This paper presents a model predictive control (MPC) based reference governor approach for control of constrained linear systems. A nominal closed-loop system is first designed to guarantee that, in the unconstrained case, asymptotic zero-error regulation for (piecewise) constant reference signals is achieved. Then, a couple of exogenous signals are added to the reference signal and to the control variable and their value is determined by formulating a MPC problem in order to guarantee that (i) when the state and control constraints are not active, the nominal closed-loop system is recovered, (ii) in transient conditions the constraints are always satisfied and the difference of the performances between the real and the nominal closed-loop systems is minimised, and (iii) when the reference signal is infeasible, the output is brought to the nearest feasible value. A simulation example is reported to witness the potentialities of the approach

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A partial shading condition can adversely affect the energy conversion efficiency of domestic photovoltaic (PV) systems. Connecting each PV module to a microinverter and performing module-level maximum power point tracking (MPPT) are proposed as promising solutions. In this paper, a feedback linearization-based control strategy is designed for the nonlinear system by a novel straightforward approach. The obtained nonlinear control law can independently govern each microinverter, providing module-level MPPT for PV modules without DC optimizer. Moreover, PV modules can be easily connected or disconnected due to the lug-and-play ability of the proposed controller. As a result, the proposed PV system can be easily maintained and extended even by non-expert users. Moreover, any module failure in the proposed PV system can be tolerated without impacts on the normal operation of other PV modules. The advantages of the proposed control strategy are verified by the simulation of a test PV system in MATLAB/Simulink under various partial shading conditions as well as adding or removing PV modules.

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This paper proposes a hierarchical decentralized reference governor for constrained cascade systems. The reference governor (RG) approach is reformulated in terms of receding horizon strategy such that a locally receding horizon optimization is obtained for each subsystem with a preestablished prediction horizon. The algorithm guarantees that not only the nominal overall closedloop system without any constraint is recoverable but also the state and control constraints are satisfied in transient conditions. Also, considering unfeasible reference signals, the output of any subsystem goes locally to the nearest feasible value. The proposed dynamic constraint tightening strategy uses a receding horizon to reduce the conservatism of conventional robust RGs. Moreover, a decentralized implementation of the algorithms used to compute tightened constraints and output admissible sets is introduced that allow to deal with large scale systems. Furthermore, a set of dynamic constraints are presented to preserve recursive feasibility of distributed optimization problem. Feasibility, stability, convergence, and robust constraint satisfaction of the proposed algorithm are also demonstrated. The proposed approach is verified by simulating a system composed of three cascade jacketed continuous stirred tank reactors.

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