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

作者： Huang, Jiangyang Zhangl, Zhengxuan Yang, Xu Tu, Rang Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Civil & Resources Engn Beijing 100083 Peoples R China

ISBN: (纸本)9798350321050

An adaptive control method based on online identification of controller parameters is proposed for the cooling process of Computer Room Air Conditioning(CRAC) in data centers with complex nonlinear and dynamic characteristics. Firstly, the nonlinear system is divided into low-order linear part and high-order unmodeled dynamic term. The random vector functional link network is used to identify the controller parameters. The unmodeled dynamic compensator is introduced into the control strategy to offset the influence of uncertain dynamic term on the closed-loop control system of CRAC-Cabinet system. Then, in order to solve the problem that the identification ability of the offline model is reduced due to the changes of the location layout of the cabinet, the number and placement of electronic devices and other factors in the data center, the online addition of hidden layer nodes is added, and the output weight of the original hidden layer is recalculated and updated when new nodes are added. The simulation results show that the control and tracking ability of the nonlinear adaptive controller with unmodeled dynamic compensation is better than that of the adaptive controller based on linear model, and the experimental results show that the incremental learning network model has faster response speed and higher tracking accuracy than the original model.

关键词： Adaptive control CRAC Nonlinear Random vector functional link network incremental learning

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learning-Based High-Precision Tracking control: Development, Synthesis, and Verification on Spiral Scanning With a Flexure-Based Nanopositioner

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ieee-ASME TRANSACTIONS ON MECHATRONICS 2024年第5期29卷 3867-3876页

作者： Li, Xiaocong Zhu, Haiyue Ma, Jun Wang, Wenxin Teo, Tat Joo Teo, Chek Sing Lee, Tong Heng ASTAR Adapt Robot & Mechatron Grp Singapore Inst Mfg Technol Singapore 138634 Singapore Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Guangzhou Peoples R China Hong Kong Univ Sci & Technol Div Emerging Interdisciplinary Areas Hong Kong Peoples R China HKUST Shenzhen Hong Kong Collaborat Innovat Res Shenzhen Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Home Team Sci & Technol Agcy Robot Automat & Unmanned Syst Ctr Expertise Singapore 329560 Singapore

The traditional methodology utilized in dynamic tracking control synthesis is usually model-based, and therefore, the performance is highly dependent on a precise mathematical model. However, with the growth in system complexity, extremely precise dynamic models for modern robotic and automation systems are very hard to obtain. This challenge has sparked the interest of researchers in moving toward data-driven learning-based concepts, specifically aiming to fully exploit the abundant data available to learn better controls. Along this research direction, continued efforts have been spent on learning a single feedback controller. However, just attempting learning procedures on the feedback controller alone could suffer from severe performance limitations due to its reactive nature, i.e., an error must occur first before any corrective action is taken. In line with this consideration, we propose an integrated two-degree-of-freedom (2-DOF) learning-based tracking control synthesis for high-precision systems, consisting of both feedforward and feedback controllers. Unlike the traditional control design where full knowledge of the dynamics is assumed, we explore the use of actual motion data to iteratively determine the optimal controller parameters using gradient-based optimization. The key step here is to estimate the gradient and Hessian of the cost function without a priori knowledge about the dynamics. Experiments are further conducted based on a prototype flexure-based nanopositioner for spiral scanning applications, demonstrating high-precision nanometer-scale tracking performance that is close to the sensor resolution.

关键词： Optimization Robots Feedforward systems Automation Adaptive control Cost function Trajectory tracking data-driven optimization learning-based control model learning for control nanopositioning optimization and optimal control precision motion control

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Distributed containment control for nonlinear HOFA-MASs 12

Distributed containment control for nonlinear HOFA-MASs

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

作者： Liu, Yang Zhang, Jiaming Lu, Xinxi Beihang Univ BUAA Sch Automat Sci & Elect Engn Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Sch Automat Sci & Elect Engn Ctr Informat & Control Beijing 100191 Peoples R China Beihang Univ BUAA Sch Software Beijing 100191 Peoples R China

ISBN: (纸本)9798350321050

This work provides the distributed containment control protocols for nonlinear high-order fully actuated multi-agent systems (HOFA-MASs) under a directed communication topology graph. Based on the nonlinear HOFA model of the system, the closed-loop system can be always converted to a constant linear one with a desired eigenstructure by simply designing a controller. A reference generator is constructed for each follower by using the state information between its neighbors, then constructing the coordinate transformation so that each follower's output can track the corresponding virtual reference signal which is confined to the convex hull generated by the leaders. By using the radial basis function (RBF) neural networks (NNs) technique and the Lyapunov theorem of stability, the distributed controllers are designed for nonlinear HOFA-MASs with known nonlinear functions, nonlinear functions with unknown parameterizations and completely unknown nonlinear functions, which can guarantee that the outputs of followers can enter into the convex hull. Finally, the simulation results on robotic system demonstrate the availability of the designed protocols.

关键词： Nonlinear systems HOFA-MASs containment control neural networks adaptive control

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Adaptive Gain Iterative learning control Based on Extended State Observer for Batch Processes 13

Adaptive Gain Iterative Learning Control Based on Extended S...

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

作者： Li, Dazi Liu, Yanan Xu, Jiahui College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9798350361674

control of batch process encounters challenges in accurate modeling and presents serious nonlinearities. In this paper, an adaptive gain iterative learning control method based on the extended state observer (AG-ILC-ESO) is proposed to enhance the control performance. Addition of an adaptive gain for iterative learning proves effective in correcting the learning direction and enhancing controller performance. This adaptive gain enables real-time adjustments of the control strategy to accommodate system changes. The incorporation of an ESO, based on ILC, facilitates a more precise estimation of system states that are challenging to measure directly. Subsequently, the control system can be adjusted based on the actual system response, leading to gradual improvements in control performance. The proposed method's effectiveness is validated through simulation experiments. © 2024 ieee.

关键词： Batch data processing

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PD control with gravity compensation for 3-RPS parallel manipulator 12

PD control with gravity compensation for 3-RPS parallel mani...

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

作者： Lv, Zhiyuan Ning, Donghong Liu, Pengfei Liao, Peng Ocean Univ China Engn Coll Qingdao 266500 Peoples R China

ISBN: (纸本)9798350321050

When a 3-RPS (revolute-prismatic-spherical) parallel manipulator is working, the gravity center of its load will move with its rotation, which will deteriorate the performance of the traditional Proportional Derivative (PD) controller and introduce steady-state error. This paper proposes a dynamic gravity compensation (GC) based controller for a 3-RPS parallel manipulator to improve the dynamic performance. Specifically, this paper offers a detailed analysis of this manipulator's Jacobian matrix, through which the gravity term in the dynamic model can be separated and converted to the joint space. Then the Jacobian matrix can be used in dynamic GC for the traditional PD controller. As a result, the improved controller provides smoother motion and better control. The simulation results show that the PD controller with GC has a better control effect than the traditional one. The root mean square (RMS) values of tracking errors in three degrees of freedom under sinusoidal excitation are reduced by 49.18%, 70.25%, and 88.28%, respectively, compared to traditional PD controllers.

关键词： 3-RPS parallel manipulator Inverse kinematics Gravity compensation PD control

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H∞ Tracking control of Tow Time Scale Linear System Based on Off-Policy Reinforcement learning 13

H∞ Tracking Control of Tow Time Scale Linear System Based o...

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

作者： Shen, MinYin Liu, Fei Wuxi214122 China

ISBN: (纸本)9798350361674

In this paper, by integrating singular perturbation method and off-policy reinforcement learning, a new approach to solving the H∞ tracking control problem is presented for linear time-invariant two time scales systems. A composite control scheme is designed through decomposing the two time scales system into fast and slow subsystems. For the slow subsystem, a model-free H∞ tracking controller is designed based on off-policy reinforcement learning to accomplish system tracking of the desired trajectory while effectively suppressing disturbances, without knowledge of the dynamics. Meanwhile, for the fast subsystem, a model-based offline controller is adopted. To better tracking performance, an exponential discount factor is introduced to weight the cost of input and tracking errors. By analysis and simulation, the effectiveness and superior tracking performance of proposed method has been verified. © 2024 ieee.

关键词： Reinforcement learning

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Model - free Adaptive Sliding Mode Predictive control of Linear Ultrasonic Motor 12

Model - free Adaptive Sliding Mode Predictive Control of Lin...

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

作者： Li Yifan Cao Rongmin Hou Zhongsheng Zhou Hui Xing Chang Debiao Jia Jihui Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China Qingdao Univ Sch Automat Shandong 266071 Peoples R China Beijing Jinduo Technol Dev Co Ltd Dept Technol Beijing 100081 Peoples R China

ISBN: (纸本)9798350321050

Because the linear ultrasonic motor system has obvious nonlinearity and time-varying. In the operation process, the tracking error, mechanical delays, and other factors will greatly impact the position tracking accuracy. To reduce the linear ultrasonic motor position steady-state error. Sliding mode control (SMC) is invariant to system disturbance and model-free adaptive predictive control (MFAPC) can realize adaptive control only by input and output data of a controlled system, this paper designed a model-free adaptive sliding mode predictive controller (MFASMPC) and proved its stability and convergence Finally, the position control of linear ultrasonic motor based on model-free adaptive sliding mode predictive control method is simulated and analyzed. Theoretical proof and simulation results show that such an algorithm can effectively reduce the steady-state error to meet the control accuracy requirements.

关键词： model-free adaptive control predictive control linear ultrasonic motor sliding mode control position control

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A Novel Lazy learning-based data driven Fault Diagnosis Method for Public Transport Vehicles 12

A Novel Lazy Learning-based Data Driven Fault Diagnosis Meth...

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

作者： Fu, Xiaotian Wang, Li Liu, Shida Ji, Honghai North China Univ Technol Sch Elect & Control Engn Beijing 100043 Peoples R China

ISBN: (纸本)9798350321050

In this work, a new data driven online local modeling method, which combines relevance vector machine and Lazy learning (RVM-LL) is proposed for fault diagnosis of public transport vehicles. In proposed RVM-LL, LL builds multiple local models at each query point based on the data query, and determines the best data model in the current query through a selection mechanism. First, the method establishes the similarity function and selects the local nearest neighbor points. Secondly, a local model is built based on RVM algorithm. Third, the optimal local model is selected by leave-one-out cross validation method. The advantages of this method are: LL does not need to establish an accurate bus fault diagnosis model, and it has the characteristics of small sample local online modeling, which does not need to spend a lot of time on training, saves time and is adaptive. In addition, the background of this paper is based on practical applications, and the data used are also from real public transport vehicles, and the accuracy of the method is verified by simulation experiments.

关键词： Lazy learning Fault Diagnosis Fault Diagnosis of Public Transport Vehicles

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A Novel data-driven control Method for a Class Magnetic Levitation systems with Time Delay and Noise Interference 13

A Novel Data-Driven Control Method for a Class Magnetic Levi...

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

作者： Li, Zhen Jin, Shangtai Yin, Chenkun Institute of Advanced Control Systems Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798350361674

In response to the challenges posed by nonlinear large time delays and external noise interference in the control process of magnetic levitation systems, this paper proposes a model-free adaptive control algorithm based on time delay compensation and filtering (DCF-MFAC) to attain magnetic levitation systems control. Firstly, an adaptive control algorithm is designed by introducing a dynamic linearization technique with time-varying parameters based on pseudo-gradient (PG), which linearizes the unknown nonlinear system and facilitates controller design. Subsequently, the algorithm utilizes Smith predictor and second-order integrator-based estimation to estimate the output differentials of the controlled object, compensating for system time delays. Additionally, the algorithm introduces a filter to attenuate or suppress noise in the system, ensuring smoother control signals. The effectiveness of the algorithm in magnetic levitation system control is verified through joint simulation using HUMUSOFT real-time toolbox and Matlab. © 2024 ieee.

关键词： Magnetic levitation

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learning Distributed Predictive control via Spatial-Temporal Games

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第4期72卷 4186-4195页

作者： Wang, Zhuping Liu, Peijiang Ren, Hongliang Zhang, Hao Yang, Xindi Shanghai Inst Intelligent Sci & Technol Shanghai 201210 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

This article investigates learning predictive control framework for multiagent systems with unknown dynamics. Predictive control, which generates temporal control inputs, provides potential applications in observation loss scenarios. First, control causality is inversely extracted from time series analysis, and predictive control is characterized as sequential feedback control. Next, we focus on distributed communication and time consistency under the causality. Distributed predictive control is equivalently partitioned into spatial and temporal subgames, respectively. Spatial subgames achieve equivalence between global and local objectives through Nash equilibrium, while temporal ones force local control causality to achieve stability and optimality with time consistency. Furthermore, a multistep reinforcement learning algorithm is proposed for data-driven implementation, and dynamics knowledge is avoided with interactive data. The learning properties are discussed with theoretical proof and parameters selection. Finally, we pose numerical results to demonstrate effectiveness, and robotics experiments are also carried out to show potential advantage under observation loss scenarios.

关键词： control causality detection multiagent game predictive control reinforcement learning control causality detection multiagent game predictive control reinforcement learning

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