scholarly journals Linear Model Predictive Control for a Coupled CSTR and Axial Dispersion Tubular Reactor with Recycle

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 711
Author(s):  
Seyedhamidreza Khatibi ◽  
Guilherme Ozorio Cassol ◽  
Stevan Dubljevic
Keyword(s):  
Closed Loop ◽  
Controller Design ◽  
Tubular Reactor ◽  
Coupled System ◽  
Axial Dispersion ◽  
Continuous Stirred Tank Reactor ◽  
Model Predictive Controller ◽  
Luenberger Observer ◽  
In Series ◽  
Predictive Controller

This manuscript addresses a novel output model predictive controller design for a representative model of continuous stirred-tank reactor (CSTR) and axial dispersion reactor with recycle. The underlying model takes the form of ODE-PDE in series and it is operated at an unstable point. The model predictive controller (MPC) design is explored to achieve optimal closed-loop system stabilization and to account for naturally present input and state constraints. The discrete representation of the system is obtained by application of the structure properties (stability, controllability and observability) preserving Cayley-Tustin discretization to the coupled system. The design of a discrete Luenberger observer is also considered to accomplish the output feedback MPC realization. Finally, the simulations demonstrate the performance of the controller, indicating proper stabilization and constraints satisfaction in the closed loop.

scholarly journals Dynamic Modelling and Model Predictive Control of a Continuous Pulp Digester

2021 ◽  
Author(s):  
Lu Zhang ◽  
Junyao Xie ◽  
Stevan Dubljevic
Keyword(s):  
Controller Design ◽  
Dynamic Modelling ◽  
Observer Design ◽  
Hyperbolic Partial Differential Equations ◽  
Time Model ◽  
Model Predictive Controller ◽  
Luenberger Observer ◽  
Predictive Controller ◽  
Pulp Digester ◽  
Current Zone

This work explores the model predictive controller design of the continuous pulp digester process consisting of the co-current zone and counter-current zone modelled by a set of nonlinear coupled hyperbolic partial differential equations (PDE). The distributed parameter system of interest is not spectral and slow-fast dynamic separation does not hold. To address this challenge, the nonlinear continuous-time model is linearized and discretized in time utilizing the Cayley-Tustin discretization framework, which ensures system theoretic properties and structure preservation without spatial discretization or model reduction. The discrete model is used in the full state model predictive controller design, which is augmented by the Luenberger observer design to achieve the output constrained regulation. Finally, a numerical example is provided to demonstrate the feasibility and applicability of the proposed controller designs.

Multi-objective adaptive cruise controller design using nonlinear predictive controller with the objective function with variable weights determined by fuzzy logic controller

2021 ◽  
pp. 095440702110147
Author(s):  
Behzad Samani ◽  
Amir H. Shamekhi
Keyword(s):  
Objective Function ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Hierarchical Control ◽  
Control Input ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Control Step ◽  
Predictive Controller

In this paper, an adaptive cruise control system with a hierarchical control structure is designed. The upper-level controller is a model predictive controller (MPC) that by minimizing an objective function in the presence of the constraints, calculates the desired acceleration as control input and sends it to the lower-level controller. So the lower-level controller, which is a fuzzy controller, determines the amount of throttle valve opening or brake pressure to get the car to this desired acceleration. The model predictive controller performs optimization at each control step to minimize the objective function and achieve the reference values. Usually, the objective function has predetermined and constant weights to meet objectives such as maintain the driver’s desired speed and increase safety and in some cases increase comfort and reduce fuel consumption. In this paper, it is suggested that instead of using constant weights in the objective function, these weights should be determined by a fuzzy controller, depending on the different conditions in which the car is placed. The simulation results show that the variability of the weights of the objective function achieves control objectives much better than the optimization of the objective function with constant weights.

Online Hybrid Model Predictive Controller Design for Cruise Control of Automobiles

2017 ◽  
Author(s):  
Kaveh Merat ◽  
Jafar Abbaszadeh Chekan ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Hybrid Model ◽  
Optimization Problem ◽  
Controller Design ◽  
Research Work ◽  
Mixed Integer ◽  
Control Procedure ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Gear Shift ◽  
Predictive Controller

In the proposed study, a Hybrid Model Predictive Controller is introduced for cruise control of an automobile model. The presented model consists of the engine, the gearbox, and the transmission dynamics, where the aerodynamics force and elastic friction between the tires and road are taken into account. Through Piecewise Linearization of nonlinearities in the system; (torque)-(throttle)-(angular velocity) of engine and (aerodynamic drag force)-(automobile velocity), a comprehensive piecewise linear model for the system is obtained. Then combined with the switch and shift between engaged gears in gearbox, the Piecewise Affine (PWA) model for the vehicle dynamics is acquired. As far as the control design is concerned, the cruise control problem for tracking a desired speed fashion is addressed by a MPC-based controller design. The proposed control approach is based on the online model predictive control, applied on the obtained PWA dynamics. The highlighted novelties of the presented research work are summarized as: first a more complete model is examined due to the consideration of a realistic model for engine. This improvement makes the polyhedron regions of the PWA system dependent to both state variable (i.e., velocity) and input signals (i.e., throttle and engaged gear) which brings the complexity to the design of control procedure. Second, due to the switch in the dynamics and dependence of our PWA model to discrete input (gear shift), the desperate need to solve the optimization problem through mixed integer programming, which needs high computation effort specially for our system, seems inevitable. We triumph over this challenge through introducing “possible gear shift scenario” sets. Hence, by constraining the optimization problem to the introduced logical sets, the problem still remains convex optimization type and the computation volume is reduced. In addition, we hired branch and bound method which allowed us to have large problems to be solved in a tractable amount of time and computation resources. At last, some simulations are presented to exhibit the performance of the proposed method.

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