ADAPTIVE SUBOPTIMAL CONTROL OF INPUT CONSTRAINED PLANTS

Abstract. This paper deals with adaptive regulation of a discrete-time linear time-invariant plant witharbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptivecontrol algorithm exploits the one-step-ahead control strategy and the gradient projection type estimationprocedure using the modified dead zone. The convergence property of the estimation algorithm is shown tobe ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously thesuboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented tosupport the theoretical results

ADAPTIVE SUBOPTIMAL CONTROL OF INPUT CONSTRAINED PLANTS

Abstract. This paper deals with adaptive regulation of a discrete-time linear time-invariant plant witharbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptivecontrol algorithm exploits the one-step-ahead control strategy and the gradient projection type estimationprocedure using the modified dead zone. The convergence property of the estimation algorithm is shown tobe ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously thesuboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented tosupport the theoretical results

ADAPTIVE SUBOPTIMAL CONTROL OF INPUT CONSTRAINED PLANTS

This paper deals with adaptive regulation of a discrete-time linear time-invariant plant with arbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptive control algorithm exploits the one-step-ahead control strategy and the gradient projection type estimation procedure using the modified dead zone. The convergence property of the estimation algorithm is shown to be ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously the suboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented to support the theoretical result

ADAPTIVE SUBOPTIMAL CONTROL OF INPUT CONSTRAINED PLANTS

This paper deals with adaptive regulation of a discrete-time linear time-invariant plant with arbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptive control algorithm exploits the one-step-ahead control strategy and the gradient projection type estimation procedure using the modified dead zone. The convergence property of the estimation algorithm is shown to be ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously the suboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented to support the theoretical result

Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints

We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results.Comment: 16 pages, 2 figures, submitted to IEEE Control Systems Letter