scholarly journals Application of Model Predictive Control to Renewable Energy Development via Reactive Distillation Process

2016 ◽  
Vol 27 ◽  
pp. 95-110 ◽  
Author(s):  
Saidat Olanipekun Giwa ◽  
Abel Adekanmi Adeyi ◽  
Abdulwahab Giwa
Keyword(s):  
Renewable Energy ◽  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Reactive Distillation ◽  
Regulatory Control ◽  
Distillation Process ◽  
Closed Loop System ◽  
Tuning Parameters

Reactive distillation is a process that combines chemical reaction and separation in a single piece of equipment (distillation column). The process has a lot of benefits especially for those reactions occurring at conditions suitable for the distillation of the process components, and these result in significant economic advantages. However, owing to the complexities resulting from the integration of reaction and separation, its control is still a challenge to process engineers because it requires a control method that is robust enough to handle its complexities. Therefore, in this work, model predictive control (MPC) has been applied to a reactive distillation process used for developing a renewable energy known as biodiesel. The control algorithm of the MPC was formulated with the aid of MPC toolbox of MATLAB/Simulink in which the closed-loop models of the process were developed and simulated. The analysis of the results obtained from the simulations carried out for the optimization of the tuning parameters revealed that, among the tuning parameters considered, integral absolute error of the control system was less affected by the control horizon because its p-value was greater than 0.05 based on 95% confidence level. Furthermore, the simulation of the closed-loop system of the process using model predictive control tuned with control horizon of 11, prediction horizon of 18, weight on manipulated variable rate of 0.05 and weight on output variable of 2.17, which were the optimum parameters obtained using Excel Solver, showed that the system was well handled by the controller under servo control because it was able to get settled at desired mole fractions within 60 min. However, the settling time recorded in the case of regulatory control system of the process with the same controller was found not to be encouraging. Therefore, it is recommended that further work should be carried out on this subject matter in an attempt to obtain tuning parameters that will make the settling time of the closed-loop system of the process under regulatory control simulation very reasonable.

Speed Model Predictive Control Based on the Track Error Rate

2013 ◽  
Vol 336-338 ◽  
pp. 839-842
Author(s):  
Jin Huang ◽  
Cheng Zhi Yang ◽  
Ji Feng Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Tracking Error ◽  
Matlab Simulation ◽  
Closed Loop System ◽  
Index Function ◽  
Dynamical Characteristics ◽  
Simulation Results ◽  
Controlled Object

In order to make the controlled object have better dynamical characteristics, through introducing the differential item of error into optimal performance index function of tracking error, an improved algorithm of model predictive control is discussed in this paper. The theoretical analysis and Matlab simulation results show that it has better controlled quality and stronger robustness for closed-loop system.

A Dual-Mode Model Predictive Control Algorithm Trajectory Tracking in Discrete-Time Nonlinear Dynamic Systems

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Asad A. Ul Haq ◽  
Michael E. Cholette ◽  
Dragan Djurdjanovic
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
Dynamic Systems ◽  
Trajectory Tracking ◽  
Closed Loop ◽  
Linear Control ◽  
Nonlinear Dynamic Systems ◽  
Closed Loop System ◽  
Dual Mode

In this paper, a dual-mode model predictive/linear control method is presented, which extends the concept of dual-mode model predictive control (MPC) to trajectory tracking control of nonlinear dynamic systems described by discrete-time state-space models. The dual-mode controller comprises of a time-varying linear control law, implemented when the states lie within a sufficiently small neighborhood of the reference trajectory, and a model predictive control strategy driving the system toward that neighborhood. The boundary of this neighborhood is characterized so as to ensure stability of the closed-loop system and terminate the optimization procedure in a finite number of iterations, without jeopardizing the stability of the closed-loop system. The developed controller is applied to the central air handling unit (AHU) of a two-zone variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system.

Ellipsoidal Lyapunov-based hybrid model predictive control for mixed logical dynamical systems with a recursive feasibility guarantee

2018 ◽  
Vol 41 (9) ◽  
pp. 2475-2487
Author(s):  
Alireza Olama ◽  
Mokhtar Shasadeghi ◽  
Amin Ramezani ◽  
Mostafa Khorramizadeh ◽  
Paulo R C Mendes
Keyword(s):  
Dynamical Systems ◽  
Model Predictive Control ◽  
Hybrid Model ◽  
Predictive Control ◽  
Robust Stability ◽  
Optimization Problem ◽  
Closed Loop ◽  
Loop System ◽  
Closed Loop System ◽  
Mixed Logical Dynamical

This paper proposes an ellipsoidal hybrid model predictive control approach to solve the robust stability problem of uncertain hybrid dynamical systems modelled by the mixed logical dynamical framework. In this approach, the traditional terminal equality constraint is replaced by an ellipsoid that results in a maximal positive invariant set for the closed-loop system. Then, a Lyapunov decreasing condition along with the robustness criterion is introduced to the optimization problem to achieve the robust stability of the closed-loop system. As the main advantages, the ellipsoidal terminal set proposed in this paper attains a larger domain of attraction along with the recursive feasibility guarantee. Moreover, the stability and robustness constraints are achieved by a lower prediction horizon, which leads to a smaller dimension optimization problem. In addition, to reduce the computational complexity of the corresponding optimization problem, a suboptimal version of the proposed algorithm is introduced. Finally, numerical and car suspension system examples show the capabilities of the proposed method.

Control of a Reactive Distillation Process Using Model Predictive Control Toolbox of MATLAB

2017 ◽  
Vol 30 ◽  
pp. 167-180 ◽  
Author(s):  
Abdulwahab Giwa ◽  
Abel Adekanmi Adeyi ◽  
Saidat Olanipekun Giwa
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Reactive Distillation ◽  
Research Work ◽  
Variable Rate ◽  
Control Parameters ◽  
Distillation Process ◽  
Prediction Horizon ◽  
Better Than

This research work has been carried out to investigate the application of the Model Predictive Control Toolbox contained in MATLAB in controlling a reactive distillation process used for the production of a biodiesel, the model of which was obtained from the work of Giwa et al.1. The optimum values of the model predictive control parameters were obtained by running the mfile program written for the implementation of the control simulation varying the model predictive control parameters (control horizon and prediction horizon) and recording the corresponding integral squared error (ISE). Thereafter, using the obtain optimum value of 5 and 15 for control horizon and prediction horizon respectively as well as a manipulated variable rate weight of 0.025 and an output variable rate weight of 1.10, various steps were applied to the setpoint of the controlled variable and the responses plotted. The results given by the simulations carried out by varying the model predictive control parameters (control horizon and prediction horizon) for the control of the system revealed that optimizing the control parameters is better than arbitrary choosing. Also, the simulation of the developed model predictive control system of the process showed that its performance was better than those used to control the same process using a proportional-integral-derivative (PID) controller tuned with Cohen-Coon and Ziegler-Nichols techniques. It has, thus, been discovered that the Model Predictive Control Toolbox of MATLAB can be applied successfully to control a reactive distillation process in order to obtain better performance than that obtained from a PID controller tuned with Cohen-Coon and Ziegler-Nichols methods.

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