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Adaptive multiple model control for nonlinear constrained systems with a novel barrier Lyapunov function

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第11期33卷 6533-6550页

作者： Zhang, Yanqi Wang, Xin Wang, Zhenlei East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

This paper investigates the problem of the multiple model control of nonlinear full state constrained systems with a novel barrier Lyapunov function. To handle the problem of unknown parameters, the identification model set containing q+1$$ q+1 $$ identification models is established. The novel barrier Lyapunov functions (BLFs) are employed to guarantee that all system states do not violate their boundaries and can remove the restricted condition associated with traditional BLFs. By using the first order filter technique, the "explosion of complexity" difficulty caused by the repeated differential from the backstepping technique is removed. In addition, the estimation of unknown parameters is obtained by using the convex hull property. Based on the second level adaptation technique, the convex hull parameters are designed by using the identification error dynamics, and this algorithm has better convergence property than other algorithms. Then, a multiple model control strategy is developed, which not only guarantees that the requirement of all state constraints is realized, but also ensures that the boundedness of all signals of closed-loop systems are not violated. The key advantage is that the proposed strategy does not require enormous identification models, that is, the number of identification model is reduced significantly. Finally, the effectiveness of the presented scheme is illustrated through the simulation results.

关键词： adaptive control barrier Lyapunov functions multiple model control nonlinear systems second level adaptation state constraints

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Enhanced pressure control system for the vacuum vessel of Damavand Tokamak using PID and multiple model control

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JOURNAL OF PROCESS control 2024年 135卷

作者： Amini, Mahdi Shoorehdeli, Mahdi Aliyari Rasouli, Hossein K N Toosi Univ Technol Elect Engn Fac Control & Syst Dept Tehran Iran Nucl Sci & Technol Res Inst Plasma & Nucl Fus Res Sch Tehran Iran

This paper presents the implementation of the pressure control system for the vacuum vessel of Damavand Tokamak. PID controllers within the framework of multiple-model control are utilized for controller design, aiming to safely achieve the desired setpoint for the pressure of the vacuum vessel. The chamber pressure is measured in real-time using a Cold Cathode Pirani gauge and transferred as feedback to the controller. There is a gas injection system to adjust the chamber pressure. multiple process models are derived for the vacuum vessel pressure based on a system identification approach using the experimental data from the process. The derived models are employed in designing the PID controllers. The designed controllers are implemented on the tokamak gas injection system. The experimental results demonstrate that the designed controllers effectively track the desired pressure profile.

关键词： Damavand Tokamak PID controller multiple model control Nuclear Fusion Vacuum Pressure control

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Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach

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HELIYON 2024年第4期10卷 e25986页

作者： Fathi, Mohammad Bolandi, Hossein Iran Univ Sci & Technol Elect Engn Dept Tehran Iran

In the multiple model control (MMC) strategies, a bank of simple local models is used to describe the behavior of complex systems with vast operation space. In this approach, the system operation space is divided into several subspaces, and in each subspace, a simple local model is assigned to describe the system behavior. This study addresses the two main challenges in this field which involve determining the optimal number of required local models to form the model bank and identifying the optimal distribution of the local models across the system operation space. Providing appropriate answers to these questions directly affects the performance of the MMC system. In this paper, GA-based automatic clustering method is suggested to form an optimal model bank. In this regard, an appropriate mapping is established between the concepts of MMC and automatic clustering, and a novel unsupervised algorithm is designed to determine the optimal model bank. Unlike the existing methods in the literature, the proposed method can form the global optimal model bank without entrapment into local optima regardless of the initial conditions of the used search algorithm. In this paper, the formation of the optimal model bank using the proposed method is investigated by considering the spacecraft attitude dynamics as a complex, MIMO, non-linear case study and its satisfactory and promising performance is demonstrated.

关键词： multiple model control Automatic clustering Genetic algorithm Optimal model bank

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multiple model Predictive control of the Cardiovascular System Using Vagal Nerve Stimulation

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2025年第1期33卷 400-407页

作者： Yao, Yuyu Kothare, Mayuresh V. Lehigh Univ Dept Chem & Biomol Engn Bethlehem PA 18015 USA

Vagal nerve stimulation (VNS) is currently under investigation for the treatment of various cardiovascular diseases including heart failure, arrhythmia, and hypertension. In preclinical and clinical studies, VNS stimulation parameters are heuristically determined in the open loop. However, its therapeutic efficacy remains inconclusive, strongly suggesting the need for a closed-loop approach to optimize patient-specific stimulation parameters. In this paper, we develop a multiple model predictive control (MMPC) algorithm for automated regulation of heart rate (HR) and mean arterial pressure by optimally adjusting the amplitude and frequency of electrical pulses applied to three locations of the vagal nerve. The multiple local models are identified from our previously reported pulsatile rat cardiac model that emulates symptoms of hypertension in rest and exercise states. The computational expense of the proposed method is verified in simulation with rigorous hardware-in-the-loop (HIL) implementation.

关键词： Mathematical models Predictive models Heart rate Rats Computational modeling Heart Prediction algorithms Optical fiber devices Physiology Blood pressure Adaptive control cardiovascular system model predictive control (MPC) multiple model control nonlinear optimization peripheral stimulation vagal nerve stimulation (VNS)

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multiple model Switching control of Linear Time-Invariant Systems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第8期49卷 10961-10975页

作者： Dogruer, C. U. Yildirim, Bora Hacettepe Univ Dept Mech Engn Beytepe Campus TR-06800 Ankara Turkiye

In this article, multiple model switching controller was proposed to regulate the motion modes of a linear time-invariant system. Frequency domain of a linear time-invariant system is divided into a finite number of subregions;for each frequency domain subinterval, a single motion mode is picked by the modal decomposition of linear time-invariant system's dynamic equations. multiple model switching controller picks a subset of motion modes among the members of the model set by comparing the total energy stored in each mode and only those most active modes are regulated. Thus, a reduced-order dynamic model of linear time-invariant system is designed and regulated.

关键词： Active vibration control multiple model control Switching control model predictive control

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multiple model Adaptive control of Nonlinear Systems Based on Dynamic Optimization

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AUTOMATIC control AND COMPUTER SCIENCES 2022年第6期56卷 509-518页

作者： Long, Wenkun Xu, Tianpeng Gao, Haiyan Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Xiamen Univ Technol High Voltage Key Lab Fujian Prov Xiamen 361024 Peoples R China

In multiple model control, the model set is very important to the control performance. A novel multiple model control algorithm based on dynamic optimization is proposed for a nonlinear system with jumping parameters. The model set includes linear and nonlinear models. The linear models include some fixed and adaptive models which approximate the linear part of the system. Specially, the fixed models are constantly optimized using the new available data. For the nonlinear part of the system, a neural network is used to describe it. On this basis, the controller set corresponding to multiple models is designed according to the principle of asymptotic tracking. In practice, the switching between controllers is determined by a performance index. The results show that the optimization of the model set is helpful to improve the system control performance. The effectiveness of the proposed method is verified by the simulation results.

关键词： nonlinear systems adaptive control multiple model control optimization

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Neural Networks-based multiple model control of a Class of Nonlinear Systems with Unknown Parameters

Neural Networks-based Multiple Model Control of a Class of N...

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Chinese Automation Congress (CAC)

作者： Tang, Weiqiang Qi, Yongda Long, Wenkun Gao, Haiyan Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou Peoples R China Xiamen Inst Technol Coll Elect Engn & Automat Xiamen Peoples R China

ISBN: (纸本)9781728176871

A novel nonlinear system adaptive control method based on neural networks is proposed for a class of nonlinear discrete-time systems with unknown parameters. The nonlinear dynamics arc first represented by the linear part and the nonlinear part. For the linear part, several fixed models are established by the localization method. At the same time, in order to improve the control quality and accelerate the convergence of the system parameters, two adaptive models are introduced. For the nonlinear part, its model can be set up by a neural network. Then, robust adaptive controllers arc designed based on the fixed model, adaptive model and nonlinear model. In practice, the sub-model which is most suitable for the system is selected according to the switching rule, and the corresponding control law is implemented. Finally, the simulation results show that the proposed method can effectively improve the transient performance of the system.

关键词： nonlinear systems multiple model control localization neural networks

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multiple U-model control of uncertain discrete-time nonlinear systems

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INTERNATIONAL JOURNAL OF modelLING IDENTIFICATION AND control 2020年第4期34卷 293-300页

作者： Zhang, Weicun Zhang, Zhaolong Li, Qing Xue, Zhuoer Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 10083 Peoples R China

Based on U-model concept and multiple model methodology, this paper presents a weighted multiple U-model control scheme to address the control problem of some classes of discrete-time nonlinear systems with large parameter uncertainties including parameter jumps, which is a difficult problem without a unified solution up to now. Simulation results on two types of non-linear systems with large parameter uncertainties verified the effectiveness of the proposed scheme.

关键词： U-model control multiple model control non-linear system uncertain system discrete-time system

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Removing the Feasibility Conditions Imposed on Full-State Constraint control Design for Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第5期71卷 2684-2688页

作者： Zhang, Yanqi Wang, Xin Wang, Zhenlei East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

In this brief, a novel full state constraint scheme for a class of high-order discrete-time nonlinear systems subjected to unknown parameters is investigated. The new prescribed performance functions are used to ensure that all states of systems can be constrained in a predefined region. Compared with the existing state constraint control and prescribed performance function schemes, the new prescribed performance functions have the advantage of not requiring the feasibility conditions that the virtual controllers are assumed to be bounded. Unlike the conventional switch multiple model set, the proposed multiple model method not only does not rely on a large number of identification models, but also it avoids the potential chattering problem from the switch multiple model method. It is demonstrated that the presented control method can ensure the global boundedness of all the closed-loop signals and that all states converge to the predefined regions. The demonstrated control scheme is implemented on one simulation example for illustrating the effectiveness of the reported scheme.

关键词： Estimation Adaptation models Discrete-time systems Time-varying systems Nonlinear systems Lyapunov methods Switches Feasibility conditions full state constraint prescribed performance function multiple model control discrete-time nonlinear systems

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The use of adaptive multiple models for predefined exponential stability of control systems of uncertain discrete-time nonlinear objects

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INTERNATIONAL JOURNAL OF ADAPTIVE control AND SIGNAL PROCESSING 2024年第3期38卷 772-791页

In this work, an adaptive multiple model pre-assigned exponential convergence control scheme based on the time varying exponential function is demonstrated for a class of discrete-time nonlinear systems. For coping with the noncausality problem from previous adaptive backstepping design method of nonlinear discrete-time systems, a first-order recursive filter is used in controller design procedures and the n-step prediction model is not needed, thereby reducing the computational burden effectively. The time varying function is employed to force all states variables to convergence to zero with a pre-defined rate which does not rely on the initial conditions of systems. In addition, the multiple model set is constructed to address unknown parameters problem and further the second level adaptive algorithm of unknown parameters is developed. A characteristic feature of the second-level multi-model adaptation algorithm is that it dynamically adapts to time changes in the identified model (it uses identification information better than the switching multiple model control). In a unified framework of second level adaption, a first-order recursive filter and backstepping technique, a pre-assigned exponential convergence controller is presented. Finally, the results about discrete-time pre-assigned exponential convergence method are compared with other methods using two case studies.

关键词： adaptive control discrete-time systems multiple model control pre-assigned exponential regulation second level adaption

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