A robust control system scheme based on model predictive controller (MPC) for continuous‐time systems

Abstract

Summary In this paper, an LMI framework based on model predictive strategy is addressed to design a robust dynamical control law in a typical control system. In the proposed method, instead of traditional static controller, a dynamic control law is used. With a suitable matrix transformation, the controller parameters selection are translated into an optimization problem with some LMI constraints. The plant input and output constraints are also handled with another LMIs. The controller is represented in state space form, and its parameters are computed in real‐time operation. For achieving this goal, by solving an optimization problem, a dynamic controller is designed, which meets the required plant performances. These results are used in 2 numerical examples to demonstrate the effectiveness of the proposed approach.

Robust integral controller design based on composite nonlinear feedback in uncertain systems with constrained inputs and external disturbances

“…Nowadays, to solve an LMI issue, there are various LMI software packages such as LMILab, YALMIP, SeDuMi, and so on. Hence, the controller parameters are numerically calculated by performing an optimization 33 . In uncertain control systems, most studies have focused on LMI‐based controller synthesis.…”

“…Hence, the controller parameters are numerically calculated by performing an optimization. 33 In uncertain control systems, most studies have focused on LMI-based controller synthesis. Recently, based on LMI representation, robust stabilization is accomplished in constrained uncertain systems.…”

A Linear Fractional Transformation Based Approach to Robust Model Predictive Control Design in Uncertain Systems

“…Also, the RMPC is developed using two-stages neural network modeling [29], considering state-dependent uncertainties [30], under partial actuator faults [31], guaranteeing stability and satisfying constraints [32], assuming saturated inputs and randomly occurring uncertainties [33], involving finite-time convergence result [34], and employing collective neuro-dynamic optimization [35]. Although the RMPC synthesis is primarily discussed in the discrete-time system, it is extended to continuous-time representations [36,37].…”