This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.

Model Predictive Control

高等学校計算数学学報 = Numerical mathematics

http://pdclab.seas.ucla.edu/Publications/PDChristofides/PDChristofides_RScattolini_DMunozdelaPena_JLiu_CCE_2013_21_DMPC_A_Tutorial_Review_and_Future_Research_Directions.pdf

Distributed model predictive control: A tutorial review and future research directions

Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then, the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach.

/pdf/a-model-predictive-control-approach-to-microgrid-operation-q6s5cb0thi.pdf

A Model Predictive Control Approach to Microgrid Operation Optimization

A tutorial review of economic model predictive control methods

In this work, we develop model predictive control (MPC) designs, which are capable of optimizing closed-loop performance with respect to general economic considerations for a broad class of nonlinear process systems. Specifically, in the proposed designs, the economic MPC optimizes a cost function, which is related directly to desired economic considerations and is not necessarily dependent on a steady-state—unlike conventional MPC designs. First, we consider nonlinear systems with synchronous measurement sampling and uncertain variables. The proposed economic MPC is designed via Lyapunovbased techniques and has two different operation modes. The first operation mode corresponds to the period in which the cost function should be optimized (e.g., normal production period); and in this operation mode, the MPC maintains the closed-loop system state within a predefined stability region and optimizes the cost function to its maximum extent. The second operation mode corresponds to operation in which the system is driven by the economic MPC to an appropriate steady-state. In this operation mode, suitable Lyapunov-based constraints are incorporated in the economic MPC design to guarantee that the closed-loop system state is always bounded in the predefined stability region and is ultimately bounded in a small region containing the origin. Subsequently, we extend the results to nonlinear systems subject to asynchronous and delayed measurements and uncertain variables. Under the assumptions that there exist an upper bound on the interval between two consecutive asynchronous measurements and an upper bound on the maximum measurement delay, an economic MPC design which takes explicitly into account asynchronous and delayed measurements and enforces closed-loop stability is proposed. All the proposed economic MPC designs are illustrated through a chemical process example and their performance and robustness are evaluated through simulations. V C 2011 American Institute of Chemical Engineers AIChE J, 58: 855–870, 2012

/pdf/economic-model-predictive-control-of-nonlinear-process-kjkvo0pnfr.pdf

Economic model predictive control of nonlinear process systems using Lyapunov techniques

This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed-loop performance. In this case, it is desirable to design the pre-existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre-existing control system and the new control system are designed via Lyapunov-based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov-based controller that stabilizes the nominal closed-loop system using only the pre-existing control loops, two separate Lyapunov-based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov-based controller, improves the closed-loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009

/pdf/distributed-model-predictive-control-of-nonlinear-process-3qe4s0f9zi.pdf

Distributed model predictive control of nonlinear process systems

A review On reinforcement learning: Introduction and applications in industrial process control

This work focuses on the development of a supervisory model predictive control method for the optimal management and operation of hybrid standalone wind-solar energy generation systems. We design the supervisory control system via model predictive control which computes the power references for the wind and solar subsystems at each sampling time while minimizing a suitable cost function. The power references are sent to two local controllers which drive the two subsystems to the requested power references. We discuss how to incorporate practical considerations, for example, how to extend the life time of the equipment by reducing the peak values of inrush or surge currents, into the formulation of the model predictive control optimization problem. We present several simulation case studies that demonstrate the applicability and effectiveness of the proposed supervisory predictive control architecture.

/pdf/supervisory-predictive-control-of-standalone-wind-solar-3812bs21m1.pdf

Jinfeng Liu

Papers

Distributed model predictive control: A tutorial review and future research directions

Economic model predictive control of nonlinear process systems using Lyapunov techniques

Distributed model predictive control of nonlinear process systems

A review On reinforcement learning: Introduction and applications in industrial process control

Supervisory Predictive Control of Standalone Wind/Solar Energy Generation Systems