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access icon free Adaptive tracking control for a class of stochastic non-linear systems with input saturation constraint using multi-dimensional Taylor network

  • Author(s): Yu-Qun Han 1, 2
    • View affiliations
  • Source: Volume 14, Issue 9, 11 June 2020, p. 1193 – 1199
    DOI:  10.1049/iet-cta.2019.0934 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 09/08/2019, Accepted 05/03/2020, Revised 31/01/2020, Published 06/03/2020

The randomness and input saturation increase computational complexity and impede the tracking performance of stochastic non-linear systems, therefore, it is necessary to build a simple but effective controller. Based on this, a multi-dimensional Taylor network (MTN) is first applied to a class of stochastic non-linear systems with input saturation constraint in this study. Aiming to solve the tracking control problem, a novel MTN-based controller is proposed via backstepping, and the proposed control scheme has some advantages such as simple structure, good real-time performance and easy realisation. With the help of the hyperbolic tangent function approximating the symmetric saturation non-linearity, an adaptive MTN tracking control scheme is constructively designed via backstepping technique. The simulation results are given to illustrate the validity and accuracy of the model of the proposed approach.

Inspec keywords: nonlinear control systems; adaptive control; control system synthesis; function approximation; closed loop systems; tracking; linear systems; control nonlinearities

Other keywords: simple but effective controller; tracking performance; multidimensional Taylor network; novel MTN-based controller; tracking control problem; symmetric saturation nonlinearity; input saturation constraint; nonlinear systems; adaptive tracking control; input saturation increase computational complexity; good real-time performance; adaptive MTN tracking control scheme

Subjects: Linear control systems; Stability in control theory; Control system analysis and synthesis methods; Nonlinear control systems; Interpolation and function approximation (numerical analysis); Self-adjusting control systems

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