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Abstract: In this paper, we study the cooperative global adaptive output regulation problem for a class of nonlinear uncertain multi-agent systems subject to an uncertain leader system. In comparison with the case where the leader system is known exactly, we need to combine adaptive control and robust control techniques to deal with the unknown parameters in the leader system. By employing a distributed internal model for the multi-agent system, we first convert the problem into a global adaptive robust stabilization problem of a distributed augmented system. Then we further solve the global adaptive stabilization problem of the augmented system by a distributed adaptive control law. Finally, we show that the unknown parameters in the leader system can be exactly estimated provided that the distributed internal model is of minimal order. [Copyright &y& Elsevier]

Abstract: In this study, an adaptive robust tracking control scheme for a nanomechanical beam is proposed. The mathematical nanobeam model takes into consideration the presence of parametric uncertainties, and external disturbances, and fringing field effects and intermolecular forces, are also incorporated. The proposed backstepping controller is enhanced with an adaptive dynamic surface technique and an control scheme. The overall effectiveness of the designed controller is checked and illustrated with the presented results. These results show that the desired transient output tracking performance is achieved, and that the closed loop system exhibits good robustness to system uncertainties. Particular attention has been paid, so as to identify any relevance between the variations in the controller''s design parameter and the system''s output response and performance. [Copyright &y& Elsevier]

In this article on-line parameter estimation of dynamical systems is addressed in the context of model-based networked control systems (MB-NCSs). Stability conditions that are robust to parameter uncertainties and lack of feedback for extended intervals of time are presented. The updated model is used to control the real system the next time feedback information is unavailable. Additionally, new estimation models are proposed that offer better convergence properties than typical state-space parameter estimation methods; common assumptions such as availability of persistently exciting inputs and estimation of only a canonical form of the system are relaxed. The implementation of upgraded models in MB-NCSs results in better usage of the network by allowing longer intervals without the need for measurement updates. [ABSTRACT FROM AUTHOR]

Due to simple construction with convex effect, the synchronous reluctance motor (SynRM) drive system has highly nonlinear uncertainties. The accurate mathematic models are difficult to be established for time-varying and nonlinear uncertainties of the actual SynRM drive system. In this paper, the robust backstepping control system using an adaptive modified Elman neural network (ENN) uncertainty observer for a SynRM drive system is proposed to achieve the better performance and robustness. Firstly, the field-oriented mechanism is applied to formulate the dynamic equation of the SynRM drive system. Secondly, the backstepping approach is proposed to control the motion of SynRM drive system. With proposed integral backstepping control system, the rotor position of the SynRM drive possesses the advantages of good control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the SynRM drive system for nonlinear uncertainties, an adaptive modified ENN uncertainty observer is proposed to estimate the required lumped uncertainty. The on-line adaptive law of the modified ENN is derived in accordance with Lyapunov function. The updated parameters of the modified ENN are used by the gradient descent method and the backpropagation algorithm. Finally, the effectiveness of the proposed control scheme is verified by some experimental results. [ABSTRACT FROM AUTHOR]

A robust tracking controller for robot manipulators measuring only the angular positions and considering model uncertainties is presented. It is considered that the model is uncertain; that is, the system parameters, nonlinear terms, external perturbations, and the friction effects in each robot joint are considered unknown. The controller is composed by two parts, a linearizing-like control feedback and a high-gain estimator. The main idea is to lump the uncertain terms into a new state which represents the dynamics of the uncertainties. This new state is then estimated in order to be compensated. In this way the resulting controller is robust. A numerical example for a RR robot manipulator is provided, in order to corroborate the results. [ABSTRACT FROM AUTHOR]

In this paper, robust adaptive boundary control is developed for a class of flexible string-type systems under unknown time-varying disturbance. The dynamics of the string system is represented by a nonhomogeneous hyperbolic partial differential equation (PDE) and two ordinary differential equations. Boundary control is proposed at the right boundary of the string based on the original distributed parameter system model (PDE) to suppress the vibration excited by the external unknown disturbance. Adaptive control is designed to compensate the system parametric uncertainty. With the proposed robust adaptive boundary control, all the signals in the closed-loop system are guaranteed to be uniformly ultimately bounded. The state of the string system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Simulations are provided to illustrate the effectiveness of the proposed control. [ABSTRACT FROM PUBLISHER]