Robust Output Feedback Model Predictive Control: A Stochastic Approach

Abstract

This paper addresses the robust explicit model predictive control scheme for linear systems with input and output constraint in the presence of disturbances and noise. Conditions for disturbance rejection are established by incorporating a full state/disturbance observer. The separation principle is applied to design an optimal observer in the unconstrained problem. Then, an efficient algorithm is developed to explicitly design observer gains by minimizing a quadratic performance criterion. It is shown that the solution includes a set of regions with piecewise affine functions of the state and reference vectors and a set of regions with optimal observers. In the proposed method, two sets of partitions associated with the control law and the observer gains are obtained. Therefore, the online computation includes finding the active regions of both observer and control law partitions in which the current state is located. The proposed technique is particularly attractive for a wide range of practical problems where the exact model of the actual system is not available.

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

“…−0: Considering unknown input disturbances and unknown output disturbances, (14) can be expressed as [63][64][65][66]:…”

Managing a hybrid energy smart grid with a renewable energy source

“…Each approach has its own features (in terms of precision in the determination of the optimal solution, calculation time, etc.) . By referring to the technique applied for establishing the operational planning of an MG, four categories can be distinguished: heuristic methods ; analytical methods that include linear programming (LP) and nonlinear programming (NLP) ; intelligent search methods such as the taboo search (TS), the ant colony search algorithm (ACS), and the particle swarm (PS) , and fuzzy control applications ; dynamic programming (Bellman algorithm) . …”

“…Each approach has its own features (in terms of precision in the determination of the optimal solution, calculation time, etc.) [16]. By referring to the technique applied for establishing the operational planning of an MG, four categories can be distinguished:…”

“…Unfortunately, due to its nonlinear nature, this is not the case in the problem of managing an MG. In [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], the nonlinear optimization problem of MG management is solved by quadratic programming. Although these approaches are simple in their application, the accuracy and optimality of the solution are often questioned [24].…”

An iterative model predictive control algorithm for constrained nonlinear systems

“…This model is used at regular intervals to predict the system optimal control inputs and respective system responses with respect to a performance index. The ability of the method to handle constraints in a simple way and its widespread applicability has motivated the development of many works in both industry and academia [1][2][3][4][5][6][7][8] and many of these studies have focused on extending the applicability of MPC to systems with faster dynamics than those that can be handled by the standard MPC algorithm [9][10][11].…”