Article
Automation & Control Systems
Na Lin, Ronghu Chi, Yang Liu, Zhongsheng Hou, Biao Huang
Summary: In this article, a data-driven set-point tuning (DDST) approach is proposed to enhance the control performance of model-free adaptive control (MFAC). The proposed approach uses a dynamic linearization (DL) technique to realize an ideal nonlinear set-point tuning (NST) law and estimates unknown parameters in a linear data model (LDM) using a projection algorithm. Simulation results validate the theoretical findings.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Robotics
Chen Song, Darwin Lau
Summary: This paper presents a workspace-based model predictive control scheme to address the challenges in controlling cable-driven robots, such as nonlinearity, actuation redundancy, and actuation constraints. By combining online model predictive control with offline workspace analysis, a set of convex constraints can be generated for a given reference trajectory, allowing for effective motion tracking with cable force constraints satisfied even in the presence of model uncertainties.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Automation & Control Systems
Ronghu Chi, Xiaolin Guo, Na Lin, Biao Huang
Summary: This article addresses the challenges of data-driven control design in the presence of strong uncertainties, hard nonlinearities, and model dependency. It proposes a dynamic linearization (DL) method and an extended state observer (ESO) to handle an unknown nonlinear nonaffine system. The article presents a modified linear data model (mLDM) that accurately captures the input-output dynamics, including both linear parameter increments and unmodeled uncertainties and disturbances. The theoretical results are mathematically proven and validated through simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Mechanical
Xiangquan Li, Zhengguang Xu
Summary: This work presents a pattern-moving-based partial form dynamic linearization model free adaptive control scheme for unknown nonaffine nonlinear discrete-time systems, aiming to control the system outputs to belong to certain pattern classes. The bounded convergence of tracking error is demonstrated through theoretical analysis and simulation examples.
Article
Computer Science, Interdisciplinary Applications
Hugo A. Pipino, Carlos A. Cappelletti, Eduardo J. Adam
Summary: This study investigates the design of a Model Predictive Control (MPC) formulation for polytopic multi-model system representation and applies it to a continuous stirred tank reactor (CSTR) system. The proposed method uses a virtual model-process tuning variable and optimizes the Linear Time Invariant (LTI) prediction sequence based on the LTI vertices of the polytopic system. Discussions are made on the a-priori design procedure, online computational effort, and application difficulties.
COMPUTERS & CHEMICAL ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Zhong-Hua Pang, Biao Ma, Guo-Ping Liu, Qing-Long Han
Summary: This paper investigates the data-driven control problem for a class of nonlinear systems. It presents an incremental triangular dynamic linearization (ITDL) model and designs an online estimation algorithm and adaptive control law. The convergence and stability of the closed-loop system are analyzed.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Automation & Control Systems
Chen Chen, Jiangang Lu
Summary: This paper addresses the compensation control problem for a class of nonlinear discrete-time systems with bounded disturbances. It proposes two data-driven controllers based on the dynamic linearization technique, which are purely adaptive disturbance compensation controllers. The controllers have time-varying adaptive gains that are updated using input-output data, without the need for any model information of the controlled plant. The stability of the controllers is guaranteed, and their effectiveness and applicability are demonstrated through numerical simulation and a distillation column.
Article
Automation & Control Systems
Xing Liu, Lin Qiu, Youtong Fang, Jose Rodriguez
Summary: In this article, a novel robust data-driven model-free predictive control framework is proposed based on the I/O data of the controlled plants. The framework combines neural predictor-based model-free adaptive control and finite control-set model predictive control to explicitly address uncertainties in controlled systems. However, the online computational complexity of the framework limits its practical implementation. To overcome this limitation, a supervised imitation learning technique is developed to imitate the known suggested controller, simplifying the implementation process of robust predictive control.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Civil
Bai-Fan Yue, Wei-Wei Che
Summary: This paper investigates the resilient fault-tolerant model-free adaptive platooning security control issue for the vehicular platooning systems subject to sensor faults and aperiodic denial-of-service attacks. Firstly, an equivalent linear data model is obtained using the partial form dynamic linearization technique. Then, a fault-tolerant control framework is developed with consideration of the sensor faults and a gradient descent method-based neural network is adopted for fault approximation. Thirdly, an attack compensation mechanism is designed and a novel resilient FT-MFAPSC algorithm is proposed for the VPSs against aperiodic DoS attacks, accomplishing the control objectives. Finally, the effectiveness of the developed algorithm is illustrated through simulation examples and comparisons.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Energy & Fuels
Yeon-Pyeong Jo, Mohammed Saad Faizan Bangi, Sang-Hwan Son, Joseph Sang-Il Kwon, Sung-Won Hwang
Summary: This paper discusses the importance of liquefied natural gas (LNG) as an eco-friendly fuel source in the ocean industry and proposes the use of a model predictive control (MPC) system to regulate LNG tank pressure. The research shows that the offset-free MPC system is able to effectively control pressure, even in abnormal circumstances such as fire accidents.
Article
Metallurgy & Metallurgical Engineering
Ding Shu-chen, Peng Li, Qiao Shang-ling, Liu Rong-qiang, Bundi Josephat
Summary: The development of the underactuated cable-driven truss-like manipulator (UCTM) for aerospace applications faces challenges in stabilization and control due to system complexity and nonlinearity. This paper proposes dynamic modelling and trajectory tracking control methods to address these difficulties and achieve desired control goals for UCTM. Simulation experiments validate the effectiveness of the proposed methods.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Automation & Control Systems
Xiangxiang Meng, Haisheng Yu, Jie Zhang, Tao Xu, Herong Wu, Kejia Yan
Summary: This paper proposes a novel input/output feedback linearization control method using a nonlinear disturbance observer (NDOB) for a quadruple-tank liquid level (QTLL) system. By establishing the mathematical model and designing a controller, and applying a disturbance observer for compensation control, better control performance than traditional methods is achieved.
Article
Engineering, Marine
Jiafeng Zhao, Yuanqin Qin, Chaocheng Hu, Guohua Xu, Kan Xu, Yingkai Xia
Summary: This paper proposes a novel motion-tracking control methodology for an underwater cable-driven parallel mechanism. It introduces a linear model predictive control (LMPC) method for planning cable tensions and a robust adaptive backstepping controller for converting cable tension into winch speed. The X-swapping method is used for linearization and identification of time-varying nonlinear parameters. Additionally, a dynamic minimum tension control (DMTC) method is proposed to obtain cable-tension constraint values based on the equivalent control concept.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Guanru Pan, Ruchuan Ou, Timm Faulwasser
Summary: Data-driven stochastic predictive control scheme is proposed for LTI systems subject to unbounded additive process disturbances. A data-driven surrogate optimal control problem is constructed using a stochastic extension of the fundamental lemma and leveraging polynomial chaos expansions. Sufficient conditions for recursive feasibility and stability of the proposed scheme are provided, along with an online selection strategy of the initial condition. Numerical examples illustrate the efficacy and closed-loop properties of the proposed scheme for process disturbances governed by different distributions.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Qi Li, Liangzhen Yin, Hanqing Yang, Tianhong Wang, Yibin Qiu, Weirong Chen
Summary: This article proposes a hierarchical performance enhancement control strategy (HPECS) to effectively address the challenges of efficient and stable operation of proton exchange membrane fuel cell (PEMFC) system.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)