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Zeinab Echreshavi Mokhtar Shasadeghi Mohammad Hassan Asemani 《Journal of The Franklin Institute》2021,358(1):575-605
This paper focuses on a systematic constrained fuzzy integral sliding mode controller design for a class of uncertain discrete-time nonlinear systems which can be represented as Takagi-Sugeno (T-S) fuzzy models. The contributions are to consider constraints on the control input amplitude and control input amplitude rate and to extend the existing pole-placement design technique for designing gain matrices of the fuzzy sliding surface. Moreover, a dynamic-gain observer along with performance is proposed for attenuating disturbance, which generalizes the existing results on the Proportional Observer (PO), the Proportional Integral Observer (PIO) and the dynamic observer (DO). Finally, the dynamic-observer-based constrained fuzzy integral sliding mode controller is designed. All the proposed design conditions are represented in terms of LMIs-based ones. The methods are studied for not only single-input single-output (SISO) but also multi-input multi-output (MIMO) systems. In the end, the proposed approaches are evaluated on practical and numerical systems to illustrate the superiority of the proposed control scheme. 相似文献
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Jing Zhao Zhixiong Zhong Chih-Min Lin Hak-Keung Lam 《Journal of The Franklin Institute》2021,358(1):650-673
This paper studies the 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 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 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 control scheme. 相似文献
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《Journal of The Franklin Institute》2019,356(10):5222-5239
This paper deals with the feedback Stackelberg strategies for the discrete-time mean-field stochastic systems in infinite horizon. The optimal control problem of the follower is first studied. Employing the discrete-time linear quadratic (LQ) mean-field stochastic optimal control theory, the sufficient conditions for the solvability of the optimization of the follower are presented and the optimal control is obtained based on the stabilizing solutions of two coupled generalized algebraic Riccati equations (GAREs). Then, the optimization of the leader is transformed into a constrained optimal control problem. Applying the Karush-Kuhn-Tucker (KKT) conditions, the necessary conditions for the existence and uniqueness of the Stackelberg strategies are derived and the Stackelberg strategies are expressed as linear feedback forms involving the state and its mean based on the solutions of a set of cross-coupled stochastic algebraic equations (CSAEs). An iterative algorithm is put forward to calculate efficiently the solutions of the CSAEs. Finally, an example is solved to show the effectiveness of the proposed algorithm. 相似文献
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