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 a parallel elite genetic algorithm (PEGA) and its application to global path planning for autonomous mobile robots navigating in structured environments. This PEGA, consisting of two parallel EGAs along with a migration operator, takes advantages of maintaining better population diversity, inhibiting premature convergence, and keeping parallelism in comparison with conventional GAs. This initial feasible path generated from the PEGA planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Both global path planner and smoother are implemented in one field-programmable gate array chip utilizing the system-on-a-programmable-chip technology and the pipelined hardware implementation scheme, thus significantly expediting computation speed. Simulations and experimental results are conducted to show the merit of the proposed PEGA path planner and smoother for global path planning of autonomous mobile robots.

Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

This paper presents an adaptive control using radial-basis-function neural networks (RBFNNs) for a two-wheeled self-balancing scooter. A mechatronic system structure of the scooter driven by two dc motors is briefly described, and its mathematical modeling incorporating two frictions between the wheels and the motion surface is derived. By decomposing the overall system into two subsystems (yaw motion and mobile inverted pendulum), one proposes two adaptive controllers using RBFNN to achieve self-balancing and yaw control. The performance and merit of the proposed adaptive controllers are exemplified by conducting several simulations and experiments on a two-wheeled self-balancing scooter.

Adaptive Neural Network Control of a Self-balancing Two-wheeled Scooter

This paper develops a novel system hardware structure and systematic digital signal processing algorithms for self-localization of an autonomous mobile robot by fusing dead-reckoning and ultrasonic measurements. The multisensorial dead-reckoning (DR) subsystem is established based on the optimal filtering by first fusing heading readings from a magnetic compass, a rate-gyroscope and two encoders mounted on the robot wheels, thereby computing the dead-reckoned location estimate. The novel ultrasonic localization subsystem consists of one ultrasonic transmitter and one radio-frequency (RF) controlled switch mounted on the known location fixed to an inertial frame of reference, four ultrasonic receivers and one RF controlled switch installed on the mobile robot. Four ultrasonic Time-of-Flight (TOF) measurements together with the dead-reckoned location information are fused to update vehicle's position by utilizing the extended Kalman filtering (EKF) algorithm. The proposed algorithms are implemented by using a host PC 586 computer and standard C++ programming techniques. A built system prototype together with its experimental results is used to confirm that the system not only retains its strengths of high accuracy and magnetic interferencing immunity, but also provides a simple and practical structure of use and installation calibration.

A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements

Generalized predictive control using recurrent fuzzy neural networks for industrial processes

An adaptive predictive control with recurrent neural network prediction for industrial processes is presented. The neural predictive control law with integral action is derived based on the minimization of a modified predictive performance criterion. The stability and steady-state performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative nonlinear systems with time-delay. Experimental results for temperature control of a variable-frequency oil-cooling process show the efficacy of the proposed method for industrial processes with set-points changes and load disturbances.

Adaptive Predictive Control With Recurrent Neural Network for Industrial Processes: An Application to Temperature Control of a Variable-Frequency Oil-Cooling Machine

Ching-Chih Tsai

Papers

Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

Adaptive Neural Network Control of a Self-balancing Two-wheeled Scooter

A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements

Generalized predictive control using recurrent fuzzy neural networks for industrial processes

Adaptive Predictive Control With Recurrent Neural Network for Industrial Processes: An Application to Temperature Control of a Variable-Frequency Oil-Cooling Machine