摘要
In this paper, adaptive neural control is proposed for a class of multi-input multi-output （MIMO） nonlinear unknown state time-varying delay systems in block-triangular control structure. Radial basis function （RBF） neural net- works （NNs） are utilized to estimate the unknown continuous functions. The unknown time-varying delays are compensated for using integral-type Lyapunov-Krasovskii functionals in the design. The main advantage of our result not only efficiently avoids the controller singularity, but also relaxes the restriction on unknown virtual control coefficients. Boundedness of all the signals in the closed-loop of MIMO nonlinear systems is achieved, while The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The feasibility is investigated by two simulation examples.
In this paper, adaptive neural control is proposed for a class of multi-input multi-output （MIMO） nonlinear unknown state time-varying delay systems in block-triangular control structure. Radial basis function （RBF） neural net- works （NNs） are utilized to estimate the unknown continuous functions. The unknown time-varying delays are compensated for using integral-type Lyapunov-Krasovskii functionals in the design. The main advantage of our result not only efficiently avoids the controller singularity, but also relaxes the restriction on unknown virtual control coefficients. Boundedness of all the signals in the closed-loop of MIMO nonlinear systems is achieved, while The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The feasibility is investigated by two simulation examples.
基金
supported by the National Natural Science Foundation of China(Nos.60864001,61074124)
