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A Quantum Computing Based Numerical Method for Solving Mixed-Integer Optimal Control Problems

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Journal of Systems Science & Complexity 2021年第6期34卷 2428-2469页

作者： liU Zhe li shurong School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China

Mixed-integer optimal control problems(MIOCPs) usually play important roles in many real-world engineering applications. However, the MIOCP is a typical NP-hard problem with considerable computational complexity, resulting in slow convergence or premature convergence by most current heuristic optimization algorithms. Accordingly, this study proposes a new and effective hybrid algorithm based on quantum computing theory to solve the MIOCP. The algorithm consists of two parts:(i) Quantum Annealing(QA) specializes in solving integer optimization with high efficiency owing to the unique annealing process based on quantum tunneling, and(ii) Double-Elite Quantum Ant Colony Algorithm(DEQACA) which adopts double-elite coevolutionary mechanism to enhance global searching is developed for the optimization of continuous decisions. The hybrid QA/DEQACA algorithm integrates the strengths of such algorithms to better balance the exploration and exploitation abilities. The overall evolution performs to seek out the optimal mixed-integer decisions by interactive parallel computing of the QA and the DEQACA. Simulation results on benchmark functions and practical engineering optimization problems verify that the proposed numerical method is more excel at achieving promising results than other two state-of-the-art heuristics.

关键词： Double-elite coevolution interactive parallel computing mixed-integer optimal control problem quantum annealing quantum ant colony algorithm

ON THE COLliSION DETECTION PROBLEM OF TWO MOVING OBJECTS DESCRIBED BY ALGEBRAIC SETS

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Journal of Systems Science & Complexity 2007年第1期20卷 108-118页

作者： shurong li Chunwang HOU College of Information and Controlling Engineering University of Petroleum (East China) Dongying 257061 China.

In this paper, the collision problem of two moving objects is investigated. The objects are described by two algebraic sets （ellipses or circles in the paper）. The collision problem discussed involves both static and dynamic case. The static case is that each object moves with known velocity. We use nonlinear programming to decide whether the objects collide. The dynamic case is that each object is controlled by a constraint external force which can be regulated online. For the dynamic case, the collision problem can be modelled as a Minmax problem which can be solved by using differential games. If collision occurs, the time and place of the first collision are given. The moving trajectories are provided in the paper.

关键词： Differential games mathematical mechanization moving object collision optimization.

Optimization for ASP flooding based on adaptive rationalized Haar function approximation

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Chinese Journal of Chemical Engineering 2018年第8期26卷 1758-1765页

作者： Yulei Ge shurong li Xiaodong Zhang College of Information and Control Engineering China University of Petroleum (East China) Automation School Beijing University of Posts and Telecommunications

This paper presents an adaptive rationalized Haar function approximation method to obtain the optimal injection strategy for alkali-surfactant-polymer(ASP) flooding. In this process, the non-uniform control vector parameterization is introduced to convert original problem into a multistage optimization problem, in which a new normalized time variable is adopted on the combination of the subinterval length. Then the rationalized Haar function approximation method, in which an auxiliary function is introduced to dispose path constraints, is used to transform the multistage problem into a nonlinear programming. Furthermore, an adaptive strategy proposed on the basis of errors is adopted to regulate the order of Haar function vectors. Finally, the nonlinear programming for ASP flooding is solved by sequential quadratic programming. To illustrate the performance of proposed method,the experimental comparison method and control vector parameterization(CVP) method are introduced to optimize the original problem directly. By contrastive analysis of results, the accuracy and efficiency of proposed method are confirmed.

关键词： Alkali-surfactant-polymer flooding Optimization Enhanced oil recovery Mathematical modeling Rationalized Haar function approximation Adaptive strategy

A Novel Interacting Multiple-Model Method and Its Application to Moisture Content Prediction of ASP Flooding

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Computer Modeling in Engineering & Sciences 2018年第1期114卷 95-116页

作者： shurong li Yulei Ge Renlin Zang Automation School Beijing University of Posts and TelecommunicationsBeijingChina College of Information and Control Engineering China University of Petroleum(East China)QingdaoChina

In this paper,an interacting multiple-model(IMM)method based on datadriven identification model is proposed for the prediction of nonlinear dynamic ***,two basic models are selected as combination components due to their proved *** is Gaussian process(GP)model,which can provide the predictive variance of the predicted output and only has several optimizing *** other is regularized extreme learning machine(RELM)model,which can improve the overfitting problem resulted by empirical risk minimization principle and enhances the overall generalization *** both of the models are updated continually using meaningful new data selected by data selection ***,recursive methods are employed in the two models to reduce the computational burden caused by continuous ***,the two models are combined in IMM algorithm to realize the hybrid prediction,which can avoid the error accumulation in the single-model *** order to verify the performance,the proposed method is applied to the prediction of moisture content of alkali-surfactant-polymer(ASP)*** simulation results show that the proposed model can match the process very *** IMM algorithm can outperform its components and provide a nice improvement in accuracy and robustness.

关键词： Interacting multiple model regularized extreme learning machine gaussian process moisture content of ASP flooding

Optimization method for solving bang-bang and singular control problems

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控制理论与应用（英文版） 2012年第4期10卷 559-564页

作者： shurong li Ruiyan ZHAO Qiang ZHANG College of Information and Control Engineering China University of Petroleum

In this paper we study optimal control problems with the control variable appearing linearly. A novel method for optimization with respect to the switching times of controls containing both bang-bang and singular arcs is presented. This method transforms the control problem into a finite-dimensional optimization problem by reformulating the control problem as a multi-stage optimization problem. The optimal control problem is partitioned as several stages, with each stage corresponding to a particular control arc. A control vector parameterization approach is applied to convert the control problem to a static nonlinear programming （NLP） problem. The control profiles and stage lengths act as decision variables. Based on the Pontryagin maximal principle, a multi-stage adjoint system is constructed to calculate the gradients required by the NLP solvers. Two examples are studied to demonstrate the effectiveness of this strategy.

关键词： Bang-bang control Singular control Multi-stage problem Nonlinear programming problem

A Possibilistic Programming Approach Based on Trapezoidal Possibility Distribution and Its Application in Oil Field Exploitation 36

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A Possibilistic Programming Approach Based on Trapezoidal Po...

Robust Sliding-Mode Position/Force Set-Point Control of Underactuated Constrained manipulator 36

第36届中国控制会议

作者： Zhe liu shurong li Yulei Ge College of Information and Control Engineering China University of Petroleum（East China）

ISBN: (纸本)9781538629185

Against a low accuracy for people to solve fuzzy assignment problem by using possibilistic programming approach,an improved algorithm based on trapezoidal possibility distribution is given in this *** possibility distribution has comparative complex membership function,so we can process actual decision information better and reflect decision maker’s subjectivity much *** the improved possibilistic programming approach is used to solve a fuzzy multi-objective assignment problem in oil field exploitation,which contains imprecise objective functions and fuzzy relation ***,we obtain a regular conclusion in which the decision maker can acquire different appropriate schemes by changing aspiration *** acquired optimal assignment schemes in oil field exploitation verify the feasibility and effectiveness of the improved algorithm.

关键词： trapezoidal fuzzy numbers oil field exploitation fuzzy mathematical programming

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Robust Sliding-Mode Position/Force Set-Point Control of Unde...

Numerical Computation of a Mixed-Integer Optimal Control Problem Based on Quantum Annealing

第36届中国控制会议

作者： Zhulong Shao shurong li Huaiqiang Yin College of Information and Control Engineering China University of Petroleum （East China）

ISBN: (纸本)9781538629185

In this paper, a robust position/force set-point control strategy based on a sliding-mode is designed for constrained underactuated manipulator. At first, the reduced underactuated position/force dynamic model is obtained by considering a coordination transformation. Then, a controller based on a sliding-mode is designed to drive the closed-loop system states to reach the adopted sliding model surface. Considering the parametric uncertainties of the manipulator, a robust control strategy is applied to bound compensation. Furthermore, we can obtain a global asymptotic stability result for the positions error in the Lyapunov sense and the error of constraint force is bounded with a controllable bound. Finally, the simulation results of a three-DOF manipulator system illustrate the validity and the feasibility of the proposed method in this paper.

关键词： Underactuated manipulator Position/Force control Sliding-Mode control

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Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 623-629页

作者： liU Zhe li shurong GE Yulei Automation School Beijing University of Posts and TelecommunicationsBeijing 100876China Qingdao Topscomm Communication Co. Ltd..Qingdao 266024ShandongChina

It is extremely challenging to solve the mixed-integer optimal control problems(MIOCPs)due to the complex computation in solving the integer decision *** paper presents a new method based on quantum annealing(QA)to solve *** QA is a metaheuristic which applies quantum tunneling in the annealing *** has a faster convergence speed in optimal-searching and is less likely to run into local ***,QA is applied to deal with this kind of optimization ***,MIOCP is transformed into a mixed-integer nonlinear programming(MINLP).Then,a method based on QA is adopted to solve the MINLP and acquire the optimal *** last,two benchmark examples including Lotka-Volterra type fishing problem and distillation column are presented and *** effectiveness of the metliodology is verified by the acquired optimal schemes.

关键词： mixed-integer optimal control quantum annealing distillation column

Adaptive Third-Order Fixed Time Sliding Mode Control with Chattering Free for Mobile Loading Arm Systems

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Journal of Systems Science & Complexity 2024年

作者： WANG Baigeng li shurong liU Zhe School of Artificial Intelligence Beijing University of Posts and Telecommunications State Key Laboratory of Industrial Control Technology Zhejiang University

The mobile loading arm(MLA) system is one of the most important mechanical equipments in the petrochemical industry consisting of rigid pipelines and rotating elbows. This paper researches the problems of dynamical modelling and tracking control. At first, an MLA dynamic model is built by using Euler-Lagrange function. And then, an adaptive third-order fixed time sliding mode(FDTSM) tracking controller with chattering free is represented, which enables the MLA system to dock with the vehicle tank mouth. In this paper, a new double-layer third-order fixed time sliding mode controller is first proposed to address the tracking problem in model-based MLA systems with known parameters. Moreover, considering the presence of modeling uncertainty and external disturbances,a nerual network adaptive FDTSM controller is designed to ensure that the close-loop system state tracking error converges to a bounded region around zero within a fixed time. Further, the stability of the close-loop system is proven by using the Lyapunov stability theorem. Simulation results illustrate the validaty of the controller.

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