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 techniques for design and embedded implementation of a kinematics controller for a Mecanum wheeled omnidirectional robot (MWOR). The kinematic controller is designed based on feedback linearization method, and then shown asymptotically stable via the Lyapunov stability theory. The proposed motion control law has been implemented into an FPGA development board using System-on-a-programmable-chip (SoPC). Simulations and experimental results are conducted to illustrate the effectiveness and performance of the proposed controller for Mecanum wheeled omnidirectional robots.

Motion controller design and embedded realization for Mecanum wheeled omnidirectional robots

This paper presents an adaptive nonlinear control using radial-basis-function neural network (RBFNN) for an electric unicycle. A mechatronic system structure of the unicycle is constructed and its simplified mathematical modeling is then established by using Newtonian mechanics and incorporating the frictions between the wheel and the terrain surface. An adaptive nonlinear control together with RBFNN is developed based on adaptive backstepping technique, in order to simultaneously achieve self-balancing and forward motion. Simulation results are conducted to illustrate feasibility and effectiveness of the proposed control method. The performance and merit of the proposed method are well exemplified by real riding test.

Adaptive nonlinear control using RBFNN for an electric unicycle

In this paper, a new PID fuzzy scheme is proposed to control the temperature of a plastic extrusion barrel. This fuzzy controller is based on the proportional-integral derivative fuzzy control scheme with if-then rules and triangular membership functions. For a traditional PID controller, the output is a linear combination of the error of the system, the derivative of the error, and the integral of the error. On the other hand, a conventional fuzzy controller has a nonlinear function of the error of the system and the derivative of the error as output. Since a nonlinear controller can usually control a nonlinear system more efficiently, thus fuzzy controllers often provide better performances than PID controllers in terms of shorter rise time and smaller overshoot. However, the traditional fuzzy controller cannot improve its steady-state performance for time-varying systems. To avoid this drawback, the PID fuzzy controller is developed. The design of this controller is briefly described. Then the experiment results are demonstrated. It is shown that the PID fuzzy control algorithm can meet the requirement of this temperature control project.

Temperature control of a plastic extrusion barrel using PID fuzzy controllers

In order to achieve good handling (or flying) qualities in longitudinal axis for all flight conditions, an adaptive backstepping flight control law is presented. With the backstepping technique, an adaptive controller is synthesized for the purpose of accomplishing desired responses under a wide range of flight envelope. The specification assignments for flying qualities are used for selecting essential parameters of the proposed adaptive controller for all flight conditions. In comparison with other conventional control methods, the proposed adaptive control law is much simpler and easier to construct and implement. Simulation results show that the proposed method is capable of giving desired closed-loop dynamic performance and robustness against uncertainties within the subsonic and supersonic flight conditions

Longitudinal axis flight control law design by adaptive backstepping

In this paper, a robust tracking controller is proposed for the trajectory tracking problem of a dual-arm wheeled mobile manipulator subject to some modeling uncertainties and external disturbances. Based on backstepping techniques, the design procedure is divided into two levels. In the kinematic level, the auxiliary velocity commands for each subsystem are first presented. A sliding-mode equivalent controller, composed of neural network control, robust scheme and proportional control, is constructed in the dynamic level to deal with the dynamic effect. To deal with inadequate modeling and parameter uncertainties, the neural network controller is used to mimic the sliding-mode equivalent control law; the robust controller is designed to compensate for the approximation error and to incorporate the system dynamics into the sliding manifold. The proportional controller is added to improve the system's transient performance, which may be degraded by the neural network's random initialization. All the parameter adjustment rules for the proposed controller are derived from the Lyapunov stability theory and e-modification such that uniform ultimate boundedness (UUB) can be assured. A comparative simulation study with different controllers is included to illustrate the effectiveness of the proposed method.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1934-6093.2007.tb00392.x

Ching-Chih Tsai

Papers

Motion controller design and embedded realization for Mecanum wheeled omnidirectional robots

Adaptive nonlinear control using RBFNN for an electric unicycle

Temperature control of a plastic extrusion barrel using PID fuzzy controllers

Longitudinal axis flight control law design by adaptive backstepping

Robust tracking control for a wheeled mobile manipulator with dual arms using hybrid sliding-mode neural network