Finite-time asynchronous state estimation for jump systems with partial transition probabilities via redundant channels: A co-design method

This paper addresses the $H_{\infty }$ finite-time asynchronous state estimation issue for Markov jump systems with partial transition probabilities. A hidden-Markov-chain-based redundant channel model (HMCb-RCM) is established to reflect a more practical situation. Based on the output of the HMCb-RCM, firstly an asynchronous full-order state estimator is devised for the jump system with partial transition probabilities. Then, new sufficient criteria are derived such that the state estimation error is $H_{\infty }$ stochastically finite-time bounded. The relationship between the partial transition probabilities and asynchronous modes is revealed as few attempts. The conditional transition probability matrix (CTPM) for the HMCb-RCM is not fixed but designable accordingly; a co-design strategy is newly developed to synthesize the CTPM and the state estimator simultaneously, which produces less conservatism than that with fixed CTPM. Finally, the theoretical results are applied to a one-link robotic manipulator to validate the proposed results.     

H∞ tracking control for nonlinear multivariable systems using wavelet-type TSK fuzzy brain emotional learning with particle swarm optimization

This paper studies the ${\mathit{H}}_{\infty }$ tracking control for uncertain nonlinear multivariable systems. We propose a control strategy, which combines the adaptive wavelet-type Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning controller (WTFBELC) and the $H_{\infty }$ robust tracking compensator. As for the adaptive WTFBELC, it is a main controller designed to mimic the ideal controller. The proposed WTFBELC is to obtain much better ability of handling nonlinearities and uncertainties, but the proposed $H_{\infty }$ robust tracking compensator is to compensate the residual error between the adaptive WTFBELC and the ideal controller. Furthermore, the optimal learning rates of the adaptive WTFBELC are searched quickly by using the particle swarm optimization (PSO) algorithm, and the parameter updated laws are derived based on the steepest descent gradient method. The robust tracking performance of this novel control scheme is guaranteed based on Lyapunov stability theory. The mass-spring-damper mechanical system and the three-link robot manipulator, are used to verify the effectiveness of the proposed adaptive PSO-WTFBELC $H_{\infty }$ control scheme.