von Digital Twin sind drei wesentliche Verfahren von der Modellierung des Digital Twins zur deren Applizierung erforderlich: Parametrierungsprozess für die Modellierung des Digital Twins, Datengenerierung mit Digital Twin Simulation und Anwendung mit Machine Learning Algorithmus für die Erkennung der Anomalie. Die Validierung der Zuverlässigkeit der Parametrierung für Digital Twin und der Eventserkennung erfolgt mittels numerischer Fallstudien. Dazu werden die Algorithmen für Online und Offline zur Parametrierung des Digital Twins untersucht. Im Rahmen dieser Arbeit wird das auf CIGRÉ basierende Referenznetz zur Abbildung des Digital Twin hinsichtlich der Referenzmessdaten parametriert. So sind neben der Synchronmaschine und Umrichter basierende Einspeisung sowie Erreger und Turbine auch regler von Umrichter für den Parametrierungsprozess berücksichtigt. Nach der Validierung des Digital Twins werden die zahlreichen Simulationen zur Datengenerierung durchgeführt. Jedes Event wird mittels der Daten vo Digital Twin mit einem "Fingerprint" erfasst. Das Training des Machine Learning Algorithmus wird dazu mit den simulierten Daten von Digital Twin abgewickelt. Das Erkennungsergebnis wird durch die Fallstudien validiert und bewertet

Control of fluctuating renewable energy sources: energy quality & energy filters

This doctoral study discusses how to control fluctuating renewable energy sources at converter, unit, and system layers to deliver smoothed power output to the grid. This is particularly relevant to renewable power generation since the output power of many kinds of renewable energy sources have huge fluctuations (e.g. solar, wind and wave) that needs to be properly treated for grid integration. In this research, the energy quality is developed to describe the friendliness and compatibility of power flows/waveforms to the grid, by contrast with the well-known concept of power quality which is used to assess the voltage and current waveforms. In Chapter 1 & 2, a background introduction and a literature review of studied subjects are presented, respectively. In Chapter 3, the problem of determining the PI parameters in dq decoupling control of voltage source converter (VSC) is studied based on a state-space model. The problems of the conventional method when there is insufficient interface resistance are addressed. New methods are proposed to overcome these drawbacks. In Chapter 4 & 5, energy quality and the energy filters (EFs) are proposed as tools to assess and manage power fluctuations of renewable energy sources. The proposed EFs are energy storage control systems that could be implemented on a variety of energy storage hardware. EFs behave like low-pass filters to the power flows. Finally, in Chapter 6, as an application example of renewable power plant with energy filter control and smoothed power output, a master-slave wave farm system is proposed. The wave farm system uses enlarged rotor inertia of electric machines as self-energy storage devices

Seismic response control of structures using novel adaptive passive and semi-active variable stiffness and negative stiffness devices

Current seismic design practice promotes inelastic response in order to reduce the design forces. By allowing the structure to yield while increasing the ductility of the structure, the global forces can be kept within the limited bounds dictated by the yield strength. However, during severe earthquakes, the structures undergo significant inelastic deformations leading to stiffness and strength degradation, increased interstory drifts, and damage with residual drift. The research presented in this thesis has three components that seek to address these challenges. To prevent the inelastic effects observed in yielding systems, a new concept “apparent weakening” is proposed and verified through shake table studies in this thesis. “Apparent weakening” is introduced in the structural system using a complementary “adaptive negative stiffness device” (NSD) that mimics "yielding” of the global system thus attracting it away from the main structural system. Unlike the concept of weakening and damping, where the main structural system strength is reduced, the new system does not alter the original structural system, but produces effects compatible with an early yielding. Response reduction using NSD is achieved in a two step sequence. First the NSD, which is capable of exhibiting nonlinear elastic stiffness, is developed based on the properties of the structure. This NSD is added to the structure resulting in reduction of the stiffness of the structure and NSD assembly or “apparent weakening”-thereby resulting in the reduction of the base shear of the assembly. Then a passive damper, designed for the assembly to reduce the displacements that are caused due to the “apparent weakening”, is added to the structure-thereby reducing the base shear, acceleration and displacement in a two step process. The primary focus of this thesis is to analyze and experimentally verify the response reduction attributes of NSD in (a) elastic structural systems (b) yielding systems and (3) multistory structures. Experimental studies on 1:3 scale three-story frame structure have confirmed that consistent reductions in displacements, accelerations and base shear can be achieved in an elastic structure and bilinear inelastic structure by adding the NSD and viscous fluid damper. It has also been demonstrated that the stiffening in NSD will prevent the structure from collapsing. Analogous to the inelastic design, the acceleration and base shear and deformation of the structure and NSD assembly can be reduced by more than 20% for moderate ground motions and the collapse of structure can be prevented for severe ground motions. Simulation studies have been carried on an inelastic multistoried shear building to demonstrate the effectiveness of placing NSDs and dampers at multiple locations along the height of the building; referred to as “distributed isolation”. The results reported in this study have demonstrated that by placing a NSD in a particular story the superstructure above that story can be isolated from the effects of ground motion. Since the NSDs in the bottom floors will undergo large deformations, a generalized scheme to incorporate NSDs with different force deformation behavior in each storey is proposed. The properties of NSD are varied to minimize the localized inter-story deformation and distribute it evenly along the height of the building. Additionally, two semi-active approaches have also been proposed to improve the performance of NSD in yielding structures and also adapt to varying structure properties in real time. The second component of this thesis deals with development of a novel device to control the response of structural system using adaptive length pendulum smart tuned mass damper (ALP-STMD). A mechanism to achieve the variable pendulum length is developed using shape memory alloy wire actuator. ALP-STMD acts as a vibration absorber and since the length is tuned to match the instantaneous frequency, using a STFT algorithm, all the vibrations pertaining to the dominant frequency are absorbed. ALP-STMD is capable of absorbing all the energy pertaining to the tuned-frequency of the system; the performance is experimentally verified for forced vibration (stationary and non-stationary) and free vibration. The third component of this thesis covers the development of an adaptive control algorithm to compensate hysteresis in hysteretic systems. Hysteretic system with variable stiffness hysteresis is represented as a quasi-linear parameter varying (LPV) system and a gain scheduled controller is designed for the quasi-LPV system using linear matrix inequalities approach. Designed controller is scheduled based on two parameters: linear time-varying stiffness (slow varying parameter) and the stiffness of friction hysteresis (fast varying parameter). The effectiveness of the proposed controller is demonstrated through numerical studies by comparing the proposed controller with fixed robust H∞ controller. Superior tracking performance of the LPV-GS over the robust H∞ controller in different displacement ranges and various stiffness switching cases is clearly evident from the results presented in this thesis. The LPV-GS controller is capable of adapting to the parameter changes and is effective over the entire range of parameter variations

Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process

Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup