Asymptotic Tracking Controller Design for Nonlinear Systems with Guaranteed Performance
In this paper, a novel adaptive control strategy is presented for the tracking control of a class of multi-input-multioutput uncertain nonlinear systems with external disturbances to place user-defined time-varying constraints on the system state. Our contribution includes a step forward beyond the usual stabilization result to show that the states of the plant converge asymptotically, as well as remain within user-defined time-varying bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system from the original one, whose asymptotic stability guarantees both the satisfaction of the time-varying restrictions and the asymptotic tracking performance of the original system. The uncertainties of the transformed system are overcome by an online neural network (NN) approximator, while the external disturbances and NN reconstruction error are compensated by the robust integral of the sign of the error signal. Via standard Lyapunov method, asymptotic tracking performance is theoretically guaranteed, and all the closed-loop signals are bounded. The requirement for a prior knowledge of bounds of uncertain terms is relaxed. Finally, simulation results demonstrate the merits of the proposed controller.
B. Fan et al., "Asymptotic Tracking Controller Design for Nonlinear Systems with Guaranteed Performance," IEEE Transactions on Cybernetics, vol. 48, no. 7, pp. 2001-2011, Institute of Electrical and Electronics Engineers (IEEE), Jul 2018.
The definitive version is available at https://doi.org/10.1109/TCYB.2017.2726039
Electrical and Computer Engineering
Intelligent Systems Center
Keywords and Phrases
Asymptotic stability; Controllers; Errors; Lyapunov methods; Mathematical transformations; Neural networks (NNs); Nonlinear systems; Robustness (control systems); Time varying systems; Adaptive control strategy; Equivalent non-linear systems; Multi-input multi-output; On-line neural networks; Time varying; Uncertain nonlinear systems; Uncertainty; Adaptive control systems; Time-varying constraints
International Standard Serial Number (ISSN)
Article - Journal
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jul 2018