Article
Automation & Control Systems
A. H. Tahoun, M. Arafa
Summary: This paper addresses the leader-follower tracking problem in multi-agent networks with unknown uncertainties. By designing distributed adaptive observers and controllers, the tracking path can be estimated and good tracking performance is achieved.
Article
Computer Science, Artificial Intelligence
Juan Zhang, Huaguang Zhang, Wei Wang, Yanhong Luo, Gang Wang
Summary: This article presents a new approach to solve the leader-follower consensus problem in multiagent systems. The method designs observers and compensators based on the output information, which is scalable for large-scale systems and reduces the dimension of information transmission.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Qing Wang, Xiwang Dong, Bohui Wang, Yongzhao Hua, Zhang Ren
Summary: This paper investigates the problem of robust H-infinity fault-tolerant output time-varying formation tracking for a heterogeneous nonlinear multi-agent system. Distributed finite-time observers are proposed to derive the leader's information for each follower, and a novel adaptive H-infinity fault-tolerant output TVFT controller is developed. The robust H-infinity output TVFT criterion is obtained using Lyapunov theory and the linear matrix inequality technique, and a simulation example is provided to demonstrate the theoretical results.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Jing Xie, Ping Sun, Dong Yang
Summary: An adaptive fuzzy-based control strategy is proposed to handle anti-disturbance issue in a class of switched nonlinear systems with unmatched external disturbances and unknown backlash-like hysteresis. The technique aims to achieve output tracking performance and disturbance attenuation. It utilizes adaptive fuzzy disturbance observer and composite switching adaptive anti-disturbance controller, validated through a mass-spring-damper system example.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Engineering, Mechanical
Fernando Viadero-Monasterio, B. L. Boada, M. J. L. Boada, V Diaz
Summary: This paper investigates the event-triggered control problem for an active suspension system with a networked communication architecture and Dynamic Output Feedback Controller, considering the possibility of actuator failure. A Dynamic Output Feedback Controller is synthesized under LMI restrictions to ensure system stability with the H-infinity criteria, using a novel polytopic model to approach the plant function. The control performance characteristics for vibration suppression under various road conditions are evaluated to prove practical feasibility.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Chuan Yan, Tao Yang, Huazhen Fang
Summary: This article investigates control design for high-order leader-follower multi-agent systems where only the first state of an agent is measured. By developing distributed observers, followers are able to reconstruct the unmeasured or unknown quantities about themselves and the leader, and observer-based tracking control approaches are built on this basis. The proposed approaches' convergence properties are analyzed and their performance is validated through simulation.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Yanfang Lei, Junmin Li
Summary: This paper focuses on stabilizing parabolic systems with time-varying delay, external disturbance, and nonlinear periodic time-varying parameter (NPTVP). A neural networks (NNs) approximator is designed using Fourier series expansion (FSE) method and NNs approximation technology to describe the uncertain dynamic term with NPTVP. Two robust adaptive neural network control (RANNC) algorithms are then designed based on adaptive control theory, NNs approximation technology, and reparameterization method to achieve asymptotic stability and prescribed adaptive H-infinity performance of disturbance attenuation. Sufficient conditions for the stability and performance requirements of the resulting closed-loop systems are derived, and simulations are carried out to verify the effectiveness of the proposed RANNC algorithms.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Shi-Lu Dai, Shude He, He Cai, Chenguang Yang
Summary: This article studies the formation tracking control problem for a group of underactuated surface vehicles and proposes a control method that guarantees connectivity and collision avoidance under limited sensing capabilities. A transverse function control approach is used to overcome difficulties, and the barrier Lyapunov function and adaptive backstepping procedure are employed to achieve boundedness and guaranteed transient performance of the closed-loop systems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Yu Liu, Lin Li
Summary: This paper addresses the leader-follower consensus problem in a multiple flexible manipulator network with actuator failures, parameter uncertainties, and unknown time-varying boundary disturbances. The objective is to develop distributed controllers using local interactive protocols that suppress the vibration of each flexible manipulator and achieve consensus on joint angle position between actual followers and the virtual leader. The adaptive neural network method and parameter estimation technique are utilized to compensate for unknown items and bounded disturbances. The Lyapunov stability theory is employed to demonstrate the uniform ultimate boundedness of followers' angle consensus errors and vibration deflections in closed-loop systems. Finally, numerical simulation results confirm the efficacy of the proposed controllers.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Mathematics, Applied
Malika Sader, Zengqiang Chen, Zhongxin Liu, Chao Deng
Summary: This paper focuses on the H-infinity consensus problem of nonlinear multi-agent systems under switching communication topologies with multiple faults. A new distributed robust fault-tolerant controller is designed to effectively solve the control problem in the presence of nonlinear functions and multiple faults, and the stability of the closed-loop system is proven.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Multidisciplinary Sciences
Randa Herzallah, Yuyang Zhou
Summary: This paper proposes a unified probabilistic control framework for stochastic systems with both control input and state time delays. The framework considers both the stochastic nature and time delays in the system dynamics, providing a comprehensive and rigorous control methodology. The effectiveness of the framework is demonstrated through numerical and real-world examples.
SCIENTIFIC REPORTS
(2022)
Article
Chemistry, Multidisciplinary
Xiaoming Xia, Zhaodi Yang, Tianxiang Yang
Summary: This paper investigates the leader-follower formation tracking control of underactuated surface vessels (USVs) with input saturation. An event-triggered extended-state observer (ETESO) is used to recover the velocity, yaw rate and uncertainties. An estimator is then used to estimate the velocity of the leader. An event-triggered controller (ETC) is constructed based on the estimator, observer and extra variables to solve the problems of underactuation and input saturation. Simulations demonstrate the effectiveness of the proposed approach.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Yuqiang Jiang, Ben Niu, Guangdeng Zong, Xudong Zhao, Ping Zhao
Summary: A distributed adaptive secure control algorithm is proposed for considering sensor attacks and actuator faults in asynchronous switching nonlinear multi-agent systems. The algorithm eliminates the effects of attack signals, actuator faults, and uncertainties without knowing the sign of control gain functions. An improved coordinate transformation is constructed using the post-attack system states, and a backstepping method is developed to solve the consensus tracking control problem. The stability of all asynchronous switching subsystems is ensured by designing a common Lyapunov function.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Zhifeng Gao, Kaihui Shen, Xianqing Sha, Jiaqi He
Summary: In this study, a novel decentralized adaptive proportional-integral (PI) fault tolerant tracking control strategy is proposed for a class of strong interconnection nonlinear systems with unmodeled dynamics and actuator faults. The adverse effects generated by unmodeled dynamics could be eliminated by introducing a dynamic signal and the multiplicative actuator faults could be dealt with by using adaptive control technique. The decentralized adaptive PI fault tolerant tracking controller including a switching compensation mechanism is designed for the considered interconnected systems, such that all the signals in the closed-loop systems are uniformly ultimately bounded (UUB) and the tracking errors converge to a small residual set.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2023)
Article
Automation & Control Systems
Jing-Wen Xing, Chen Peng, Zhiru Cao
Summary: This paper proposes an event-triggered adaptive fuzzy tracking control method for high-order stochastic nonlinear systems. The fuzzy logic systems (FLSs) approximation approach is extended to handle the unknown nonlinear uncertainties. A novel high-order adaptive fuzzy tracking controller is presented using a backstepping approach and event-triggering mechanism, which reduces unnecessary waste of computation and communication resources. The effectiveness of the proposed control method is demonstrated through a numerical example.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Guangzhu Peng, C. L. Philip Chen, Chenguang Yang
Summary: This article proposes a robust control scheme for robots to achieve optimal performance in interacting with external forces. The environmental dynamics are defined as a linear model, and the interaction performance is evaluated using a cost function composed of trajectory errors and force regulation. The proposed method minimizes the cost function and achieves optimal interaction performance through reference adaptation based on admittance control. To make the trajectory tracking controller robust to unknown disturbance of internal system dynamics, an auxiliary system is defined and an approximation optimal controller is designed. Experiments on the Baxter robot are conducted to validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Huifang Huang, Zhi Liu, C. L. Philip Chen, Yun Zhang
Summary: This paper proposes an active broad learning system approach for hyperspectral image (HSI) classification. It extracts spectral and spatial features of the image using principal component analysis and local binary patterns respectively, and utilizes vector fusion as input for the broad learning system. The method incorporates active learning to select high-quality training samples, reducing the number of samples used and the cost of labeling. The use of incremental learning in broad learning also improves training time and classification accuracy. The proposed algorithm outperforms other state-of-the-art algorithms on three HSI datasets.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Jia Wang, Yang Xiao, C. L. Philip Chen, Tieshan Li
Summary: Global positioning system (GPS) plays a crucial role in unmanned surface ship's path planning. This article proposes a Jamming Aware Artificial Potential Field (JA-APF) method, which detects GPS jamming by monitoring the received signal strength and calculating the packet error rate, and calculates the position range of the jamming source. The JA-APF method then replans the path based on attraction forces around the goal, repulsion forces around obstacles, and repulsion forces around multiple GPS jammers. Simulation results show that the JA-APF method effectively mitigates the impact of GPS jamming on path planning and quickly recovers.
IEEE SYSTEMS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Dengxiu Yu, Ming Yang, Yan-Jun Liu, Zhen Wang, C. L. Philip Chen
Summary: This article investigates the problem of adaptive fuzzy tracking control for uncertain nonlinear systems with multiple actuators and sensors faults. The challenge of designing the control scheme arises from the fact that all states of the system cannot be accurately measured due to the presence of multiple sensor faults. Additionally, the design of the controller is complicated by multiple actuator faults and external disturbance. To address these issues, different adaptive update laws are designed to mitigate the effects of unknown actuator faults, sensor faults, and external disturbance. The actual states are estimated by combining sensor outputs with adaptive parameters, and the unknown nonlinear functions are approximated using a combination of fuzzy logic systems and state estimation. A novel adaptive fuzzy tracking control algorithm is then developed using the backstepping method. The proposed fault-tolerant control algorithm ensures bounded signals of the system despite the occurrence of multiple faults by employing the Lyapunov function. The effectiveness of the novel algorithm is verified by comparing its control performance to that of another algorithm.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hanzhen Xiao, C. L. Philip Chen, Guanyu Lai, Dengxiu Yu, Yun Zhang
Summary: This paper presents an integrated leader-follower consensus formation framework using neural-network-optimized distributed model predictive control (NNODMPC) strategy for constructing a distributed consensus formation scheme for two-wheel mobile robots with directed communication topology and nonholonomic constraints. The framework integrates a consensus trajectories planning subsystem (CTPS) and a consensus tracking subsystem. The NNODMPC based protocol is applied to control these two subsystems simultaneously. The optimal solution of the integrated system is obtained by a primal-dual neural networks (PDNN) QP optimizer. Experimental results verify the effectiveness of the proposed strategy.
Article
Automation & Control Systems
Kaixin Lu, Zhi Liu, Yaonan Wang, C. L. Philip Chen, Yun Zhang
Summary: This article proposes a state-dependent adaptive neural design method to overcome the limitations of existing adaptive neural control methods for non-linear multiagent systems. The proposed method ensures stability and convergence of synchronization errors through a compensation approach and multiple Lyapunov functions method. The experimental results demonstrate the improvement in transient performance and asymptotic convergence of synchronization errors.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Ci Chen, Lihua Xie, Yi Jiang, Kan Xie, Shengli Xie
Summary: In this article, the optimal output tracking problem for linear discrete-time systems with unknown dynamics is investigated using reinforcement learning (RL) and robust output regulation theory. Different from most existing works, which depend on the state of the system, this problem only utilizes the outputs of the reference system and the controlled system. The proposed off-policy RL algorithm allows for solving the output tracking problem using only measured output data and the reference output, without requiring complete and accurate system dynamics knowledge.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Guoxing Wen, C. L. Philip Chen
Summary: This article proposes an optimized leader-following consensus control scheme for nonlinear multi-agent systems. By learning from the optimized backstepping technique, the virtual and actual controls of backstepping are designed to be the optimized solution of corresponding subsystems, resulting in an overall optimized control. The proposed scheme uses neural network approximation-based reinforcement learning to achieve this optimized control. Compared to existing RL-based optimal controls, the proposed scheme has a simpler algorithm and can release two general conditions, known dynamic and persistence excitation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yang-Yang Qian, Mushuang Liu, Yan Wan, Frank L. Lewis, Ali Davoudi
Summary: This article investigates differential graphical games for linear multiagent systems with a leader on fixed communication graphs. A distributed adaptive Nash equilibrium solution is proposed, which is not only Nash but also fully distributed in the sense that each agent only uses local information of its own and its immediate neighbors. The solution achieves both asymptotic stability and global Nash equilibrium properties.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Ci Chen, Frank L. Lewis, Kan Xie, Yi Lyu, Shengli Xie
Summary: This paper proposes an output-feedback policy learning algorithm that utilizes input-output system data for distributed robust optimal synchronization of heterogeneous multi-agent systems. The algorithm models the interactions among agents as a zero-sum game and enables learning and control using only local system data and distributed output data. The paper demonstrates the effectiveness of the proposed algorithm through convergence and stability proof, showing that policy learning is assured based on input-output data criteria.
Article
Computer Science, Artificial Intelligence
Zhifeng Xie, Wenling Zhang, Bin Sheng, Ping Li, C. L. Philip Chen
Summary: In this article, a new model, the broad attentive graph fusion network (BaGFN), is proposed to better model high-order feature interactions. The model incorporates an attentive graph fusion module and a broad attentive cross module to enhance feature representation under graph structure and refine feature interactions at a bitwise level.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuhong Xu, Zhiwen Yu, Wenming Cao, C. L. Philip Chen
Summary: Ensemble learning improves classifiers' performance in dealing with high-dimensional small-size data. However, existing ensemble methods have limitations in handling noise and redundant features, insufficient sample space description, information loss, and defective feature space. To overcome these limitations, a new classifier ensemble method called CESE based on subspace enhancement is proposed. It effectively implements feature selection and transformation with a superior subspace enhancement scheme (SSE) and generates diverse feature subspaces. By using a mixed space enhancement process (MSE) and various feature fusion and combination strategies, CESE outperforms different mainstream integrated systems on 33 high-dimensional datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Review
Automation & Control Systems
Ningwei Zhang, Yuli Zhang, Shiji Song, C. L. Philip Chen
Summary: Robust optimization is an effective method for dealing with unanticipated events in uncertain and risky environments. This paper provides a systematic review of two emerging robust machine scheduling approaches: robust machine scheduling (R-MS) and distributionally robust machine scheduling (DR-MS), which offer tractable formulations and analytical results for machine scheduling problems under uncertainty. The paper analyzes the literature related to R-MS/DR-MS problems and classifies them based on uncertain factors, uncertainty descriptions, robustness criteria, machine environments, and solution methods. It also discusses the existing R-MS/DR-MS models in different machine environments and the solution methods for these models. Finally, it presents future research opportunities in green machine scheduling problems and machine learning-enabled algorithms.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Chengjie Huang, Zhi Liu, C. L. Philip Chen, Yun Zhang
Summary: In this article, the problem of adaptive fixed-time tracking control for multiagent systems (MASs) with mismatched uncertainty is considered. A new adaptive consensus control criterion is proposed, which includes the design of Lyapunov functions and tuning functions. The use of radial basis function neural networks and direct adaptive strategy improves the stability and performance of the MASs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jintao Huang, Chi-Man Vong, C. L. Philip Chen, Yimin Zhou
Summary: This paper proposes a novel multi-label classifier based on a broad learning system (BLS-MLL). It improves the classification performance and training efficiency of large-scale multi-label learning by introducing kernel-based feature reduction and correlation-based label thresholding.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(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.