LQR-Mapped Fuzzy Controller Applied to Attitude Stabilization of a Power-Aided-Unicycle

Abstract. Analysis of attitude stabilization of a power-aided unicycle points out that a unicycle behaves like an inverted pendulum subject to power constraint. An LQR-mapped fuzzy controller is introduced to solve this nonlinear issue by mapping LQR control reversely through least square and Sugeno-type fuzzy inference. The fuzzy rule surface after mapping remains optimal

A Simultaneous Perturbation Stochastic Approximation (SPSA)-Based Model Approximation and its Application for Power System Stabilizers

Abstract: This paper presents an intelligent model; named as free model, approach for a closedloop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure

Robust multi-objective design of suspension systems

This thesis presents a robust multi-objective optimal design of four-degree-of-freedom passive and semi-active suspension systems. The passive suspension system is used in a racing car and the semi-active suspension is implemented on a passenger car. Mathematical models of the commercial and racing vehicle suspension systems are used in the computer simulations. A robust multi-objective design of the suspension systems is carried out by considering the minimization of three objectives: passenger’s head acceleration (HA), suspension deflection (SD), and tire deflection (TD). The first objective is concerned with the passenger’s health and comfort. The suspension stroke is described by SD and the tire holding is characterized by TD. The optimal design of the passive suspension involves tuning the coefficients of the sprung spring and damper, tire stiffness, and inertance of the inerter. Suspension systems’ parametric variations are very common and cannot be avoided in practice. To this end, a robust multi-objective optimization method that takes into consideration small changes in the design parameters should be considered. Unlike traditional multi-objective optimization problems where the focus is placed on finding the global Pareto-optimal solutions which express the optimal trade-offs among design objectives, the robust multi-objective optimization algorithms are concerned with robust solutions that are less sensitive to perturbations of decision variables. As a result, the mean effective values of the fitness functions are used as design objectives. Constraints on the design parameters and goals are applied. Numerical simulations show that the robust multi-objective design (RMOD) is very effective and guarantees a robust behavior as compared to that of the classical multi-objective design (MOD). The results also show that the robust region is inside the feasible search space and avoids all of its boundaries. The decision parameter space of the semi-active suspension includes both passive and active components. The passive components include the stiffness of the sprung spring, damping coefficient of the shock absorber, and stiffness of the tire. The active elements are the design details of the LQR algorithm. During the design, global sensitivity analysis is conducted to determine the elements of the suspension system that have high impact on the design objectives. The mass of the passenger’s head and upper body, the mass of the passenger’s lower body and cushion, passenger and cushion’s elastic properties, and the sprung mass of the vehicle are selected for the sensitivity analysis. Results show that the design goals are more sensitive to the variations in the sprung mass than the other parameters. As a result, parametric variations in the sprung mass of the vehicle and passive elements of the suspension system are considered. Similar to the design of the passive suspension, the mean effective values of SD, TD, and HA are used as design objectives. Also, constraints are applied on the objectives in compliance with the requirements of ISO 2631-1 on the design of car suspension systems. The optimization problem is solved by the NSGA-II (non-dominated sorting genetic algorithm) and robust Pareto front and set are obtained

Trajectory Planning and Subject-Specific Control of a Stroke Rehabilitation Robot using Deep Reinforcement Learning

There are approximately 13 million annual new stroke cases worldwide. Research has shown that robotics can provide practical and efficient solutions for expediting post-stroke patient recovery. Assistive robots provide automatic limb training, which saves a great deal of time and energy. In addition, they facilitate the use of data acquisition devices. The data is beneficial in terms of quantitative evaluation of the patient progress. This research focused on the trajectory planning and subject-specific control of an upper-extremity post-stroke rehabilitation robot. To find the optimal rehabilitation practice, the manipulation trajectory was designed by an optimization-based planner. A linear quadratic regulator (LQR) controller was then applied to stabilize the trajectory. The integrated planner-controller framework was tested in simulation. To validate the simulation results, hardware implementation was conducted, which provided good agreement with simulation. One of the challenges of rehabilitation robotics is the choice of the low-level controller. To find the best candidate for our specific setup, five controllers were evaluated in simulation for circular trajectory tracking. In particular, we compared the performance of LQR, sliding mode control (SMC), and nonlinear model predictive control (NMPC) to conventional proportional integral derivative (PID) and computed-torque PID controllers. The real-time assessment of the mentioned controllers was done by implementing them on the physical hardware for point stabilization and circular trajectory tracking scenarios. Our comparative study confirmed the need for advanced low-level controllers for better performance. Due to complex online optimization of the NMPC and the incorporated delay in the method of implementation, performance degradation was observed with NMPC compared to other advanced controllers. The evaluation showed that SMC and LQR were the two best candidates for the robot. To remove the need for extensive manual controller tuning, a deep reinforcement learning (DRL) tuner framework was designed in MATLAB to provide the optimal weights for the controllers; it permitted the online tuning of the weights, which enabled the subject-specific controller weight adjustment. This tuner was tested in simulation by adding a random noise to the input at each iteration, to simulate the subject. Compared to fixed manually tuned weights, the DRL-tuned controller presented lower position-error. In addition, an easy to implement high-level force controller algorithm was designed by incorporating the subject force data. The resulting hybrid position/force controller was tested with a healthy subject in the loop. The controller was able to provide assist as needed when the subject increased the position-error. Future research might consider model reduction methods for expediting the NMPC optimization, application of the DRL on other controllers and for optimization parameter adjustment, testing other high-level controllers like admittance control, and testing the final controllers with post-stroke patients

Electric Vehicle Efficient Power and Propulsion Systems

Vehicle electrification has been identified as one of the main technology trends in this second decade of the 21st century. Nearly 10% of global car sales in 2021 were electric, and this figure would be 50% by 2030 to reduce the oil import dependency and transport emissions in line with countries’ climate goals. This book addresses the efficient power and propulsion systems which cover essential topics for research and development on EVs, HEVs and fuel cell electric vehicles (FCEV), including: Energy storage systems (battery, fuel cell, supercapacitors, and their hybrid systems); Power electronics devices and converters; Electric machine drive control, optimization, and design; Energy system advanced management methods Primarily intended for professionals and advanced students who are working on EV/HEV/FCEV power and propulsion systems, this edited book surveys state of the art novel control/optimization techniques for different components, as well as for vehicle as a whole system. New readers may also find valuable information on the structure and methodologies in such an interdisciplinary field. Contributed by experienced authors from different research laboratory around the world, these 11 chapters provide balanced materials from theorical background to methodologies and practical implementation to deal with various issues of this challenging technology. This reprint encourages researchers working in this field to stay actualized on the latest developments on electric vehicle efficient power and propulsion systems, for road and rail, both manned and unmanned vehicles

A multi-mode attitude determination and control system for small satellites

Thesis (PhD)--Stellenbosch University, 1995.ENGLISH ABSTRACT: New advanced control techniques for attitude determination and control of small (micro) satellites are presented. The attitude sensors and actuators on small satellites are limited in accuracy and performance due to physical limitations, e.g. volume, mass and power. To enhance the application of sophisticated payloads such as high resolution imagers within these confinements, a multi-mode control approach is proposed, whereby various optimized controller functions are utilized during the orbital life of the satellite. To keep the satellite's imager and antennas earth pointing with the minimum amount of control effort, a passive gravity gradient boom, active magnetic torquers and a magnetometer are used. A "cross-product" detumbling controller and a robust Kalman filter angular rate estimator are presented for the preboom deployment phase. A fuzzy controller and magnetometer full state extended Kalman filter are presented for libration damping and Z-spin rate control during inactive imager periods. During imaging, when high performance is required, additional fine resolution earth horizon, sun and star sensors plus 3-axis reaction wheels are employed. Full state attitude, rate and disturbance estimation is obtained from a horizon/sun extended Kalman filter. A quaternion feedback reaction wheel controller is presented to point or track a reference attitude during imaging. A near-minimum time, eigenaxis rotational reaction wheel controller for large angular maneuvers. Optimal linear quadratic and minimum energy algorithms to do momentum dumping using magnetic torquers, are presented. A new recursive magnetometer calibration method is designed to enhance the magnetic in-flight measurements. Finally, a software structure is proposed for the future onboard implementation of the multi-mode attitude control system.AFRIKAANSE OPSOMMING: Nuwe gevorderde beheertegnieke vir die oriëntasiebepaling en -beheer van klein (mikro-) satelliete word behandel. Die oriëntasiesensors en -aktueerders op klein satelliete het 'n beperkte akkuraatheid en werkverrigting as gevolg van fisiese volume, massa en kragleweringbeperkings. Om gesofistikeerde loonvragte soos hoë resolusie kameras binne hierdie tekortkominge te kan hanteer, word 'n multimode beheerbenadering voorgestel. Hiermee kan 'n verskeidenheid van optimale beheerfunksies gedurende die wentelleeftyd van die satelliet gebruik word. Om die satellietkamera en -antennas aardwysend te rig met 'n minimale beheerpoging, word 'n passiewe graviteitsgradiëntstang, aktiewe magneetspoele en 'n magnetometer gebruik. 'n "Kruisproduk" onttuimellings beheerder en 'n robuuste hoektempo Kalmanfilter afskatter is ontwikkel vir die periode voordat die graviteitsgradiëntstang ontplooi word. 'n Wasige beheerder en 'n volledige toestand, uitgebreide Kalmanfilter afskatter is ontwikkel om librasiedemping en Z-rotasietempo beheer te doen gedurende tydperke wanneer die kamera onaktief is. Gedurende kamera-opnames word hoë werkverrigting verlang. Fyn resolusie aardhorison, son en stersensors met 3-as reaksiewiele kan dan gebruik word. 'n Volledige oriëntasie, hoektempo en steurdraaimoment Kalmanfilter afskatter wat inligting van bogenoemde sensors gebruik, is ontwikkel. 'n “Quaternion” reaksiewiel terugvoerbeheerder waarmee die satelliet na verwysings oriëntasiehoeke gerig kan word of waarmee oriëntasiehoektempos gevolg kan word, word behandel. 'n Naby minimumtyd, "eigen"-as reaksiewielbeheerder vir groothoek rotasies is ontwikkel. Optimale algoritmes om momentumontlading van reaksiewiele met lineêre kwadratiese en minimumenergie metodes te doen, word afgelei en aangebied. 'n Nuwe rekursiewe kalibrasietegniek waarmee 'n magnetometer outomaties gedurende vlug ingestel kan word, is ontwikkel. Ten slotte, word 'n programstruktuur voorgestel vir aanboord implementering van die nuwe multimode beheerstelsel

Nonlinear optimal control of interior permanent magnet synchronous motors for electric vehicles

At present time, research in the field of Electric Vehicles (EV) is significantly intensifying around the world due to the ambitious goals of many countries, including the UK, to prohibit the sale of new gasoline and diesel vehicles, as well as hybrid vehicles, in the near future around 2030-35. The primary goal of this Ph.D. research is to improve the propulsion system of electric vehicles' powertrains through improvements in the control of Interior Permanent Magnet Synchronous Motors (IPMSM), which are commonly used in EV applications. The proposed approaches are supported by simulations in Matlab, Matlab-Simulink and laboratory-based experiments. The research initially proposes an analytical solution in implicit view for a combined Maximum Torque per Ampere (MTPA) and Maximum Efficiency (ME) control, allowing to determine the optimal d-axis current, based on the concept of minimisation of the fictitious electric power loss. With the exception of two parameters, the equation is identical to that of the ME control. Therefore, upgrading the ME control to the combined MTPA/ME control is relatively easy and doesn't require any change in hardware beyond a few minors of controller code in the software. The presented research demonstrates an easy-to-apply combined MTPA/ME control leading to the ‘Transients Optimal and Energy-Efficient IPMSM Drive’ providing smooth transitions to the MTPA control during transients and to the ME control during steady states. A concept of ‘Nonlinear Optimal Control of IPMSM Drives’ is also introduced in this Ph.D. research. The velocity control loop develops nonlinearities when energy consumption optimisation methods like MTPA, ME, or combined MTPA/ME are added. In addition, the control system's parameters can be inaccurate and fluctuate depending on the operating point or possible uncertainties in real-time operation. In the proposed method, the control structure is the same as in the Field Oriented II Control (FOC), with the close velocity and two current loops, but the Proportional-Integral (PI) controllers are replaced by Nonlinear Optimal (NO) Controllers. The linear part of the controller is designed as a Linear Quadratic Regulator (LQR) with integral action for each loop separately. This is, in fact, a PI controller with optimal gain parameters for a specific operating point. The nonlinear part takes the required fluctuations of the control system’s optimal gain parameters in real-time operation as new control actions to improve a robust control structure. The design procedure for the nonlinear part is similar to that of the LQR, but the criterion of A. Krasovsky's generalised work is used, and the analytical derivations lead to an explicit control solution for the nonlinear optimal part. The nonlinear part emulates the adjustments for updating the linear part’s optimal LQR gains based on operating conditions, instead of employing extensive look-up tables or complicated estimation algorithms. The proposed control is robust in the allowed range of the system’s parameters. In conclusion, upgrading existing industrial IPMSM drives into a robust and optimal energy-efficient version that can be used for electric vehicle applications is the main advantage of the novel control concept described in this Ph.D. research. For this upgrade, only a small portion of the software that is related to the PI controllers needs to be changed; no new hardware is needed. Therefore, it is cost-effective and simple to transform existing industrial IPMSM drives into a better version with the proposed method. This feature also leads to the design of more adequate IPMSM drives to meet the demands of Electric Vehicle (EV) operating cycles