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

Abstract This paper presents an adaptive backstepping control method for trajectory tracking and stabilization of an omnidirectional wheeled mobile robot with parameter variations and uncertainties caused by friction and slip. The dynamic model of the robot with three independent driving omnidirectional wheels equally spaced at 120 degrees from one another is briefly introduced. With the dynamic model, the adaptive controller to achieve both trajectory tracking and stabilization is synthesized via adaptive backstepping approach. Experimental results are conducted to show the merit of the proposed control method.

Adaptive Trajectory Tracking and Stabilization for Omnidirectional Mobile Robot with Dynamic Effect

This paper addresses the decentralized formation stabilization issues using relative distance measurements for a class of nonholonomic multi-robot formation systems (NMRFS) under the assumption of each robot having its own stable trajectory tracking controller. This class of NMRFS, composed by n nonholonomic wheeled mobile robots (WMRs), is regarded as an abstracted system which mathematically describes the interconnect topology among the robots. Based on some critical stability results, the stabilization of the nonholonomic NMRFS are theoretically derived using differential geometry and its asymptotical formation stability is achieved. In addition, the design issue in the NMRFS with respect to the interconnection and formation stability is distinctly defined so that the changing formation parameters or interconnected structures on-line become practicable in the NMRFS. The effectiveness of the proposed methods is exemplified by conducting one simulation.

Formation stabilization of nonholonomic multi-robot systems using relative distance measurements

This paper develops methodology and technique for brightness improvement of color display systems using white sub-pixels. Based on human vision discrimination, three novel white sub-pixel filter structures without loss of original resolution are presented to enhance brightness of conventional stripe, delta and pentile color filter architectures. A new mapping algorithm fusing RGB sub-pixel data around a white sub-pixel is proposed to improve overall image quality. Numerous simulation results are provided to show the feasibility and efficacy of the proposed method for improving conventional stripe, delta and pentile display systems. An experimental result is described which has been conducted to show that the proposed color display system performs well for electronic consumer products, such as notebooks, personal digital assistants, and mobile phone, etc.

Brightness improvement of color display systems using white sub-pixel structure and mapping algorithm

We report the effects of gold nanoparticles (GNP) on the enhancement of the dose effect and double-strand break (DSB) induction for X-ray monoenergies (10 keV–2 MeV). The overall relative biological effectiveness (RBE) was defined as the product of dose and DSB enhancement. The cellular doses and energy spectra were computed using the PENELOPE (PENetration and Energy LOss of Positrons and Electrons) code. The DNA damage yields were estimated using the MCDS (Monte Carlo damage simulation) code. Considering the production of Auger electrons induced by 7mg/ml GNP, we found that dose enhancement at the 30 keV was ~4.0 for cells with radius $4~\mu \text{m}$ . The DSB induction increased as GNP concentrations increased and was dependent on X-ray energy. These findings demonstrated that the maximum RBE was attained at 30–40 keV and therefore this energy range would be more efficient in cancer cell killing.

/pdf/a-computational-method-to-estimate-the-effect-of-gold-5a0j2w1iwl.pdf

A Computational Method to Estimate the Effect of Gold Nanoparticles on X-Ray Induced Dose Enhancement and Double-Strand Break Yields

This paper presents a recurrent neural network based predictive control for a class of nonlinear discrete time systems The neural predictive control law is developed from the minimization of a generalized predictive performance criterion A real time adaptive control algorithm, including a neural predictor and a neural predictive controller, is proposed; the adaptive learning rates for both the neural predictor and controller are determined based on Lyapunov stability theory Simulation results reveal that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative nonlinear systems Experimental results for a variable frequency oil-cooling control process are performed which have shown effectiveness of the proposed method under the conditions of setpoint and load changes

Ching-Chih Tsai

Papers

Adaptive Trajectory Tracking and Stabilization for Omnidirectional Mobile Robot with Dynamic Effect

Formation stabilization of nonholonomic multi-robot systems using relative distance measurements

Brightness improvement of color display systems using white sub-pixel structure and mapping algorithm

A Computational Method to Estimate the Effect of Gold Nanoparticles on X-Ray Induced Dose Enhancement and Double-Strand Break Yields

Design and experimental evaluation of an adaptive predictive controller using recurrent neural network