The MCA (Motion Cueing Algorithm) for driving simulator takes into account the simulator’s workspace limits and the driver’s motion perception thresholds to reproduce simulated vehicle’s accelerations. For the motion based driving simulators, the most applied MCAs are the classical and the optimal filters. This paper presents a new algorithm, called MPC (Model Predictive Control) explicit algor...

Modelbased Predictive Control (MPC) is a control technique that is widely used in chemical process industry. In the past decade, stability of MPC has been an intensive research area, resulting in the general acceptance of a theoretical MPC stability framework introducing a terminal cost and terminal constraint to the classic MPC formulation. Although guaranteeing stability, issues regarding opt...

In this study we discuss the application of robust optimization to the problem of economic energy dispatch in smart grids. Robust optimization based MPC strategies for tackling uncertain load demands are developed. Unexpected additive disturbances are modelled by defining an affine dependence between the control inputs and the uncertain load demands. The developed strategies were applied to a h...

Model predictive control (MPC) is a very popular controller design method in the process industry. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. Usually MPC uses linear or nonlinear discrete-time models. In this paper and its companion paper (“Part II: Hybrid Systems”) we give an overview of some results in connection with MPC approaches for discrete-e...

This paper considers discrete-time nonlinear, possibly discontinuous, systems in closed-loop with Model Predictive Controllers (MPC). The aim of the paper is to provide a priori sufficient conditions for asymptotic stability in the Lyapunov sense and robust stability, while allowing for both the system dynamics and the value function of the MPC cost (the usual candidate Lyapunov function in MPC...

A new framework is presented for distributed, linear model predictive control (MPC) with guaranteed nominal stability and performance properties. We first show that modeling the interactions between subsystems and exchanging trajectory information among MPCs (communication) is insufficient to provide even closed-loop stability. We next propose a cooperative distributedMPC framework, in which th...

Model predictive control (MPC) is a very popular controller design method in the process industry. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. Usually MPC uses linear or nonlinear discrete-time models. In this paper and its companion paper (“Part I: Discrete-Event Systems”) we give an overview of some results in connection with MPC approaches for som...

This paper presents industrial case studies of the performance evaluation of two industrial multivariate MPC-based controllers at the Mitsubishi chemical complex in Mizushima, Japan: 1) a 6-output, 6-input Para-Xylene(PX) production process with 6 measured disturbance variables that are used for feedforward control; and 2) a multivariate MPC controller for a 6-output, 5 input poly-propylene spl...

The analysis study has been done for a First Order Plus Delay Time (FOPDT) model controlled by Proportional Integral Derivative (PID), proportional integral(PI),Internal Model Controller(IMC) and Model Predictive Control (MPC) using MATLAB software. The study has been done for both MPC and conventional control methods to design the controller for the level tank system and the results has been c...