Robust Model Predictive Control Synthesis Research Articles

Designing of non-fragile robust model predictive control for constrained uncertain systems and its application in process control

A non-fragile robust model predictive control (RMPC) is designed in the uncertain systems under bounded control signals. To this aim, a class of the nonlinear systems with additive uncertainty is considered in its general form. The RMPC synthesis could lead to the proper selection of the controller’s gains. Thus, the non-fragile RMPC design is translated into a minimization problem subjected to some constraints in terms of linear matrix inequality (LMI). Hence, the controller’s gains are computed by solving such a minimization problem. In some numerical examples, the suggested non-fragile RMPC is compared with the other methods. The simulation results demonstrate the effectiveness of the proposed RMPC in comparison with similar techniques.

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

A novel robust model predictive control (RMPC) scheme is developed for uncertain nonlinear systems. To the RMPC design, firstly, the uncertain system would be described using a linear fractional transformation (LFT). Then, regarding the system’s uncertainties and control limitations, a linear matrix inequality (LMI) based control strategy is addressed to translate the RMPC synthesis into a minimization problem. Thus the controller’s gains are automatically updated at some time-instants by the solution of such optimization problem. Finally, the outcomes are numerically applied in some control examples. The simulation results show the effectiveness of the suggested robust predictive controller in comparison to similar RMPC techniques.

Robust model predictive control synthesis for state-delayed systems with randomly occurring input saturation nonlinearities

In this paper, a model predictive control algorithm is presented for linear parameter varying systems with both state delays and randomly occurring input saturation. The input saturation is assumed to be occurred randomly with Bernoulli-distributed white sequences. A constant sate feedback law is designed at each time instant to ensure the robust stability of the closed-loop system with respect to polytopic uncertainties. The optimization of model predictive controller is cast into solving a linear matrix inequalities optimization problem. Then, the results are extended to gain-scheduled approach in which a set of state feedback laws are designed for each vertex of the system model. The state feedback law is scheduled by the time varying model parameters to achieve less conservatism in controller design. Finally, two examples are employed to show the effectiveness of the proposed algorithms.