Iterative learning control for discrete-time systems with exponential rate of convergence

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Iterative learning control for discrete-time systems with exponential rate of convergence

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An algorithm for iterative learning control is proposed based on an optimisation principle used by other authors to derive gradient-type algorithms. The new algorithm is a descent algorithm and has potential benefits which include realisation in terms of Riccati feedback and feedforward components. This realisation also has the advantage of implicitly ensuring automatic step-size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm achieves a geometric rate of convergence for invertible plants. One important feature of the proposed algorithm is the dependence of the speed of convergence on weight parameters appearing in the norms of the signals chosen for the optimisation problem.

Inspec keywords: iterative methods; multidimensional systems; optimal control; discrete time systems; matrix algebra; convergence; optimisation; intelligent control; feedforward; feedback

Other keywords: discrete-time systems; optimisation; gradient-type algorithms; optimal control; iterative learning control; reference input tracking; 2D systems; convergence rate; descent method; feedforward; Riccati feedback

Subjects: Interpolation and function approximation (numerical analysis); Discrete control systems; Optimal control; Distributed parameter control systems; Artificial intelligence (theory); Algebra; Optimisation techniques

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