Output Feedback Receding Horizon Control Research Articles

Output feedback receding horizon control for takagi‐sugeno systems with Non‐PDC law

AbstractThe issue of constrained output feedback receding horizon control (RHC) for Takagi‐Sugeno systems is studied in this paper. The control strategy is developed through parameterizing the infinite horizon control moves as a non‐parallel distributed compensation (non‐PDC) law. The parameter‐dependent non‐PDC controller is optimized in a receding horizon manner. The extended non‐quadratic Lyapunov function serves to guarantee the robust stability of the augmented closed‐loop system, which is an improvement over existing results with common‐quadratic Lyapunov functions. The recursive feasibility of the proposed approach is also carefully analyzed. The approach is shown to enhance the control performance at a cost of computational efficiency. A numerical example is demonstrate the effectiveness of the approach.

Output feedback receding horizon control of constrained systems

This paper considers output feedback control of linear discrete-time systems with convex state and input constraints which are subject to bounded state disturbances and output measurement errors. We show that the non-convex problem of finding a constraint admissible affine output feedback policy over a finite horizon, to be used in conjunction with a fixed linear state observer, can be converted to an equivalent convex problem. When used in the design of a time-varying robust receding horizon control law, we derive conditions under which the resulting closed-loop system is guaranteed to satisfy the system constraints for all time, given an initial state estimate and bound on the state estimation error. When the state estimation error bound matches the minimal robust positively invariant (mRPI) set for the system error dynamics, we show that this control law is time-invariant, but its calculation generally requires solution of an infinite-dimensional optimization problem. Finally, using an invariant outer approximation to the mRPI error set, we develop a time-invariant control law that can be computed by solving a finite-dimensional tractable optimization problem at each time step that guarantees that the closed-loop system satisfies the constraints for all time.

Nonlinear sampled-data output feedback receding horizon control

In this paper, we present an output feedback receding horizon control for a class of SISO nonlinear systems. A globally stabilizing state feedback receding horizon control scheme is combined with a discretized high gain observer. This is motivated by the fact that measurable system’s outputs are only available at specific sampling intervals. Our result follows from the application of a separation principle applicable to a class of sampled-data nonlinear systems. It is shown that the output-feedback scheme recovers the performance (rate of convergence) achieved under state feedback receding horizon control for a sufficiently large observer gain and sampling frequency.

Stabilizing static output feedback receding horizon controls for linear discrete time-invariant systems

In this paper, a stabilizing static output feedback receding horizon control (RHC) is proposed for linear discrete time-invariant systems. An inequality condition on the terminal weighting matrices is presented under which the closed-loop stability of static output feedback receding horizon controls is guaranteed. It is shown that the stabilizing static output feedback receding horizon control problem can be represented as a non-linear minimization problem based on linear matrix inequalities (LMIs). An algorithm for solving the presented non-linear minimization problem is proposed. In addition, this result is extended to an arbitrary reference tracking problem that is one of the main features of receding horizon control. Finally, the efficiency of the proposed algorithm is illustrated by numerical examples.

A stabilizing static output feedback receding horizon control for linear discrete time-invariant systems

In this paper, a stabilizing static output feedback receding horizon control (RHC) is proposed for linear discrete time-invariant systems. Firstly, an inequality condition on the terminal weighting matrix is presented under which the closed-loop stability of static output feedback receding horizon controls is guaranteed. Then, it is shown that the stabilizing static output feedback receding horizon control problem can be represented as a nonlinear minimization problem based on linear matrix inequalities (LMl's). An algorithm for solving the nonlinear minimization problem is proposed. Finally, applications of the proposed controller are illustrated through numerical examples.