scholarly journals Model Predictive Control for First-Order Hyperbolic System Based on Quasi-Shannon Wavelet Basis

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1114
Author(s):  
Ling Ai ◽  
Kok Lay Teo ◽  
Liwei Deng ◽  
Desheng Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Heating System ◽  
Order System ◽  
Distributed Parameter ◽  
Wavelet Basis ◽  
First Order ◽  
Forward Difference ◽  
Shannon Wavelet ◽  
Predictive Controller

In this paper, we consider a class of first-order hyperbolic distributed parameter systems. Our focus is on the design of a new class of model predictive control schemes using a quasi-Shannon wavelet basis. First, the first-order hyperbolic distributed parameter system is transformed into an equivalent system using collocation techniques for the approximation of spatial derivatives and Euler forward difference method for the approximation of the time component. Then, a model reduction method is applied to obtain a reduced-order system on which a nonlinear model predictive controller is designed through solving a nonlinear quadratic programming problem with input constraints. For illustration, the temperature control of a flow-control long-duct heating system is considered to be an example. A comparative simulation study is conducted to demonstrate the effectiveness of the proposed method.

scholarly journals Incremental State-Space Model Predictive Control of a Fresnel Solar Collector Field

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 3 ◽  
Author(s):  
Eduardo Camacho ◽  
Antonio Gallego ◽  
Adolfo Sanchez ◽  
Manuel Berenguel
Keyword(s):  
Model Predictive Control ◽  
Power Systems ◽  
State Space ◽  
Predictive Control ◽  
State Space Model ◽  
Distributed Parameter ◽  
Space Model ◽  
Luenberger Observer ◽  
Predictive Controller ◽  
Nonlinear Distributed Parameter Model

Model predictive control has been demonstrated to be one of the most efficient control techniques for solar power systems. An incremental offset-free state-space Model Predictive Controller (MPC) is developed for the Fresnel collector field located at the solar cooling plant installed on the roof of the Engineering School of Sevilla. A robust Luenberger observer is used for estimating the states of the plant which cannot be measured. The proposed strategy is tested on a nonlinear distributed parameter model of the Fresnel collector field. Its performance is compared to that obtained with a gain-scheduling generalized predictive controller. A real test carried out at the real plant is presented, showing that the proposed strategy achieves a very good performance.

Coordinated Model Predictive Control on Multi-Lane Roads

2019 ◽  
Author(s):  
Nathan Goulet ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Heterogeneous Traffic ◽  
Road Transportation ◽  
The Road ◽  
Energy Saving Potential ◽  
Coordination Scheme ◽  
Predictive Controller

Abstract There are significant economic, environmental, energy, and other societal costs incurred by the road transportation sector. With the advent and penetration of connected and autonomous vehicles there are vast opportunities to optimize the control of individual vehicles for reducing energy consumption and increasing traffic flow. Model predictive control is a useful tool to achieve such goals, while accommodating ego-centric objectives typical of heterogeneous traffic and explicitly enforcing collision and other constraints. In this paper, we describe a multi-agent distributed maneuver planning and lane selection model predictive controller that includes an information sharing and coordination scheme. The energy saving potential of the proposed coordination scheme is then evaluated via large scale microscopic traffic simulations considering different penetration levels of connected and automated vehicles.

Model Predictive Control of General Fractional Order Systems Using a General Purpose Optimal Control Problem Solver: RIOTS

2019 ◽  
Author(s):  
Sina Dehghan ◽  
Tiebiao Zhao ◽  
YangQuan Chen ◽  
Taymaz Homayouni
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Fractional Order ◽  
Predictive Control ◽  
Order System ◽  
Problem Solver ◽  
Fractional Order Systems ◽  
Application Process ◽  
Order Model ◽  
Integer Order

Abstract RIOTS is a Matlab toolbox capable of solving a very general form of integer order optimal control problems. In this paper, we present an approach for implementing Model Predictive Control (MPC) to control a general form of fractional order systems using RIOTS toolbox. This approach is based on time-response-invariant approximation of fractional order system with an integer order model to be used as the internal model in MPC. The implementation of this approach is demonstrated to control a coupled MIMO commensurate fractional order model. Moreover, the performance and its application process is compared to examples reported in the literature.

Model Predictive Control of a Distributed-Parameter Process Employing a Moving Radiant Actuator

2010 ◽  
Author(s):  
Fan Zeng ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Process Model ◽  
Energy Use ◽  
State Parameter ◽  
Source Model ◽  
Distributed Parameter ◽  
Optimization Approach ◽  
Radiant Intensity ◽  
Process Energy

Many industrial processes employ radiation-based actuators with two or more manipulated variables. Moving radiant actuators, in particular, act on a distributed parameter process where the velocity of the actuator is an additional manipulated variable with its own constraints. In this paper, a model predictive control (MPC) scheme is developed for a distributed-parameter process employing such a moving radiant actuator. The designed MPC controller uses an online optimization approach to determine both the radiant intensity and velocity of the moving actuator based on a linearized process model and a distributed state/parameter estimator. A particular source-model reduction that enables the approach is outlined. The proposed strategy is then demonstrated for a radiative curing process considering different control scenarios with the objective of achieving desired cure level uniformity and minimizing process energy use.

scholarly journals Nonlinear model predictive control of a boiler unit: A fault tolerant control study

2012 ◽  
Vol 22 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Krzysztof Patan ◽  
Józef Korbicz
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Nonlinear Model Predictive Control ◽  
Boiler Unit ◽  
Predictive Controller ◽  
One Step

Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.

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