This paper is concerned with the design of Multi-Inputs and Multi-Outputs (MIMO) predictive PID controllers, which have similar features to the model-based predictive controller. A PID type control structure is defined which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the MIMO PID gains are calculated using th...

The aim of this paper is to study the application of model predictive controller for improving stability of rotor and rotor vibration. Rotor vibrations in electrical machines are dampen out by model predictive control algorithm. The controlled system is the one dimensional Jeffcott-rotor. Model predictive control algorithm was designed, and the simulation results were obtained by Mat lab softwa...

Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from inputout...

This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the pas...

In this work, we consider nonlinear systems with input constraints and uncertain variables, and develop a robust hybrid predictive control structure that provides a safety net for the implementation of any model predictive control (MPC) formulation, designed with or without taking uncertainty into account. The key idea is to use a Lyapunov–based bounded robust controller, for which an explicit ...

This paper presents an experimental application of a state-space predictive controller. The controller is used for control of a gas conditioning unit, an integrating process with two inputs and two outputs, in an industrial environment. The predictive controller is used as a general-purpose low-level controller, which is not usual for the established practice of industrial model predictive cont...

This paper aims to solve the fault tolerant control problem of a wind turbine benchmark. A hierarchical controller with model predictive pre-compensators, a global model predictive controller and a supervisory controller is proposed. In the model predictive pre-compensator, an extended Kalman Filter is designed to estimate the system states and various fault parameters. Based on the estimation,...

It has recently been proposed that it is advantageous to have models of dynamical systems be based solely on observable quantities. Predictive state representations (PSRs) are a type of model that uses predictions about future observations to capture the state of a dynamical system. However, PSRs do not use memory of past observations. We propose a model called memory-PSRs that uses both memori...

A nonlinear predictive controller (NMPC) is developed to control a unicycle-like mobile robot for trajectory tracking. Dynamic model of PIONNER 3-DX mobile robot is used, where external forces and wheels sliding have been considered. Restrictions on control actions and system states are also considered. Simulations results at both tracking and regulation (positioning) are shown, these results s...

This chapter proposes a reliable fault-tolerant model predictive control applied to drinking water transport networks. After a fault has occurred, the predictive controller should be redesigned to cope with the fault effect. Before starting to apply the fault-tolerant control strategy, it should be evaluated whether the predictive controller will be able to continue operating after the fault ap...