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In this paper, a robust adaptive neural control design approach is presented for a class of uncertain pure-feedback nonlinear systems. To reduce the complexity of the both controller structure and computation, only one neural network is used to approximate the lumped unknown function of the system at the last step of the recursive design process. By this approach, the complexity growing problem existing in conventional methods can be eliminated completely. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation results demonstrate the effectiveness and merits of the proposed approach. [ABSTRACT FROM AUTHOR]

The problem of decentralised adaptive robust stabilisation is considered for a class of uncertain large-scale interconnected non-linear systems with time-varying delays. It is assumed that the upper bounds of the uncertainties and interconnection terms are unknown, and that the time-varying delays are any non-negative continuous and bounded functions, and do not require their derivatives to be less than one. In particular, it is only required that the non-linear interconnection terms, which can also include time-varying delays, are bounded in any non-negative non-linear functions, which are not required to be known for the system designer. For such a class of uncertain large-scale time-delay interconnected non-linear systems, a new method is presented whereby a class of continuous memoryless decentralised local adaptive robust state feedback controllers with a rather simpler structure is proposed. It is also shown that the solutions of uncertain large-scale time-delay interconnected systems can be guaranteed as uniformly exponentially convergent towards a ball that can be as small as desired. Finally, a numerical example is given to demonstrate the validity of the results. [ABSTRACT FROM AUTHOR]

Abstract: It is well-known that robustness refers to the ability dealing with unknown uncertainties or unmodeled dynamics of the model-based control theory. However, this kind of robustness no longer has any significance for model free or data-driven control theory because the controller is designed only using I/O data of the controlled plant. In this paper, a novel robustness of the model free control or data-driven control is presented when the system is controlled by the model free adaptive control (MFAC) scheme. It is assumed that an MFAC scheme is implemented via a network control system and that data dropout occurs due to a failing sensor, actuator or network failure, resulting in what it is called intermittent MFAC. The stability of such a MFAC scheme is analyzed by the statistical approach. It is shown that the MFAC system is still stable when data dropout occurs, and the data dropout impacts the convergence speed. [Copyright &y& Elsevier]

Abstract: In this paper, adaptive control is studied for a class of single-input–single-output (SISO) nonlinear discrete-time systems in strict-feedback form with nonparametric nonlinear uncertainties of the Lipschitz type. To eliminate the effect of the nonparametric uncertainties in an unmatched manner, a novel future states prediction is designed using states information at previous steps to compensate for the effect of uncertainties at the current step. Utilizing the predicted future states, constructive adaptive control is developed to compensate for the effects of both parametric and nonparametric uncertainties such that global stability and asymptotical output tracking is achieved. The effectiveness of the proposed control law is demonstrated in the simulation. [Copyright &y& Elsevier]

In this paper, we study the limitations of the capability of the feedback mechanism in dealing with structural uncertainty for a class of pth order non-linear autoregressive control systems. The size of the structural uncertainty is characterized by multiple Lipschitz constants (say L1,L2,...,Lp) of the uncertain non-linear function. It is shown that if L1,L2,...,Lp satisfy certain algebraic conditions, then there exists no globally stabilizing feedback control law for the corresponding uncertain systems. The result here is an extension of the previous result obtained by Zhang and Guo (2002). [ABSTRACT FROM AUTHOR]