In this paper, an adaptive intelligent control scheme is presented to investigate the problem of adaptive tracking control for a class of nonstrict-feedback nonlinear systems with constrained states and unmodeled dynamics. By approximating the unknown nonlinear uncertainties, utilizing Barrier Lyapunov functions (BLFs), and designing a dynamic signal to deal with the constrained states and the unmodeled dynamics, respectively, an adaptive neural network (NN) controller is developed in the frame of the backstepping design. In order to simplify the design process, the nonstrict-feedback form is treated by using the special properties of Gaussian functions. The proposed adaptive control scheme ensures that all variables involved in the closed-loop system are bounded, the corresponding state constraints are not violated. Meanwhile, the tracking error converges to a small neighborhood of the origin. In the end, the proposed intelligent design algorithm is applied to one-link manipulator to demonstrate the effectiveness of the obtained method.

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Partial-State-Constrained Adaptive Intelligent Tracking Control of Nonlinear Nonstrict-Feedback Systems with Unmodeled Dynamics and Its Application

An Indirect Adaptive Control Scheme for MIMO Systems

In this short paper we consider the adaptive control of multivariable discrete time minimum phase systems. The system under consideration is not supposed to be decouplable by static state feedback. The adaptive control design presented here is based on pole zero placement. The a priori knowledge of the plant transfer matrix Hermite form is not necessary, this knowledge is replaced by that of the largest infinite zero order of the plant transfer matrix.

J.M. Dion

Papers

An Indirect Adaptive Control Scheme for MIMO Systems

An indirect adaptive control scheme for mimo systems