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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Jiang, Minghao Zheng, Dongdong Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9798350321050

In this paper, a novel adaptive impedance control strategy for the flexible joint robot (FJR) is proposed. To simplify the controller design process, the singular perturbation technique is used to decompose the original high-order system into low-order subsystems. To reduce the mismatch of the system model, the neural network is used to estimate the friction and unknown system dynamic, where an improved optimal bounded ellipsoid (IOBE) algorithm is adopted to optimize the weight matrix of the neural network, which can fix the learning gain matrix vanishing or unbounded growth in traditional OBE algorithm. Different from traditional impedance controllers with fixed impedance parameters, in this paper, the variable stiffness and damping coefficients are used, which can maintain a fast response speed when the FJR is moving freely and can show more compliance characteristics when the FJR is interacting with the environment. The stability of the closed-loop system is proved via the Lyapunov approach and the effectiveness of the algorithm is verified by simulations.

关键词： singular perturbation optimal bounded ellipsoid neural network variable impedance control

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Model-Free Adaptive control of Nonlinear systems with Input Backlash and Constraints 13

Model-Free Adaptive Control of Nonlinear Systems with Input ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Hao, Leilei Jiang, Xiaoqing Liu, Xiaona Zhong, Yumeng Li, Hang Su, Ruixian School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Collage of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350361674

For nonlinear Backlash systems with input constraints that are difficult to model accurately, a model-free adaptive control algorithm (MFAC)based on data-driven technology is proposed. The criterion function of MFAC algorithm is applied to set up a quadratic programming problem with input constraints, and the optimal control input signal is obtained by solving the quadratic programming problem. The tracking differentiator is used to process the measured output signal to reject the adverse effect of random noise on the solution of the optimal input signal and the control stationarity. The stability of the control algorithm is also analyzed and proved. Finally, the superiority of the proposed control algorithm is verified by example test. © 2024 ieee.

关键词： Quadratic programming

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Automatic learning-Based Calibration of Assisted Motorcycle Gearshift: A Comparative Study

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ieee TRANSACTIONS ON control systems TECHNOLOGY 2025年

作者： Catenaro, Edoardo Sabug Jr, Lorenzo Panzani, Giulio Sette, Davide Ruiz, Fredy Fagiano, Lorenzo Savaresi, Sergio M. Politecn Milan Dept Elect Informat & Bioengn Milan Italy Ducati Motor Holding I-40132 Bologna Italy

A comparison of different approaches to the automatic online, data-driven calibration of assisted gearshift settings for a motorcycle is presented. An objective function associated with the component stress and clutch resynchronization time is exploited and optimized during operation using different strategies: from na & iuml;ve space-filling approaches to learning-based black-box optimization algorithms. The performance of various methods is compared in real-world experiments using metrics related to the experimental convergence rate and the quality of the best found result.

关键词： Engines Gears Torque Motorcycles Wheels Optimization Tuning Logic control systems Vehicles Automatic tuning global optimization algorithms machine learning powertrain control

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Limbic System-Inspired Robust Event-driven control for High-Order Uncertain Nonlinear systems

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ieee control systems LETTERS 2024年 8卷 3057-3062页

作者： Escareno, J. Alvarez-Munoz, J. U. Carrillo, L. R. Garcia Scola, I. Rubio Franco-Robles, J. Labbani-Igbida, O. Univ Limoges CNRS 7252 XLIM Res Inst UMR F-87280 Limoges France Inst Polytech Sci Avancees F-94200 Ivry Sur WSeine France New Mexico State Univ Klipsch Sch Elect & Comp Engn Las Cruces NM 88003 USA Consejo Nacl Invest Cient & Tecn Dept Ind Prod Engn INTI RA-2000 Rosario Argentina 3iL Ingn F-87015 Limoges France

Nonlinearity and uncertainty are major features in control systems. In this context, the present work proposes to merge the brain emotional learning model with the benefits of robust event-driven control to handle uncertain nonlinear systems. The state-dependent unmodeled dynamics is estimated via the limbic system-inspired learning algorithm and added to the nominal control signal for compensation purposes. Furthermore, aiming at reducing data processing, and inherently, computational cost, the controller is triggered asynchronously driven by events function. Moreover, the closed-loop stability of the proposed control scheme is verified through the Lyapunov formalism, as well as the sampling admissibility to prevent the Zeno phenomena. The performance observed in the numerical results witnesses the effectiveness of the proposed control scheme.

关键词： Artificial neural networks Nonlinear systems Event detection control systems Computational modeling Vectors Stability analysis Brain modeling Uncertainty Process control Uncertain nonlinear systems bio-inspired control design robust control event-driven control

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data-driven Policy Iteration for Nonlinear Optimal control Problems

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2023年第10期34卷 7365-7376页

作者： Possieri, Corrado Sassano, Mario CNR Ist Anal Sistemi Informat A Ruberti I-00185 Rome Italy Univ Roma Tor Vergata Dipartimento Ingn Civile & Ingn Informat I-00133 Rome Italy

The design of optimal control laws for nonlinear systems is tackled without knowledge of the underlying plant and of a functional description of the cost function. The proposed data-driven method is based only on real-time measurements of the state of the plant and of the (instantaneous) value of the reward signal and relies on a combination of ideas borrowed from the theories of optimal and adaptive control problems. As a result, the architecture implements a policy iteration strategy in which, hinging on the use of neural networks, the policy evaluation step and the computation of the relevant information instrumental for the policy improvement step are performed in a purely continuous-time fashion. Furthermore, the desirable features of the design method, including convergence rate and robustness properties, are discussed. Finally, the theory is validated via two benchmark numerical simulations.

关键词： Optimal control Costs Neural networks Real-time systems Nonlinear dynamical systems Closed loop systems learning systems data-driven methods nonlinear systems optimal control policy iteration

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An Iterative learning control Method for a Class of Continuous-Time systems with Randomly Varying Trial Lengths 13

An Iterative Learning Control Method for a Class of Continuo...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Yin, Chaochen Liu, Jian School of Mechanical and Electrical Engineering Xidian University China Center for Complex Systems School of Mechano-electronic Engineering Xidian University China

ISBN: (纸本)9798350361674

This paper considers the iterative learning control (ILC) for a class of continuous-time systems with direct-though term, whose trial lengths are iteration-varying from trial to trial. A novel randomly varying trial model is proposed, in which we use a group of staircase functions to model the varying trial length. Then, an intermittent-type randomly varying trial ILC update law is developed. By the lambda-norm technique, we analyze the convergence of the iterative learning process and give the convergence condition of the ILC process. Finally, a numerical example is given to demonstrate the validity of the main result. © 2024 ieee.

关键词： Continuous time systems

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Dual Observer-based Model-Free Adaptive I/O Constrained control for MIMO Nonlinear systems 12

Dual Observer-based Model-Free Adaptive I/O Constrained Cont...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Zhang, Weiming Xu, Dezhi Yang, Weilin Liu, Jianxing Hua, Fei Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

ISBN: (纸本)9798350321050

In this paper, a dual observer based model-free adaptive control strategy is designed for multiple input multiple output (MIMO) nonlinear systems with disturbances and input/output (I/O) constraints. The dual observers consists of an adaptive observer and a discrete extended state observer, in which the former is designed to realize the dynamic reconfiguration of the system and devise the Lyapunov stability criterion-based estimation algorithm for time-varying parameters, and the latter is explored for composite disturbance estimation. Based on the information from dual observers, a dynamic anti-windup compensator along with an improved prescribed performance control method are proposed in the sliding mode controller to solve the I/O constraint problem. Finally, the stability analysis and simulation are supplied for performance verification.

关键词： Model-Free Adaptive control I/O Constraints Dual Observer MIMO Nonlinear systems

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Optimal control of stochastic power system based on braking resistance 12

Optimal control of stochastic power system based on braking ...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Lin, Xue Wang, Qi Luo, Yinsheng Cai, Changchun Jiangsu Key Lab Power Transmiss & Distribut Equip Changzhou 213001 Jiangsu Peoples R China Jiangsu Univ Technol Changzhou 213001 Jiangsu Peoples R China

ISBN: (纸本)9798350321050

How to suppress the adverse effects of the uncertainty of renewable energy on power systems has always been a major technical requirement for power system operation, which has not been sufficiently considered in the traditional power system controller. Therefore, it is necessary to study the power system control under stochastic disturbances. In this paper, a controlled single-machine infinite-bus system model is established based on the stochastic averaging method of quasi-Hamiltonian systems, and a one-dimensional diffusion equation based on energy function with control item is obtained. According to the stochastic optimal control theory, the optimal control law of the system is obtained from the diffusion equation with the maximum reliability of the bounded fluctuation of the system as the control target. The effectiveness of the control method is verified by simulation.

关键词： Renewable energy Stochastic disturbances Quasi-Hamiltonian system Stochastic optimal control

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Adaptive Iterative learning Fault-Tolerant control for High-Order Non-Strict Feedback Nonlinear systems with Prescribed Performance 13

Adaptive Iterative Learning Fault-Tolerant Control for High-...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Cui, Hanxue Sheng, Ning Qingdao University of Science and Technology China

ISBN: (纸本)9798350361674

This study explores the issue of devising an adaptive iterative learning fault-tolerance control approach for high-order nonlinear systems with prescribed performance in non-strict feedback form is investigated. By establishing an iterative update law under the same initial conditions, the actuator fault is compensated. To address the challenges posed by unknown nonlinear functions, the use of fuzzy logic systems for approximation. Additionally, the error transformation technique is designed on the adaptive robust fault-tolerant controller, which aims to secure the convergence of the output tracking error towards a prescribed performance metric. Ultimately, by employing Lyapunov stability analysis, the tracking error converges towards a small range near zero as the iteration progresses. © 2024 ieee.

关键词： adaptive iterative learning High-order nonlinear systems prescribed performance

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Research on Virtual Widening of Power Information Acquisition Channel Based on LoRa and data Compression 12

Research on Virtual Widening of Power Information Acquisitio...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Jiang, Jing Wang, Mingjia Shandong Foreign Trade Vocat Coll Dept Informat Engn Qingdao 266100 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China

ISBN: (纸本)9798350321050

Limited by the channel bandwidth and information rate of the communication network of the electric energy information acquisition system, the energy data of the power system terminal lacks a suitable access method response to the above problems, this article combines the wide coverage characteristics and data compression technology achieved by LoRa (Long Range Radio, LoRa) spread spectrum communication, and proposes a new method of access communication based on LoRa spread spectrum and improved data compression coding for embedded applications. This technology can not only take into account the advantages of low design cost, strong coverage, anti-interference, etc. But also increases the virtual bandwidth of the channel, optimizes the comlink, and lay a solid foundation for high-frequency data collection and large-scale data transmission.

关键词： Electric energy information acquisition system LoRa spread spectrum technology data compression technology Virtual bandwidth

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