Semiglobal ISpS Disturbance Attenuation With Output Tracking via Direct Adaptive Design

Prescribed Performance Output Feedback/Observer-Free Robust Adaptive Control of Uncertain Systems Using Neural Networks

A neural network output feedback/observer-free continuous controller for multiple-input-multiple-output uncertain nonlinear systems is designed, which is capable of guaranteeing prescribed performance bounds on the system output, as well as the boundedness of all other closed-loop signals, despite the presence of additive external disturbances and unmodeled dynamics. The assumptions that were made concern the satisfaction of an unboundedness observability property and an output Lagrange stability condition of the unmodeled dynamics subsystem and that the nominal system is output feedback equivalent to a strictly passive one. Simulations on an induction motor system illustrate the approach.

Adaptive neural control design for nonlinear distributed parameter systems with persistent bounded disturbances

In this paper, an adaptive neural network (NN) control with a guaranteed L(infinity)-gain performance is proposed for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities and persistent bounded disturbances. Initially, Galerkin method is applied to the PDE system to derive a low-order ordinary differential equation (ODE) system that accurately describes the dynamics of the dominant (slow) modes of the PDE system. Subsequently, based on the low-order slow model and the Lyapunov technique, an adaptive modal feedback controller is developed such that the closed-loop slow system is semiglobally input-to-state practically stable (ISpS) with an L(infinity)-gain performance. In the proposed control scheme, a radial basis function (RBF) NN is employed to approximate the unknown term in the derivative of the Lyapunov function due to the unknown system nonlinearities. The outcome of the adaptive L(infinity)-gain control problem is formulated as a linear matrix inequality (LMI) problem. Moreover, by using the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of the L(infinity)-gain, while control constraints are respected. Furthermore, it is shown that the proposed controller can ensure the semiglobal input-to-state practical stability and L(infinity)-gain performance of the closed-loop PDE system. Finally, by applying the developed design method to the temperature profile control of a catalytic rod, the achieved simulation results show the effectiveness of the proposed controller.

Adaptive neural network tracking control with disturbance attenuation for multiple-input nonlinear systems

A switching adaptive neural network controller for multiple-input nonlinear, affine in the control dynamical systems with unknown nonlinearities is designed, capable of arbitrarily attenuating L(2) or L(infinity) external disturbances. In the absence of disturbances, a uniform ultimate boundedness property of the tracking error with respect to an arbitrarily small set around the origin is guaranteed, as well as the uniform boundedness of all the signals in the closed loop. The proposed switching adaptive controller effectively avoids possible division by zero, while guaranteeing the continuity of switching. In this way, problems connected to existence of solutions and chattering phenomena are alleviated. Simulations illustrate the approach.