A new approach to SISO nonlinear system asymptotic optimal tracking

What is it about?

This paper deals with the problem of asymptotic tracking and dynamic regulation of SISO nonlinear systems via output feedbacks by the discrete multi-dimensional Taylor network (MTN) controller, a novel controller with fixed structure and sampled-data control mechanism. For verification of its validity, differential geometry and polynomial approximation are adopted. Using the emulation technique and regional pole assignment, the asymptotic tracking and dynamic regulation without online optimization of the system by discrete MTN controller is tested. With the dynamic change of error signals, the dynamic regulation by given index is realized. As a convex optimization problem, the controller parameters can be acquired by parametric learning. Based on the delta operator model, the procedure of the controller design is given in detail. Simulation results confirm the feasibility and effectiveness of the proposed approach.

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Why is it important?

The authors give a novel controller with fixed structure and sampled-data control mechanism which can realize dynamic regulation without of online optimization. The system output can be improved by the high-power product of controller input.

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