A novel model-based predictive control (MPC) strategy for input-saturated polytopic LPV discrete-time systems is proposed. It is postulated that the plant belongs to a polytopic family of linear systems, each member of which being parameterized by the value that a parameter vector assumes in the unit simplex. Such a parameter can be measured on-line and exploited for feedback while a bound on its rate of change is known and exploited for predictions. The contribution of this paper is to extend a previous MPC scheme for the restricting case of $1$-step long control horizons to the general case of control horizons of arbitrary length $N$. This is done by suitably modifying a robust MPC scheme for uncertain polytopic systems. Feasibility and closed-loop stability of this strategy are proved and a numerical example is also presented with comparisons with both the above MPC schemes in order to show how the freedom of extending the control horizon and the knowledge of the parameter is significant in order to improve the performance of the control strategy.
A feedback min-max MPC algorithm for LPV systems subject to bounded rates of change of parameters
2002
Abstract
A novel model-based predictive control (MPC) strategy for input-saturated polytopic LPV discrete-time systems is proposed. It is postulated that the plant belongs to a polytopic family of linear systems, each member of which being parameterized by the value that a parameter vector assumes in the unit simplex. Such a parameter can be measured on-line and exploited for feedback while a bound on its rate of change is known and exploited for predictions. The contribution of this paper is to extend a previous MPC scheme for the restricting case of $1$-step long control horizons to the general case of control horizons of arbitrary length $N$. This is done by suitably modifying a robust MPC scheme for uncertain polytopic systems. Feasibility and closed-loop stability of this strategy are proved and a numerical example is also presented with comparisons with both the above MPC schemes in order to show how the freedom of extending the control horizon and the knowledge of the parameter is significant in order to improve the performance of the control strategy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
