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In this paper, adaptive NN (neural network) tracking control is proposed for ocean surface vessels with parametric uncertainties, unknown disturbances and rotary actuators. Based on the Lyapunov synthesis method and backstepping technique, adaptive NN tracking control is developed by incorporating the actuator configuration matrix and considering actuator saturation constraints. In the proposed adaptive...
In this paper, decentralized controllers are developed to drive a swarm of mobile agents with high-order (n > 2) nonlinear dynamics in strict feedback form into a moving target region while avoiding collisions among themselves. It is important to consider coordination of multiple high-order agent dynamics which generalize the existing simple single-integrator/double-integrator ones because, in...
In this paper, we investigate a novel adaptive design approach for nonlinear systems as an exploration of the new challenging topic on dealing with both parametric and nonparametric internal uncertainties in adaptive control of discrete-time nonlinear systems. The existence of both two kinds of uncertainties makes it very difficult or even impossible to apply the traditional recursive identification...
In this paper, adaptive control is studied for a class of nonlinear discrete-time systems in parameter-strict-feedback form with both parametric and non-parametric uncertainties. The non-parametric uncertainty function is assumed to satisfy the Lipschitz condition. To achieve asymptotical tracking performance, estimation of both uncertainties is constructed. Future states are predicted to overcome...
In this paper, we investigate the control design for a class of strict-feedback nonlinear systems preceded by unknown backlash-like hysteresis. Using the characteristics of backlash-like hysteresis, adaptive dynamic surface control (DSC) is developed without constructing a hysteresis inverse. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Function uncertainties...
In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. The non-affine problem both in the pure-feedback form and in the generalized Prandtl-Ishlinskii hysteresis input function is solved by adopting the Mean Value Theorem. By utilizing Lyapunov synthesis, the closed-loop control...
This paper is concerned with simultaneous stability of a collection of continuous-time linear plants whose feedback control loops are closed via a shared digital communication network. Because of the limitation of communication capacity, only a limited number of controller-plant connections can be accommodated at any time instant. Therefore, it is necessary to carefully design the scheduling policy...
In this paper, adaptive model reference control is investigated for a class of discrete-time multi-input-multi-output (MIMO) systems. Estimation of both unknown system parameters and nonparametric model uncertainty is constructed. Based on the estimation, a novel adaptive control is proposed which completely compensates the nonparametric model uncertainty. The boundedness of the closed-loop signals...
Both state and output feedback adaptive neural network controls are developed for a class of discrete-time single-input single-output (SISO) nonaffine uncertain nonlinear systems. Each controller incorporates a linear dynamic compensator and an adaptive neural network term. The linear dynamic compensator is designed to stabilize the linearized system, and the adaptive neural network term is introduced...
In this paper, robust adaptive neural tracking control is developed for a class of uncertain SISO nonlinear systems in a Brunovsky form with unknown nonlinear dead-zone and unknown control gain & its sign. The design is based on the principle of sliding mode control and the use of Nussbaum-type function in solving the problem of the completely unknown function control gain. A novel description...
In this paper, robust adaptive neural tracking control is developed for a class of uncertain SISO nonlinear systems in a Brunovsky form with unknown nonlinear dead-zone and unknown control gain & its sign. The design is based on the principle of sliding mode control and the use of Nussbaum-type function in solving the problem of the completely unknown function control gain. A novel description...
This paper describes the synthesis of robust output feedback Hinfin control for a class of uncertain Markovian jump linear systems with time-delays which are time-varying and depend on the system modes of operation. Under the assumption of known bounds of system uncertainties and the control system gain variations, we present sufficient conditions on the existence of robust stochastic stability and...
In this paper, robust force/motion control strategies are presented for mobile manipulators under both holonomic and nonholonomic constraints in the presence of uncertainties and disturbances. Robust controls are proposed based on a simplified dynamic model, the defined reference signals and the mixed tracking errors. The proposed control strategies guarantee that the system motion converges to the...
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