Model-free periodic adaptive control for a class of SISO nonlinear discrete-time systems

Key Words:Discrete time control systems;Adaptive control systems - Controllers - Digital control systems - Dynamical systems;Adaptive Control - Asymptotic tracking - Controller designs - Discrete - time systems - Higher-order learning - Linearization methods - Nonlinear discrete-time systems - Simulation example

Abstract:Using the known periodicity of the given trajectory, we develop a new dynamical linearization method by introducing a concept of PPD, then present a new model-free periodic adaptive control approach (MFPAC) and its extension of higher-order learning control algorithm for a class of general nonlinear and non-affine discrete-time systems. It is model-free in nature and the controller design and analysis only depends on the I/O data of the dynamical system. The proposed MFPAC updates the PPD estimate values and the control signals periodically in a pointwise manner using the I/O data obtained at the corresponding points in previous periods, in the sequel achieves an asymptotic tracking convergence. A simulation example illustrates the feasibility and effectiveness of the proposed method. &copy; 2010 IEEE.<br/>

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