Search

In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controller within a data-driven context. To achieve this, we translate model-based conditions into data series of available input and output signals. This translation process leverages recent advances in data-driven control theory, enabling the controller to operate effectively without relying on explicit system models. The proposed framework incorporates a robust methodology for managing system constraints, ensuring that the control actions remain within predefined bounds. By means of time sequences, the controller learns the underlying system dynamics and adapts to changes in real time, providing enhanced performance and reliability. The integration of set-theoretic methods allows for the systematic handling of uncertainties and disturbances, which are common when the trajectory of a nonlinear system is embedded inside a linear trajectory state tube. To validate the effectiveness of our DDMPC framework, we conduct extensive simulations on a nonlinear DC motor system. The results demonstrate significant improvements in control performance, highlighting the robustness and adaptability of our approach compared to traditional model-based MPC techniques. [ABSTRACT FROM AUTHOR]

A novel closed-loop model-based predictive control (MPC) strategy for input-saturated polytopic linear parameter varying (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 online and used for feedback while a bound on its rate of change is known and exploited for predictions. The contribution of this note is to extend the MPC scheme presented in [5] 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 the robust MPC scheme presented in [7] for uncertain polytopic systems. Feasibility and closed-loop stability of this strategy are proved and a numerical example is also presented 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. Index Terms--Linear matrix inequalities (LMIs), linear parameter varying (LPV) systems, model predictive control (MPC), scheduling controller.

In [4] an algorithm is presented for analytic phase margin control design. Without special care, however, the compensator computed with this algorithm may not be a real rational function. The problem is evident when the plant has real unstable poles. In this case the algorithm in [4] requires a mapping of real points into complex values, and it is not clear that the resulting compensator has real coefficients. The purpose of this paper is to show how a complex mapping required in this algorithm can always be selected so that the compensator does have real coefficients. Index Terms: Analytic positive, interpolation, phase margin optimization, strict Schur.

Summary: In this paper, a sensor stuck fault‐tolerant control framework for linear time‐invariant plant models subject to input/state constraints and bounded disturbances is presented. A receding horizon control reconfigurable scheme is proposed to contrast undesired effects due to sensors malfunctioning. The main merit of this strategy relies on its intrinsic capability to quickly identify fault occurrences and to take a decision on the adequate control action. This is formally obtained by jointly exploiting set‐theoretic polyhedral ideas and the certainty equivalence concept. A numerical example is provided and the control performance contrasted with a well‐reputed competitor fault‐tolerant control scheme. [ABSTRACT FROM AUTHOR]