Robust Integral of Neural Network and Error Sign Control of MIMO Nonlinear Systems
This paper presents a novel state-feedback control scheme for the tracking control of a class of multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback multiplied with an adaptive gain is introduced. The NN in the control law learns the system dynamics in an online manner, while the NN residual reconstruction errors and the bounded disturbances are overcome by the error sign signal. Since both of the NN output and the error sign signal are included in the integral, the continuity of the control input is ensured. The controller structure and the NN weight update law are novel in contrast with the previous effort, and the semiglobal asymptotic tracking performance is still guaranteed by using the Lyapunov analysis. In addition, the NN weights and all other signals are proved to be bounded simultaneously. The proposed approach also relaxes the need for the upper bounds of certain terms, which are usually required in the previous designs. Finally, the theoretical results are substantiated with simulations.
Q. Yang et al., "Robust Integral of Neural Network and Error Sign Control of MIMO Nonlinear Systems," IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 12, pp. 3278-3286, Institute of Electrical and Electronics Engineers (IEEE), Dec 2015.
The definitive version is available at https://doi.org/10.1109/TNNLS.2015.2470175
Electrical and Computer Engineering
Keywords and Phrases
Continuous time systems; Control theory; Errors; Feedback; Feedback control; Nonlinear feedback; Nonlinear systems; Robust control; State feedback; Asymptotic tracking; Bounded disturbances; Continuous time nonlinear systems; Controller structures; MIMO nonlinear systems; Multi-input multi-output; Neural networks (NNs); Reconstruction error; Adaptive control systems; Algorithm; Artificial neural network; Computer simulation; Feedback system; Human; Nonlinear system; Algorithms; Computer Simulation; Feedback; Humans; Neural Networks (Computer); Nonlinear Dynamics; Asymptotic stability; Lyapunov method; Nonlinear unknown systems
International Standard Serial Number (ISSN)
Article - Journal
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Dec 2015