Model Predictive Control System Research Articles

Safe economic model predictive control of nonlinear systems

This work focuses on the design of a new class of economic model predictive control (EMPC) systems for nonlinear systems that address simultaneously the tasks of economic optimality, safety and closed-loop stability. This is accomplished by incorporating in the EMPC an economics-based cost function and Control Lyapunov-Barrier Function (CLBF)-based constraints that ensure that the closed-loop state does not enter unsafe sets and remains within a well-characterized set in the system state-space. The new class of CLBF-EMPC systems is demonstrated using a nonlinear chemical process example.

Data‐Driven Performance Monitoring for Model Predictive Control Using a mahalanobis distance based overall index

AbstractThis paper proposes a data‐driven approach for model predictive control (MPC) performance monitoring. It explores the I/O data of the MPC system. First, to evaluate the MPC performance and capture the fluctuation of the process variables, we present an overall performance index based on Mahalanobis distance (MDBI) with its deduced benchmark. The Mahalanobis distance can better characterize the change of the process variable in both principal component space and residual space. As the proper vectors of the two spaces are orthogonal, the MDBI eliminates the correlation between the process variables while considering the variables’ characteristics in both spaces simultaneously, which helps evaluate the MPC performance more effectively with fewer monitoring parameters. Furthermore, for the MPC performance diagnosis, we use the MDBI as inputs and construct a support vector machine (SVM) pattern classifier. The classifier can achieve a higher accuracy when recognizing four common performance degradation patterns and determine the root cause of performance degradation. The results of simulations on the Wood‐Berry distillation column process and experiments on NIAT multifunctional experiment platform illustrate the effectiveness of the proposed performance assessment/diagnosis strategies.

MPC-based compensation control system for the yaw stability of distributed drive electric vehicle

ABSTRACTConventional yaw stability strategy of distributed drive electric vehicle (DDEV) is usually realised by torque distribution strategy. However, the instantaneous variations of four independent tyres slip ratio and the effect of disturbance have not been considered sufficiently. Therefore, it is difficult to realise the robustness of yaw stability for DDEV under various operating conditions. To solve this problem, a novel model predictive controller-based compensation control system (MPC-CCS) is proposed in this paper. The proposed MPC-CCS consists of two parts, an MPC based-feedback controller and a Kalman filter based-feedforward controller. In the feedback controller, a dual torque distribution scheme is adopted to obtain optimal torque values derived from the real-time signals of four independent tyres slip ratio, an MPC is designed to realise optimal torque values for vehicle yaw motion. In the feedforward controller, a Kalman filter is employed to attenuate the effect of the disturbance on yaw performance. In this way, the robustness of yaw stability for DDEV can be guaranteed by the proposed MPC-CCS. The proposed MPC-CCS is evaluated on eight degrees of freedom simulation platform and simulation results of different conditions show the effectiveness of the MPC-CCS.

Overcoming Control Challenges in Urban Traffic

Along with the fast development of the transportation infrastructures, traffic networks become larger and more complex. Therefore, Intending to reduce traffic congestion in urban areas due to the economical and environmental problems, analysis and control of existing infrastructures is indispensable, which in order improve safety and reduce the time spend by cars on the roads. This study compares a structured network-wide traffic controller based on Model Predictive Control (MPC) theory, which it represent a Simplified model (S model) and fixed-time (FT) controller. The fixed-time control strategy is executed in the traffic network, where the fixed-time signals are designed based on the data for the saturated scenario. Based on historical data, the fixed time assumes that the traffic patterns could be predicted accurately. On the other hand, MPC controllers are able to be efficient by reducing computation time and to be accurate too, in a systematic methodology to control multi-modal traffic lights based on a model predictive control approach. Then this study focus on two issues, the first one is the nonlinearity of the traffic prediction model and it is demonstrated how this model could be simplified to reduce the on-line computation time, then the complex urban road networks can be handled by the control system with more efficiently. The second issue is to propose a reduction of the congestion, within the model predictive control system based on internal flow model to minimize a cost function along a given time horizon.

Enhancing Strong Neighbor-Based Optimization for Distributed Model Predictive Control Systems

This paper considers a class of large-scale systems which is composed of many interacting subsystems, and each of them is controlled by an individual controller. For this type of system, to improve the optimization performance of the entire closed-loop system in a distributed framework without the entire system’s information or too-complicated network information, connectivity is always an important topic. To achieve this purpose, a distributed model predictive control (DMPC) design method is proposed in this paper, where each local model predictive control (MPC) considers the optimization performance of its strong coupling subsystems and communicates with them. A method to determine the strength of the coupling relationship based on the closed-loop system’s performance and subsystem network connectivity is proposed for the selection of each subsystem’s neighbors. Finally, through integrating the steady-state calculation, the designed DMPC is able to guarantee the recursive feasibility and asymptotic stability of the closed-loop system in the cases of both tracking set point and stabilizing system to zeroes. Simulation results show the efficiency of the proposed DMPC.

Implementation and Development of a Trajectory Tracking Control System for Intelligent Vehicle

In this paper, a trajectory tracking control system, which consists of a model predictive control unit and an active safety steering control unit, has been developed. A nonlinear bicycle vehicle model, including the longitudinal, lateral, yaw, and quasi-static roll motions, was derived as a predictive model to simulate and test the proposed model predictive control (MPC) system. A 4-DOF vehicle model was used to reflect the characteristics of vehicle dynamics to avoid rollover accidents of automobiles. Simulation was performed and experiment results demonstrated good performance of both MPC unit and active safety steering control unit. Finally, it was proved that the proposed trajectory tracking control system is easy to realize with low cost.

Safeness Index-Based Economic Model Predictive Control of Stochastic Nonlinear Systems

Process operational safety plays an important role in designing control systems for chemical processes. Motivated by this, in this work, we develop a process Safeness Index-based economic model predictive control system for a broad class of stochastic nonlinear systems with input constraints. A stochastic Lyapunov-based controller is first utilized to characterize a region of the state-space surrounding the origin, starting from which the origin is rendered asymptotically stable in probability. Using this stability region characterization and a process Safeness Index function that characterizes the region in state-space in which it is safe to operate the process, an economic model predictive control system is then developed using Lyapunov-based constraints to ensure economic optimality, as well as process operational safety and closed-loop stability in probability. A chemical process example is used to demonstrate the applicability and effectiveness of the proposed approach.

Sustainable Model-Predictive Control in Urban Traffic Networks: Efficient Solution Based on General Smoothening Methods

Traffic-responsive control approaches, including model-predictive control (MPC), are efficient methods for making the best use of the available network capacity. Moreover, gradient-based approaches, which can be applied to smooth optimization problems, have proven their efficiency, both computationally and performance-wise, in finding optima of optimization problems. In this paper, we propose an MPC system for an urban traffic network that applies a gradient-based optimization approach to solve the control optimization problem. The controller uses a new smooth integrated flow-emission model to find a balanced tradeoff between reduction of the congestion and of the total emissions. We also introduce efficient smoothening methods for nonsmooth mathematical models of physical systems. The effectiveness of the proposed approach is demonstrated via a case study.

Economic model predictive control of stochastic nonlinear systems

This work focuses on the design of stochastic Lyapunov‐based economic model predictive control (SLEMPC) systems for a broad class of stochastic nonlinear systems with input constraints. Under the assumption of stabilizability of the origin of the stochastic nonlinear system via a stochastic Lyapunov‐based control law, an economic model predictive controller is proposed that utilizes suitable constraints based on the stochastic Lyapunov‐based controller to ensure economic optimality, feasibility and stability in probability in a well‐characterized region of the state‐space surrounding the origin. A chemical process example is used to illustrate the application of the approach and demonstrate its economic benefits with respect to an EMPC scheme that treats the disturbances in a deterministic, bounded manner. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3312–3322, 2018

Parameter estimation for grey-box models of building thermal behaviour

Good models for building thermal behaviour are an important part of developing building energy management systems that are capable of reducing energy consumption for space heating through model predictive control. A popular approach to modelling the temperature variations of buildings is grey-box models based on lumped parameter thermal networks. By creating simplified models and calibrating their parameters from measurement data, the resulting model is both accurate and shows good generalisation capabilities. Often, parameters of such models are assumed to be a combination of different physical attributes of the building, hence they have some physical interpretation. In this paper, we investigate the dispersion of parameter estimates by use of randomisation. We show that there is significant dispersion in the parameter estimates when using randomised initial conditions for a numerical optimisation algorithm. Further, we claim that in order to assign a physical interpretation to grey-box model parameters, we require the estimated parameters to converge independently of the initial conditions and different datasets. Despite the dispersion of estimated parameters, the prediction capability of calibrated grey-box models is demonstrated by validating the models on independent data. This shows that the models are usable in a model predictive control system.

Nonlinear dynamic simulation and control of large-scale reheating furnace operations using a zone method based model

Modern reheating furnaces are complex nonlinear dynamic systems having heat transfer performances which may be greatly influenced by operating conditions such as stock material properties, furnace scheduling and throughput rate. Commonly, each furnace is equipped with a tailored model predictive control system to ensure consistent heated product quality such as final discharge temperature and temperature uniformity within the stock pieces. Those furnace models normally perform well for a designed operating condition but cannot usually cope with a variety of transient furnace operations such as non-uniform batch scheduling and production delay from downstream processes. Under these conditions, manual interventions that rely on past experience are often used to assist the process until the next stable furnace operation has been attained. Therefore, more advanced furnace control systems are useful to meet the challenge of adapting to those circumstances whilst also being able to predict the dynamic thermal behaviour of the furnace. In view of the above, this paper describes in detail an episode of actual transient furnace operation, and demonstrates a nonlinear dynamic simulation of this furnace operation using a zone method based model with a self-adapting predictive control scheme. The proposed furnace model was found to be capable of dynamically responding to the changes that occurred in the furnace operation, achieving about ±10 °C discrepancies with respect to measured discharge temperature, and the self-adapting predictive control scheme is shown to outperform the existing scheme used for furnace control in terms of stability and fuel consumption (fuel saving of about 6%).

On integration of feedback control and safety systems: Analyzing two chemical process applications

This work focuses on two case studies and attempts to elucidate the dynamic interaction between feedback control and safety systems in the context of both model-based and classical control systems. In the first case study, the interaction of a model predictive control (MPC) system with a safety system is studied in the context of the methyl isocyanate (MIC) hydrolysis reaction in a continuous stirred tank reactor (CSTR) to avoid thermal runaway. We develop a specific action for the MPC to take when the safety system is activated due to significant feed disturbances that lead to thermal runaway conditions. In the second case study, we focus on a high-pressure flash drum separator for which the temperature, level, and pressure can be regulated using proportional-integral (PI) controllers. Using a large-scale dynamic process simulator, we demonstrate that modifying the tuning parameters of one of these PI controllers based on the safety system being on or off leads to improved closed-loop performance compared to the case in which the tuning parameters of the PI controller remain the same regardless of the state of the safety system.

A Bilevel Optimization Approach to Coordination of Distributed Model Predictive Control Systems

A novel algorithm is proposed to coordinate distributed model predictive control (DMPC) systems. The main goal of coordinated distributed model predictive (CDMPC) is to improve the performance of an existing decentralized MPC system, which neglects the interactions among subsystems. In the first scenario, an analytic approach derives the global optimal solution to the plant-wide problem in the absence of inequality constraints in local controllers. In the second scenario, an iterative approach determines a local optimal solution to the general CDMPC problem based on the method of feasible directions. The proposed CDMPC algorithm is capable of stabilizing open-loop unstable dynamics.

Neural Network Optimized Model Predictive Multi-Objective Adaptive Cruise Control

A model predictive multi-objective adaptive cruise control (MPC MO-ACC) system, designed to consider both the tracking performance and the fuel consumption, is optimized by a neural network in this paper, reducing the computational complexity without sacrificing the control performance. The optimized MO-ACC control system is built by training a neural network with the control results of the MPC MO-ACC system. Simulation tests are conducted in Matlab/Simulink in conjunction with the high-fidelity CarMaker software. Influences of four driving conditions (the learning track, NEDC, JP05, FTP75) and two kinds of sensor models (ideal radar sensor and 77GHz physical radar sensor) are analysed. Simulation results have shown that the neural network optimized model predictive MO-ACC has the same control capability and strong robustness as the original MPC MO-ACC. Meanwhile, the optimized control system has much lower computational complexity, which shows potentials for the application in real-time vehicle control and industry.

Control Lyapunov-Barrier Function-Based Economic Model Predictive Control of Nonlinear Systems

This work focuses on the design of a new class of economic model predictive control (EMPC) systems for nonlinear process systems that address simultaneously the tasks of economic optimality, safety and closed-loop stability. This is accomplished by incorporating in the EMPC an economics-based cost function and Control Lyapunov-Barrier Function (CLBF)-based constraints that ensure that the closed-loop state does not enter unsafe sets and remains within a well-characterized set in the system state-space. The new class of CLBF-EMPC systems is demonstrated using a nonlinear chemical process example.

Handling Process Safety and Stochastic Uncertainty in Economic Model Predictive Control

Abstract Process operational safety plays an important role in designing control systems for chemical processes. Motivated by this, in this work, we develop a process Safeness Index-based economic model predictive control system for a broad class of stochastic nonlinear systems with input constraints. A stochastic Lyapunov-based controller is first utilized to characterize a region of the state-space surrounding the origin starting from which the origin is rendered asymptotically stable in probability. Using this stability region characterization and a process Safeness Index function which characterizes the region in state-space in which it is safe to operate the process, an economic model predictive control system is then developed using Lyapunov-based constraints to ensure economic optimality, process operational safety and closed-loop stability in probability. A chemical process example is used to demonstrate the applicability and effectiveness of the proposed approach.

Nonlinear Aeroelastic Control of Very Flexible Aircraft Using Model Updating

A nonlinear modeling strategy is introduced to drive online optimization-based controllers for trajectory tracking and stabilization of very flexible aircraft. This is achieved thanks to a compact residualized modal projection of the aeroelastic equations of motion, including large (geometrically nonlinear) wing deflections, that is used to describe the vehicle dynamics. This study shows that a model-predictive control system can then be built with an internal model based on successive linearization of the equations of motion around the instantaneous vehicle geometry. Significant improvements in stability against large disturbances and maneuverability are demonstrated on numerical simulations of a very flexible flying wing when geometrically nonlinear effects are included in the internal model of the controller.

Model predictive control of batch processes based on two-dimensional integration frame

A novel integrated model predictive control (MPC) strategy for batch processes is proposed in this paper. Both batch-axis and time-axis information are integrated into a two-dimensional control frame. The control law is obtained through the solution of a MPC optimization with time-varying prediction horizon, which leads to superior tracking performance and robustness against disturbance and uncertainty. Moreover, both model identification and dynamic R-parameter are employed to compensate the model-plant mismatch and make zero-error tracking possible. Next, the convergence analysis and tracking performance of the proposed integrated model predictive learning control system are described and proved strictly. Lastly, the effectiveness of the proposed method is verified by an example.

Model predictive controller-based multi-model control system for longitudinal stability of distributed drive electric vehicle

Distributed drive electric vehicle(DDEV) has been widely researched recently, its longitudinal stability is a very important research topic. Conventional wheel slip ratio control strategies are usually designed for one special operating mode and the optimal performance cannot be obtained as DDEV works under various operating modes. In this paper, a novel model predictive controller-based multi-model control system (MPC-MMCS) is proposed to solve the longitudinal stability problem of DDEV. Firstly, the operation state of DDEV is summarized as three kinds of typical operating modes. A submodel set is established to accurately represent the state value of the corresponding operating mode. Secondly, the matching degree between the state of actual DDEV and each submodel is analyzed. The matching degree is expressed as the weight coefficient and calculated by a modified recursive Bayes theorem. Thirdly, a nonlinear MPC is designed to achieve the optimal wheel slip ratio for each submodel. The optimal design of MPC is realized by parallel chaos optimization algorithm(PCOA)with computational accuracy and efficiency. Finally, the control output of MPC-MMCS is computed by the weighted output of each MPC to achieve smooth switching between operating modes. The proposed MPC-MMCS is evaluated on eight degrees of freedom(8DOF)DDEV model simulation platform and simulation results of different condition show the benefits of the proposed control system.

Model Deficiency Diagnosis and Improvement via Model Residual Assessment in Model Predictive Control

To reduce the effort and cost of model maintenance in model predictive control (MPC) systems, this paper explored a model deficiency diagnosis and improvement method by the assessment of model residual and optimization of disturbance model. A model quality index (MQI) method was first presented to evaluate the model performance with the routine input and output process data. Based on MQI, a leave-one-out method was proposed to further assess the performances of submodels in multi-input–multi-output (MIMO) MPC processes. The root deficient submodel could be diagnosed based on a comparison of the overall MQI with the submodel index by moving it to the disturbance channel. Further, a model performance improvement method through the upgraded optimal disturbance model was proposed to enhance the model performance without altering the control model. The experiment results on the Wood–Berry distillation column process and an industrial process indicated the validity of the proposed model diagnosis and improvemen...