Sliding mode predictive control for chemical proces with time delay

16th IFAC World Congress Praga (República Checa), 03/07/2005A design of a novel model predictive controller is presented. The proposed Sliding Mode Predictive Control (SMPC) algorithm combines the design technique of Sliding-Mode Control (SMC) with Model based Predictive Control (MPC). The SMPC showed a considerable robustness improvement with respect to MPC in the presence of time delay, and showed an enhanced ability to handle set point changes in a nonlinear process. Its robustness was evaluated using a robustness plot, its performance was judged using a single input single output nonlinear mixing tank process with variable time delay

MODEL BASED PREDICTIVE CONTROL OF UNDERACTUATED NONLINEAR MECHATRONICAL SYSTEMS

The paper deals with model predictive control of underactuated nonlinear mechatronical systems along known reference path. It generalizes the state space predictive control algorithm of linear time invariant (LTI) systems to linearized time variant (LTV) systems. An algorithm is presented, which calculates the LTV model online from the nonlinear model along the reference trajectory. The LTV is then used in the framework of the predictive control to find the optimal control in closed analytical form without using online optimum search in the moving horizon. After MATLAB-based simulation results of the algorithm, successful test experiments were performed for the predictive control of a real inverted pendulum system, both in the swinging up and upper stabilization phases

Model-based control algorithms for the quadruple tank system: An experimental comparison

We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the parameters in a continuous-discrete time stochastic nonlinear model for the QTS using a prediction-error-method based on the measured process data and a maximum likelihood (ML) criterion. In the NMPC algorithm, we use this identified continuous-discrete time stochastic nonlinear model. The LMPC algorithm is based on a linearization of this nonlinear model. We tune the PID controller using Skogestad's IMC tuning rules using a transfer function representation of the linearized model. Norms of the observed tracking errors and the rate of change of the manipulated variables are used to compare the performance of the control algorithms. The LMPC and NMPC perform better than the PID controller for a predefined time-varying setpoint trajectory. The LMPC and NMPC algorithms have similar performance.Comment: 6 pages, 5 figures, 3 tables, to be published in Foundations of Computer Aided Process Operations / Chemical Process Control (FOCAPO/CPC 2023). Hilton San Antonio Hill Country, San Antonio, Texa

A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.© 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)fi=vertaisarvioitu|en=peerReviewed

Multiplexed Predictive Control of a Large Commercial Turbofan Engine

Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

The aim of this paper is to evaluate the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area. The model predictive control is based on an objective function minimization. Two approaches to model predictive control applied on a nonlinear system are studied here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The nonlinear system simulated here is an exothermic semi-batch reactor mathematical model based on a real chemical exothermic process. Also the input data used here to simulate the process were obtained from the same real process. Results obtained by the simulation means were than evaluated using suitable criterion which was defined for that purpose and discussed. © 2018 The Authors, published by EDP Sciences