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In this paper, the adaptive tracking control approach is framed for a class of switched discrete-time nonlinear systems with unknown parameters. Allowable switching law is constructed and the controller is chosen by switched Lyapunov function schema. Adaptive updating control algorithm for discrete-time nonlinear switched systems are first derived. Moreover, we can prove that the closed-loop switched system is uniformly stable and the output tacking error is guaranteed to converge to a small neighborhood of zero. Lastly, an illustrative example is given to demonstrate the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: Based on the backstepping technique, a direct adaptive neural network control algorithm is proposed for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. Neural networks are utilized to approximate unknown functions, and a stable adaptive neural backstepping controller is synthesized. It is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. Compared with the existing results for discrete-time systems, the proposed algorithm needs only less parameters to be adjusted online, therefore, it can reduce online computation burden. A simulation example is employed to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network tracking control is studied for a class of uncertain nonlinear systems. The studied systems are in discrete-time form and unknown time-delay is considered here. Up to now, the research works on nonlinear discrete-time main focus on systems without time-delay, so the problem of the unknown time-delay will be solved in this paper. Based on the Lipschitz or norm-boundedness assumption of the unknown nonlinearities, the mean-value theorem is utilized to solve the unknown time-delay problem. In order to overcome the noncausal problem, the strict-feedback systems will be transformed into a special form. The radial basis functions neural networks (RBFNN) are utilized to approximate the unknown functions of the systems, the adaptation laws and the controllers are designed based on the transformed systems. By using the Lyapunov analysis, it is proven that the closed-loop system is stable in the sense that semi-globally uniformly ultimately bounded (SGUUB) and the output tracking errors converge to a bounded compact set. A simulation example is used to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an effective adaptive tracking control approach is constructed for the sixth-order induction motor model which is in discrete-time form. The fuzzy logic systems (FLSs) are applied to estimate a control law and used to a block strict feedback form (BSFF) which is in existence of bounded disturbances. Then, a desired optimal control can be achieved. Depend on the Lyapunov analysis approach, for the considered system, the stability is guaranteed, the tracking error can be ensured to a small neighborhood around zero, and the weight estimates can be insured to semi-global uniformly ultimately bounded (SGUUB). An advantage for the controlled system is that the proposed algorithm needs only less parameters which is different from the existing results, therefore, it can reduce the calculation amount. The practicality of the scheme is verified via a simulation. [ABSTRACT FROM AUTHOR]