Adaptive PID controller based on model predictive control

2016 ◽  
Author(s):  
Ahmed A. Abdelrauf ◽  
M. Abdel-Geliel ◽  
E. Zakzouk
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller

Modeling and Model Predictive Control of Semi-Batch Chemical Reactor

2013 ◽  
Vol 791-793 ◽  
pp. 822-825
Author(s):  
Lubomír Macků ◽  
David Novosad ◽  
David Sámek
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Chemical Engineering ◽  
Chemical Reactor ◽  
Batch Process ◽  
Online Identification ◽  
Whole Process ◽  
Process Simulations ◽  
Reactor Geometry

The paper presents a control mechanism design for a semi-batch chemical reactor. The data obtained by chemical engineering analysis of real experiments are used to simulate the semi‑batch process. A mathematical model based on the real reactor geometry and size is used to simulate the whole process. The process simulations are implemented in MATLAB / Simulink environment and suitable PID and Model Predictive Control are also proposed. Because of that the chemical reactor is a complex and nonlinear system, the PID controller has to use an online identification to be able to deal with nonlinearities. Results obtained by simulations are compared and discussed.

scholarly journals Modeling and Control of Crane Overload Protection During Marine Lifting Operation Based on Model Predictive Control

2017 ◽  
Author(s):  
Zhengru Ren ◽  
Roger Skjetne ◽  
Zhen Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Multiple Shooting ◽  
Control Input ◽  
Lumped Mass ◽  
Modeling And Control ◽  
Overload Protection ◽  
And Control

This paper deals with a nonlinear model predictive control (NMPC) scheme for a winch servo motor to overcome the sudden peak tension in the lifting wire caused by a lumped-mass payload at the beginning of a lifting off or a lowering operation. The crane-wire-payload system is modeled in 3 degrees of freedom with the Newton-Euler approach. Direct multiple shooting and real-time iteration (RTI) scheme are employed to provide feedback control input to the winch servo. Simulations are implemented with MATLAB and CaSADi toolkit. By well tuning the weighting matrices, the NMPC controller can reduce the snatch loads in the lifting wire and the winch loads simultaneously. A comparative study with a PID controller is conducted to verify its performance.

Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration

2017 ◽  
Vol 7 (3) ◽  
pp. 1538 ◽  
Author(s):  
Zakariah Yusuf ◽  
Norhaliza Abdul Wahab ◽  
Abdallah Abusam
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Membrane Fouling ◽  
Pid Controller ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Permeate Flux ◽  
Hybrid Particle Swarm Optimization

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux.

scholarly journals Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6594
Author(s):  
Ahmed Aboelhassan ◽  
M. Abdelgeliel ◽  
Ezz Eldin Zakzouk ◽  
Michael Galea
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Mimo System ◽  
Industrial Applications ◽  
Industrial Processes ◽  
Adaptation Mechanism ◽  
Advanced Control ◽  
Automation Technology

Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative (PID) controller is a common industrial controller known for its simplicity and robustness. Adapting the parameters of the PID considering system constraints is a challenging task. Both controllers, MPC and PID, merged in a hierarchical structure in this work to improve the industrial processes performance considering the operational constraints. The proposed control system is simulated and implemented on a three-tank benchmark system as a Multi-Input Multi-Output (MIMO) system. Since the main industrial goal of the proposed configuration is to be easily implemented using the available automation technology, PID controller is implemented in a PLC (Programable Logic Controller) controller as a lower controller level, while MPC controller and the adaptation mechanism are implemented within a SCADA (Supervisory Control And Data Acquisition) system as a higher controller level.

Classical and Neural Network–Based Approach of Model Predictive Control for Binary Continuous Distillation Column

2014 ◽  
Vol 9 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Amit Kumar Singh ◽  
Barjeev Tyagi ◽  
Vishal Kumar
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Distillation Column ◽  
Chemical Engineering ◽  
Research Work ◽  
Settling Time ◽  
Training Data ◽  
Main Process

Abstract The objective of present research work is to develop a neural network–based model predictive control scheme (NN-MPC) for distillation column. To fulfill this objective, an existing laboratory setup of continuous binary-type distillation column (BDC) is used. An equation-based model that uses the fundamental physical and chemical laws along with valid normal assumptions is validated for this experimental setup. Model predictive control (MPC) is one of the main process control techniques explored in the recent past for various chemical engineering applications; therefore, the conventional MPC scheme and the proposed NN-MPC scheme are applied on the equation-based model to control the methanol composition. In NN-MPC scheme, a three-layer feedforward neural network model has been developed and is used to predict the methanol composition over a prediction horizon using the MPC algorithm for searching the optimal control moves. The training data is acquired by the simulation of the equation-based model under the variation of manipulated variables in the defined range. Two cases have been considered, one is for set point tracking and another is for feed flow disturbance rejection. The performance of the control schemes is compared on the basis of performance parameters namely overshoot and settling time. NN-MPC and MPC schemes are also compared with conventional PID controller. The results show the improvement in settling time with NN-MPC scheme as compared to MPC and conventional PID controller for both the cases.

scholarly journals Applicant hierarchical fuzzy controller for concentration control of simulated moving bed

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chaofan Xie ◽  
Yang-jie Tang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Continuous Process ◽  
Simulated Moving Bed ◽  
Moving Bed ◽  
Prediction And Control ◽  
Precise Prediction ◽  
And Control

AbstractSimulated moving bed (SMB) is a kind of continuous process which can increase the efficiency of adsorbents in the adsorbent bed. It contains several sectors of flow rate, the switching time of valves and many other possible influencing variables, moreover, these parameters are highly sensitive, so it is very difficult to achieve precise prediction and control. Model predictive control and PID controller are often used in industrial system. Model predictive control needs a lot of accurate industry experience data, and PID controller depends on the selection of control parameters. Therefore, SMB needs an intelligent controller to bypass those complex mechanisms and parameter adjustment processes. This paper we propose the hierarchical fuzzy controller fuzzy controller which is applied to the SMB system to observe the final concentration. Compared with the PID and MPC controller, it is found that the hierarchical fuzzy controller can control good without knowing the system parameters too accurately.

scholarly journals Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 899
Author(s):  
Dawei Hu ◽  
Gangyan Li ◽  
Feng Deng
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Commercial Vehicle ◽  
Chamber Pressure ◽  
Nonlinear Mathematical Model ◽  
Proportional Valve ◽  
Linear Parameter Varying ◽  
Air Brake System ◽  
Gain Scheduled

This paper presents a control-oriented Linear Parameter-Varying (LPV) model for commercial vehicle air brake systems with the electro-pneumatic proportional valve based on the nonlinear mathematical model, a set of discrete-time linearized models at different target pressures with the q-Markov Cover system identification method. The scheduled parameters for the LPV model were the brake chamber pressure, which was controlled by the electro-pneumatic proportional valve. On the basis of the LPV model, a family of Model Predictive Control (MPC) controllers with a Kalman filter was designed at each operation point. Then, the gain-scheduled MPC was designed over the entire operating range with the switched strategy, which was validated by experimental data. Furthermore, compared with the PID controller, the performance of the system was improved with a gain-scheduled MPC controller.

scholarly journals Research and development of the controller based on the model predictive control

2021 ◽  
Vol 29 (1) ◽  
pp. 36-45
Author(s):  
Aleksey A. Kolodin ◽  
Viktor V. Elshin
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Delay Time ◽  
Pid Controller ◽  
High Performance ◽  
Control Object ◽  
Transition Process ◽  
Manufacturing Execution System ◽  
Differential Control

Modern automated process control systems that use programmable logic controllers use software controllers based on the proportional integral-differential control law, the PID controller. In most cases, this regulator is sufficient for conducting the technological process. It has high performance with configurable and sufficient quality of regulation. However, using a PID controller for non-linear, poorly defined, multi-connected objects with a long delay time can lead to unstable control quality indicators, accumulation of errors, and ultimately to a deterioration in product quality. One of the most promising methods of control is Model Predictive Control - MPC. The method base on predictive models of control objects. The quality of the controller's control depends on how well the system dynamics described by the model used to design the controller. In most cases, MPC-based control use to handle optimal control problems on the Manufacturing Execution System-MES. However, thanks to the development of microprocessors and increased CPU performance, it becomes possible to apply the principles of control with a predictive model at a lower level, and perform real-time operational control in optimal modes. The work presents the algorithm of MPC controller. The control object is a SISO object with a nonlinear characteristic and a long transition process. Studies of the developed MPC regulator showed that the quality of regulation, compared to the PID regulator, increased by more than 20%, the time to get to set point decreased, and there was almost no over-regulation. The most effective application of the MPC controller is seen in processes with long transitions and with a significant delay time.

scholarly journals Self-adjusting model predictive control for modular subsystems in HVAC systems

2021 ◽  
Vol 2042 (1) ◽  
pp. 012037
Author(s):  
Alexander Kümpel ◽  
Phillip Stoffel ◽  
Dirk Müller
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Air Conditioning ◽  
Hvac Systems ◽  
Model Parameters ◽  
Controlled System ◽  
The Past ◽  
Efficient Control ◽  
General Dynamics

Abstract In order to reduce the energy consumption and CO2 emissions in the building sector, an efficient control strategy, such as model predictive control (MPC) is required. However, MPC is rarely applied in buildings since the implementation and modeling is complex, time consuming and costly. To bring MPC into practice, controllers and models are needed, that automatically adapt their behavior to the controlled system. In this work, such a self-adjusting MPC applicable to heating, ventilation and air-conditioning (HVAC) systems is developed. The MPC is based on a simple grey-box model that is able to cover the general dynamics of the considered subsystem. The controller adapts the model parameters online according to the past measurements of the controlled system using a moving horizon estimation. The developed self-adjusting MPC is applied to three heating coils in a simulation. Compared with a PID controller, the self-adjusting MPC is able to increase the control quality up to 10%, while no manual tuning is needed. Additionally, the model predictive approach is able to reduce the power consumption of the pump by 80%.

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