Article
Automation & Control Systems
Jie Wu, Jingyuan Xue, Fei Liu
Summary: This work develops a new robust multi-objective model predictive control strategy for constrained non-linear systems with bounded disturbances. The conflict between multiple objectives is reconciled by minimizing the distance of the cost function vector to the vector of independently minimised objectives obtained by solving a set of steady-state optimisation problems. A stability constraint updated online by solving an auxiliary optimization problem is established to ensure the robust stability of the system under MoMPC.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Engineering, Aerospace
Yankai Li, Yulong Huang, Han Liu, Dongping Li
Summary: In this paper, a full state-constrained anti-disturbance dynamic surface control method is proposed for six-degree-of-freedom unmanned helicopter systems under full state constraints and disturbances. The input-output feedback linearization method is used to transform the underactuated nonlinear systems. The flight controller is constructed by combining state constraint control and dynamic surface control technologies using nonlinear disturbance-observer-based control, backstepping control, and Barrier Lyapunov function methods. Lyapunov stability theory is adopted to analyze the closed-loop tracking error systems. A simulation in MATLAB/Simulink verifies the effectiveness of the designed controller.
Article
Computer Science, Artificial Intelligence
Xiaohui Yan, Mou Chen, Gang Feng, Qingxian Wu, Shuyi Shao
Summary: This article proposes an adaptive fuzzy control scheme based on HODO and DSC techniques for nonlinear systems with input saturation and external disturbances. By utilizing backstepping method and Lyapunov analysis, it is proven that all signals in the system are bounded and the tracking error converges to a compact set.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Pengfei Li, Tao Wang, Yu Kang, Kun Li, Yun-Bo Zhao
Summary: This paper investigates event-based model predictive control (MPC) for constrained nonlinear systems with dynamic disturbance. Two event-triggered disturbance prediction MPC (DPMPC) schemes, which explicitly consider the disturbance dynamics, are proposed. In both schemes, the optimal control problems are solved only at triggering instants, reducing computational resource consumption. The effectiveness of the two schemes is demonstrated through a simulation example.
Article
Automation & Control Systems
Xiaowen Qi, Shaoyuan Li
Summary: In this paper, a novel economic model predictive control (EMPC) strategy is proposed to control a constrained multi-variable process system with varying economic performance criteria under soft constraints. By optimizing a modified economic performance index and adding a contractive constraint for stability, the economic performance and feasibility of the entire plant operation can be improved.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Xing Fang, Wen-Hua Chen, Fei Liu
Summary: This article proposes an integrated model predictive control (MPC) framework with disturbance preview information for nonlinear systems. The proposed framework can predict disturbances within the prediction horizon and integrate them into online optimization, which improves stability and feasibility.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Qun Lu, Jian Chen, Qianjin Wang, Dan Zhang, Mingxuan Sun, Chun-Yi Su
Summary: This paper addresses the problem of practical fixed-time trajectory tracking for wheeled mobile robots subject to kinematic disturbances and input saturation. A two-input two-output interference system is established to transform the WMR model under kinematic disturbances considering the under-actuated characteristics. The paper proposes a fixed-time extended state observer to estimate the lumped disturbances and develops a practical fixed-time output feedback control law for trajectory tracking. The effectiveness of the proposed approach is demonstrated through simulation results.
Article
Computer Science, Artificial Intelligence
Ziming Wang, Hui Wang, Xin Wang, Ning Pang, Quan Shi
Summary: The paper proposes a novel adaptive nonlinear observer to deal with uncertain nonlinear systems subject to constraints and disturbance. By combining neural networks and nonlinear mapping mechanism, the system can be transformed into an unconstrained form and effectively handle system uncertainties. An adaptive tracking control approach is formulated using backstepping techniques and event-sampled scheme. The proposed algorithm applies Lyapunov functions, RBF NNs theory, and inequality techniques to address the adaptive control problem. The validity of the approach is illustrated through a numerical example.
COGNITIVE COMPUTATION
(2023)
Article
Automation & Control Systems
Yeqin Wang, Beibei Ren, Qing-Chang Zhong
Summary: The article introduces a bounded UDE-based controller to handle systems with uncertainties, disturbances, and input constraints. By introducing an additional time-varying variable, the issue of integral windup is naturally avoided, ensuring that the controller output always satisfies the constraints.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Automation & Control Systems
Alex dos Reis de Souza, Denis Efimov, Tarek Raissi, Xubin Ping
Summary: This work addresses the problem of robust output feedback model predictive control for discrete-time, constrained linear systems corrupted by (bounded) state and measurement disturbances. By using the available information on measurements and uncertainty bounds, interval observer and predictor with guaranteed performance are incorporated into the classic MPC scheme to stabilize the system while robustly respecting the imposed constraints on state and control. This approach offers advantages such as enlarged feasible regions for the optimal control problem, low computational burden, and ease of design.
Article
Automation & Control Systems
Youwu Du, Weihua Cao, Jinhua She, Min Wu, Mingxing Fang, Seiichi Kawata
Summary: This article presents an observer-and-predictor-based method to reject an unknown exogenous disturbance for an input-delay system, demonstrating its validity and superiority through two case studies.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Shang Shi, Shengyuan Xu, Jason Gu, Zhengqiang Zhang
Summary: A novel robust predictive scheme is proposed in this article for exact state prediction in finite-time with respect to system disturbances. By combining the super-twisting technique, a new predictor-based output-feedback super-twisting controller is designed to compensate input delay and eliminate the effect of unmatched disturbances completely, showing effectiveness in simulation comparison.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Mathematics
Masoud Chatavi, Mai The Vu, Saleh Mobayen, Afef Fekih
Summary: This paper proposes a nonlinear state feedback controller based on linear matrix inequality (LMI) for handling nonlinear systems with parametric uncertainties and external disturbances. The proposed controller aims to ensure system stability and performance in the presence of uncertainties and time-dependent disturbances. Through mathematical derivation and simulation experiments, the effectiveness and performance of the controller are verified.
Article
Automation & Control Systems
Yuanqing Qin, Yue Zhao, Kaixing Huang, Yu-Chu Tian, Chunjie Zhou
Summary: Modern networked control systems, known as cyber-physical systems (CPSs), are tightly integrated with the physical world and human intervention, serving as the basis for future smart services. However, they are also susceptible to cyber-attacks due to increasing connectivity to the Internet. This paper investigates secure model predictive control (MPC) for constrained CPSs subject to actuator attacks, utilizing invariant set theory to construct a Luenberger observer for error-bounded state estimation and designing a robust output feedback MPC controller for systems under attack. Stability conditions for the proposed MPC controller are theoretically established and a numerical example is provided to demonstrate its effectiveness.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Shengli Du, Mingming Zhao, Xue-Fang Wang, Honggui Han, Junfei Qiao
Summary: In this article, a dual-mode event-triggered predictive control method is proposed for nonlinear systems with bounded disturbances. The method consists of two triggering mechanisms, namely the hybrid threshold-based event-triggered model predictive control mechanism and the event-triggered linear quadratic regulator mechanism. The feasibility and practical stability analysis of the designed strategy are presented, along with simulations to demonstrate the correctness and feasibility of the designed algorithms.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Management
Dimitris Bertsimas, Melvyn Sim, Meilin Zhang
MANAGEMENT SCIENCE
(2019)
Article
Computer Science, Software Engineering
Yu Zhang, Roberto Baldacci, Melvyn Sim, Jiafu Tang
MATHEMATICAL PROGRAMMING
(2019)
Article
Computer Science, Software Engineering
Wenzhuo Yang, Melvyn Sim, Huan Xu
MATHEMATICAL PROGRAMMING
(2020)
Article
Management
Shuangchi He, Melvyn Sim, Meilin Zhang
MANAGEMENT SCIENCE
(2019)
Article
Management
Zhi Chen, Melvyn Sim, Huan Xu
OPERATIONS RESEARCH
(2019)
Article
Management
Le Thi Khanh Hien, Melvyn Sim, Huan Xu
OPERATIONS RESEARCH
(2020)
Article
Management
Zhi Chen, Melvyn Sim, Peng Xiong
MANAGEMENT SCIENCE
(2020)
Article
Management
Taozeng Zhu, Jingui Xie, Melvyn Sim
Summary: The study introduces a joint estimation and robustness optimization framework to address the impact of estimation uncertainty in optimization problems. By incorporating both the parameter estimation process and the optimization problem seamlessly, the framework aims to obtain solutions that are immune to parameter perturbations. The size of the uncertainty set, based on the accuracy of parameter estimation from data using specific procedures, is maximized to achieve this goal.
MANAGEMENT SCIENCE
(2022)
Article
Management
Yu Zhang, Zhenzhen Zhang, Andrew Lim, Melvyn Sim
Summary: The study proposes a distributionally robust optimization model for a vehicle routing problem with time windows, utilizing a Wasserstein distance-based ambiguity set to minimize delays while limiting travel costs. The proposed solution significantly improves on-time arrival performance with only modest increases in expenditures and outperforms traditional decision criteria in out-of-sample simulations.
OPERATIONS RESEARCH
(2021)
Article
Management
Daniel Zhuoyu Long, Melvyn Sim, Minglong Zhou
Summary: We present a general framework for robust satisficing that favors solutions which can achieve an acceptable target even when the actual probability distribution deviates from the empirical distribution. By balancing the model's ability to withstand uncertainty, the decision maker specifies an acceptable target or loss of optimality compared to the empirical optimization model. Through numerical studies, it is shown that solutions to the robust satisficing models are more effective in improving out-of-sample performance.
OPERATIONS RESEARCH
(2023)
Article
Management
Patrick Jaillet, Gar Goei Loke, Melvyn Sim
Summary: This study introduces a new approach to address the workforce planning issues of hiring, dismissing, and promoting while ensuring compliance with organizational constraints. By considering factors such as employees' time-in-grade, a new model is proposed to meet constraints under uncertainty, providing insights into HR management.
OPERATIONS RESEARCH
(2022)
Article
Management
Jingui Xie, Gar Goei Loke, Melvyn Sim, Shao Wei Lam
Summary: Bed shortages in hospitals have negative consequences on patient satisfaction and medical outcomes. Traditional metrics such as bed occupancy rates (BORs) are insufficient in capturing the risk of bed shortages. We propose the bed shortage index (BSI) to capture more facets of bed shortage risk. Our metric is based on the riskiness index by Aumann and Serrano and can be easily computed without additional assumptions. We also propose optimization models to plan for bed capacity using this metric.
OPERATIONS RESEARCH
(2023)
Article
Management
Georgia Perakis, Melvyn Sim, Qinshen Tang, Peng Xiong
Summary: We propose a new distributionally robust optimization model for a two-period, multiitem joint pricing and production problem, which utilizes historical demand and side information for demand prediction. By introducing a partitioned-moment-based ambiguity set, we characterize the residuals of an additive demand model and determine the evolution of the second-period demand from the first-period information in a data-driven setting. We investigate the problem by proposing a cluster-adapted markdown policy and affine recourse adaptation, reformulating it as a mixed-integer linear optimization problem and solving it to optimality using commercial solvers. We also extend our framework to ensemble methods using ambiguity sets constructed from different clustering approaches. Numerical experiments and a case study demonstrate the benefits of the cluster-adapted markdown policy and the partitioned moment-based ambiguity set in improving the mean profit over empirical models in most out-of-sample tests.
MANAGEMENT SCIENCE
(2023)
Article
Engineering, Manufacturing
Minglong Zhou, Melvyn Sim, Shao-Wei Lam
Summary: The study addresses the issue of advance scheduling of ward admission requests in a public hospital and proposes a resource satisficing framework to reduce the risks of resource overutilization.
PRODUCTION AND OPERATIONS MANAGEMENT
(2022)
Article
Management
Georgia Perakis, Melvyn Sim, Qinshen Tang, Peng Xiong
Summary: We propose a new distributionally robust optimization model for a two-period, multiitem joint pricing and production problem, which can be implemented in a data-driven setting using historical demand and side information. We introduce a new partitioned-moment-based ambiguity set to characterize the residuals of an additive demand model and investigate the problem by proposing a cluster-adapted markdown policy and an affine recourse adaptation. Experimental results demonstrate the effectiveness of the cluster-adapted markdown policy and the partitioned moment-based ambiguity set in improving the mean profit.
MANAGEMENT SCIENCE
(2023)
Article
Automation & Control Systems
Xiaoyu Luo, Chengcheng Zhao, Chongrong Fang, Jianping He
Summary: This paper investigates the problem of false data injection attacks in multi-agent dynamical systems and proposes FDI attack set selection algorithms to maximize the convergence error by finding the optimal subset of compromised agents.
Article
Automation & Control Systems
Nitin K. Singh, Abhisek K. Behera
Summary: In this paper, a twisting observer is proposed for robustly estimating the states of a second-order uncertain system. The observer approximates the unknown sign term for the non-measurable state with a delayed output-based switching function, and achieves the desired steady-state accuracy by controlling the delay parameter. The application of the observer to output feedback stabilization is also discussed.
Article
Automation & Control Systems
Alexander Aleksandrov
Summary: This paper investigates the absolute stability problem for positive Persidskii systems with delay, proposes a special construction method for diagonal Lyapunov-Krasovskii functionals, and derives a criterion for the existence of such functionals guaranteeing the absolute stability, as well as obtaining sufficient conditions for a family of time-delay Persidskii systems to construct a common diagonal Lyapunov-Krasovskii functional. The efficiency of the developed approaches is demonstrated through four examples.
Article
Automation & Control Systems
Noureddine Toumi, Roland Malhame, Jerome Le Ny
Summary: This paper addresses large multi-agent dynamic discrete choice problems using a linear quadratic mean field games framework. The model incorporates the features where agents have to reach a predefined set of possible destinations within a fixed time frame and running costs can become negative to simulate crowd avoidance. An upper bound on the time horizon is derived to prevent agents from escaping to infinity in finite time. The existence of a Nash equilibrium for infinite population and its epsilon-Nash property for a large but finite population are established. Simulations are conducted to explore the model behavior in various scenarios.
Article
Automation & Control Systems
Philippe Schuchert, Vaibhav Gupta, Alireza Karimi
Summary: This paper presents the design of fixed-structure controllers for the As2 and Asw synthesis problem using frequency response data. The minimization of the norm of the transfer function between the exogenous inputs and performance outputs is approximated through a convex optimization problem involving Linear Matrix Inequalities (LMIs). A general controller parametrization is used for continuous and discrete-time controllers with matrix transfer function or state-space representation. Numerical results show that the proposed data-driven method achieves performance equivalent to model-based approaches when a parametric model is available.
Correction
Automation & Control Systems
Zhijun Guo, Gang Chen
Article
Automation & Control Systems
Matteo Della Rossa, Thiago Alves Lima, Marc Jungers, Raphael M. Jungers
Summary: This paper presents new stabilizability conditions for switched linear systems with arbitrary and uncontrollable underlying switching signals. The study focuses on two specific settings: the robust case with completely unknown and unobservable active mode, and the mode-dependent case with controller depending on the current active switching mode. The technical developments are based on graph-theory tools and path-complete Lyapunov functions framework, enabling the design of robust and mode-dependent piecewise linear state-feedback controllers using directed and labeled graphs.
Article
Automation & Control Systems
Elena Petri, Romain Postoyan, Daniele Astolfi, Dragan Nesic, W. P. M. H. (Maurice) Heemels
Summary: This study investigates a scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. By designing both the observer and the local transmission policies, accurate state estimates can be obtained while only sporadically using the communication network.
Article
Automation & Control Systems
Jonas Krook, Robi Malik, Sahar Mohajerani, Martin Fabian
Summary: This paper proposes a method to synthesise controllers for cyber-physical systems subjected to disturbances, such that the controlled system satisfies specifications given as linear temporal logic formulas. The approach constructs a finite-state abstraction of the original system and synthesises a controller for the abstraction. It introduces the robust stutter bisimulation relation to account for disturbances and uncertainty, ensuring that related states have similar effects under the same controller. The paper demonstrates that the existence of a controller for the abstracted system implies the existence of a controller for the original system enforcing the linear temporal logic formula.
Article
Automation & Control Systems
Clement Chahbazian, Karim Dahia, Nicolas Merlinge, Benedicte Winter-Bonnet, Aurelien Blanc, Christian Musso
Summary: The paper derives a recursive formula of the Fisher information matrix on Lie groups and applies it to nonlinear Gaussian systems on Lie groups for testing. The proposed recursive CRLB is consistent with state-of-the-art filters and exhibits representative behavior in estimation errors. This paper provides a simple method to recursively compute the minimal variance of an estimator on matrix Lie groups, which is fundamental for implementing robust algorithms.
Article
Automation & Control Systems
Yiheng Fu, Pouria Ramazi
Summary: This study investigates the characteristics of decision fluctuations in heterogeneous populations and explores the uncertainties in imitation behavior. The findings are important for understanding the bounded rationality nature of imitation behaviors.
Article
Automation & Control Systems
Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Summary: This paper introduces a mathematical relationship between the accuracy of reduced-order linear-time invariant subsystem models and the stability and accuracy of the resulting reduced-order interconnected linear time-invariant model. This result can be used to directly translate the accuracy characteristics of the reduced-order subsystem models to the accuracy properties of the interconnected reduced-order model, or to translate accuracy requirements on the interconnected system model to accuracy requirements on subsystem models.
Article
Automation & Control Systems
Piyush Gupta, Vaibhav Srivastava
Summary: We study the optimal fidelity selection for a human operator servicing tasks in a queue, considering the trade-off between high-quality service and penalty due to increased queue length. By modeling the operator's cognitive dynamics and task fidelity, we determine the optimal policy and value function numerically, and analyze the structural properties of the optimal fidelity policy.
Article
Automation & Control Systems
Lukas Schwenkel, Alexander Hadorn, Matthias A. Mueller, Frank Allgoewer
Summary: In this work, the authors study economic model predictive control (MPC) in periodic operating conditions. They propose a method to achieve optimality by multiplying the stage cost by a linear discount factor, which is easy to implement and robust against online changes. Under certain assumptions, they prove that the resulting linearly discounted economic MPC achieves optimal asymptotic average performance and guarantees practical asymptotic stability of the optimal periodic orbit.
Article
Automation & Control Systems
Taher Ebrahim, Sankaranarayanan Subramanian, Sebastian Engell
Summary: We propose a robust nonlinear model predictive control algorithm for dynamic systems with mixed degrees of freedom. This algorithm optimizes both continuous and discrete manipulated variables, enhancing closed-loop performance. Our approach relies on a computationally efficient relaxation and integrality restoration strategy and provides sufficient conditions to establish recursive feasibility and guarantee robust closed-loop stability. The effectiveness of the approach is demonstrated through two nonlinear simulation examples.
