By a switched system, we mean a hybrid dynamical system consisting of a family of continuous-time subsystems and a rule that orchestrates the switching between them. The article surveys developments in three basic problems regarding stability and design of switched systems. These problems are: stability for arbitrary switching sequences, stability for certain useful classes of switching sequences, and construction of stabilizing switching sequences. We also provide motivation for studying these problems by discussing how they arise in connection with various questions of interest in control theory and applications.

Basic problems in stability and design of switched systems

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

System Identification I

Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.

https://hal-centralesupelec.archives-ouvertes.fr/hal-01337007/document

FPGAs in Industrial Control Applications

This paper presents an adaptive polar-space motion controller for trajectory tracking and stabilization of a three-wheeled, embedded omnidirectional mobile robot with parameter variations and uncertainties caused by friction, slip and payloads. With the derived dynamic model in polar coordinates, an adaptive motion controller is synthesized via the adaptive backstepping approach. This proposed polar-space robust adaptive motion controller was implemented into an embedded processor using a field-programmable gate array (FPGA) chip. Furthermore, the embedded adaptive motion controller works with a reusable user IP (Intellectual Property) core library and an embedded real-time operating system (RTOS) in the same chip to steer the mobile robot to track the desired trajectory by using hardware/software co-design technique and SoPC (system-on-a-programmable-chip) technology. Simulation results are conducted to show the merit of the proposed polar-space control method in comparison with a conventional proportional-integral (PI) feedback controller and a non-adaptive polar-space kinematic controller. Finally, the effectiveness and performance of the proposed embedded adaptive motion controller are exemplified by conducting several experiments on steering an embedded omnidirectional mobile robot.

Adaptive Polar-Space Motion Control for Embedded Omnidirectional Mobile Robots with Parameter Variations and Uncertainties

This paper presents a Lyapunov-based controller with integral action which improves the trajectory tracking capability of a small-scale helicopter. An approximate helicopter model with decoupled dynamics is adopted for the controller design. The controller is completely derived based on the backstepping technique. Three numerical simulations are conducted to illustrate the tracking performance of the proposed control method. The simulation results using the full helicopter model with coupled dynamics show that the proposed controller is capable of giving better tracking performance while comparing with the robust control method in [R. Mahony & T. Hamel, International Journal of Robust and Nonlinear Control, vol. 14, no. 12, pp.1035-1059 (2004)].

Improvement in Trajectory Tracking Control of a Small Scale Helicopter via Backstepping

Mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine

This paper develops methodologies and techniques for the design, analysis and implementation of a model reference adaptive predictive temperature controller for a variable frequency oil-cooling machine, suited for cooling high-speed machine tools. The oil-cooling process is modeled experimentally as a first-order system model with a time-delay, and its system parameters are identified using the recursive least-square method. Based on this model, a model-reference adaptive predictive controller is proposed for achieving set-point tracking and disturbance rejection. A real-time model-reference adaptive predictive control algorithm is then presented and implemented utilizing a standalone digital signal processor TMS320F243 from Texas Instruments. The experimental results show that the proposed control method is proven capable of giving satisfactory performance under set-point changes, fixed loads and load changes.

Model reference adaptive predictive control of a variable-frequency oil-cooling machine

This paper develops a methodology for trajectory tracking control of a nonholonomic wheeled mobile manipulator with uncertainties and external load changes. The proposed control law consists of two levels: kinematics and dynamic levels. First, the auxiliary kinematic velocity control laws for the mobile platform and the onboard manipulator are separately proposed via backstepping. Then, a hybrid robust tracking control system is presented to ensure the velocity tracking ability in spite of the uncertainties. To achieve the goal, a neural network controller is developed to mimic an equivalent control law in the sliding-mode control, a robust controller is designed to incorporate the system dynamics into the sliding surface for guaranteeing the asymptotical stability, and the proportional controller is designed to improve the transient performance for randomly initializing neural network. All the adaptive learning algorithms for sliding-mode neural networks (SMNN) are derived from the Lyapunov stability theory so that the system tracking ability can be guaranteed in the close-loop system no matter the uncertainties occur or not. Simulation results illustrate the feasibility as well as efficacy of the proposed control strategy in comparison with the backstepping method.

Ching-Chih Tsai

Papers

Adaptive Polar-Space Motion Control for Embedded Omnidirectional Mobile Robots with Parameter Variations and Uncertainties

Improvement in Trajectory Tracking Control of a Small Scale Helicopter via Backstepping

Mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine

Model reference adaptive predictive control of a variable-frequency oil-cooling machine

Hybrid robust tracking control for a mobile manipulator via sliding-mode neural network