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Zero-dynamics-based adaptive sliding mode control for a wheeled inverted pendulum with parametric friction and uncertain dynamics compensation

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2015年第1期37卷 91-99页

作者： Yue, Ming Wei, Xing Li, Zhijun Dalian Univ Technol Sch Automot Engn Dalian 116024 Liaoning Provin Peoples R China Zhejiang Univ State Key Lab Fluid Power Transmiss & Control Hangzhou Zhejiang Peoples R China South China Univ Technolgy Key Lab Autonomous Syst & Networked Control Guangzhou Guangdong Peoples R China

In this paper, we propose a novel control methodology based on zero-dynamics theory for a class of wheeled inverted pendulum (WIP) vehicles, which is efficient even in the presence of uncertain system frictions and dynamics parameters. The control schemes are elegantly constructed so that the WIP vehicle can successfully implement stabilizing of the posture (longitudinal and rotational movements), as well as hold the upright position of the vehicle body (tilt angle stability), only by the two control inputs with the aid of the design approach of zero-dynamics. In particular, the dynamics uncertainties, especially the friction effects, would deteriorate the control performance severely in practice. Therefore, we employ adaptive laws for the design parameters of zero-dynamics subsystem and uncertain coefficients of parametric frictions and dynamics. Consequently, the estimated frictions and dynamics are compensated through feedforward to obtain better control performance. To enhance the robustness of the system against parameter variations and external disturbances, sliding mode control techniques are applied to derive the specific algorithms, and then the closed-loop systems are proven to be globally asymptotically stable by Lyapunov techniques and LaSalle's invariance theorem. In addition, simulation studies have been performed to demonstrate the feasibility and effectiveness of the proposed strategies, which illuminate the promising practical application potentiality of the designed WIP vehicle control system.

关键词： Wheeled inverted pendulum parametric uncertainty zero-dynamics adaptive control

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Two time-scale assignment with state extension for an autonomous helicopter

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ASIAN JOURNAL OF control 2021年第4期23卷 1707-1719页

作者： Yang, Hao Pei, Hailong South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist EducSch Automat Sci & Engn Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Guangzhou 510640 Peoples R China

In this paper, a feedback control law based on two time-scale assignment and state extension is implemented for the nonlinear non-minimum phase dynamics of three degree-of-freedom autonomous unmanned helicopters (AUH). Unlike previous techniques, the design procedure does not require time-scale decomposition between attitude angle and translational states. This reduces the requirements for the AUH model and makes the controller more reliable. Time-scale assignment is implemented with partial internal dynamics. Exponential stability of the closed-loop system is proved with the retained non-minimum phase character, and simulation results are provided to demonstrate the effectiveness of the presented approach.

关键词： autonomous helicopter non-minimum phase singular perturbation theory state extension time-scale assignment

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Redesign of approximate dynamic inversion for pure-feedback nonaffine-in-control nonlinear systems via input saturation

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INTERNATIONAL JOURNAL OF control 2021年第10期94卷 2758-2765页

In this paper, a redesigned approximate dynamic inversion (RADI) method is developed for a class of pure-feedback nonaffine-in-control nonlinear systems (PFNNSs). Recently, the approximate dynamic inversion (ADI)-based algorithms have been widely implemented for nonaffine-in-control nonlinear systems. To employ ADI-based methods, a fast dynamic subsystem has to be established to derive the explicit inversion of the nonaffine equation. However, the previous research on ADI rarely considers that the amplitude of actual input derived from the fast augmented subsystem can be extremely high, which maybe intolerable for practical systems. In this paper, we set up input saturation and view the difference between input with and without saturation as a perturbation. Then, an intermediate subsystem is constructed to compensate the influence of this perturbation by approximating its inversion. The exponential stability of the overall closed-loop system is ensured via singular perturbation theorem. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

关键词： Approximate dynamic inversion input saturation pure-feedback systems nonaffine-in-control singular perturbation theory

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Neuro-Adaptive-Based Predefined-Time Smooth control for Manipulators With Disturbance

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第8期54卷 4605-4616页

作者： Fan, Yanli Yang, Chenguang Zhan, Hong Li, Yongming South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China

In this article, an adaptive neural network (NN) predefined-time tracking control strategy is investigated for robot systems with external disturbance. First, under the predefined-time stability criterion, a new time-controlled torque controller is constructed, which allows for the system convergence time to be set beforehand. This is conducive to manipulators performing trajectory tracking tasks that require specific convergence times. In addition, the continuous terms are constructed by smoothly switching between the fractional and cubic terms of state-dependence. This solution successfully resolves the issues of singularity. Moreover, in order to compensate for unknown nonlinearity and torque disturbance, two different adaptive update laws are established, respectively. Furthermore, rigorous stability is proved based on the predefined-time Lyapunov theory. Finally, the accuracy and efficiency of the NN-based predefined-time control algorithm is confirmed and validated through both numerical simulations and practical experiments conducted with the Baxter robot.

关键词： Convergence Artificial neural networks Robots Manipulator dynamics Thermal stability Stability criteria Torque External disturbance neural network (NN) robot control predefined-time control

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A Predefined Time Constrained Adaptive Fuzzy control Method With Singularity-Free Switching for Uncertain Robots

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IEEE TRANSACTIONS ON FUZZY systems 2024年第5期32卷 2650-2662页

作者： Fan, Yanli Zhan, Hong Li, Yongming Yang, Chenguang South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China

In this article, an adaptive fuzzy logic system (FLS) predefined time tracking control technique is investigated for robot systems with constrained position errors. To this end, a practical predefined time control approach that integrates FLSs learning technique, predefined time stability criterion, tan-type constraint function, and the back stepping recursive design is constructed for the first time. Compared with finite/fixed-time control schemes, the upper bound of the convergence time no longer depends on initial conditions or complex design parameters. Instead, it can be predefined by making adjustments to a single relevant control parameter in advance. In order to avoid the violation of constraint boundaries by position tracking errors, a suitable tan-type barrier Lyapunov function is constructed, and rigorous stability is derived from constrained predefined time Lyapunov theory. The results demonstrate that the error signals converge to a small range near origin within the user-defined time. In addition, to compensate for unknown nonlinearity, a new adaptive update law associated with the convergence time control parameter is established. Simulations and experiments on the Baxter robot platform are carried out to confirm the validity and functionality of the devised controller.

关键词： Convergence Fuzzy logic Stability criteria control systems Adaptive systems Trajectory Task analysis Fuzzy robot control position error constraint predefined time (PT) control

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Event-Triggered Output Voltage Practical Tracking control for Uninterruptible Power Supply systems 35

Event-Triggered Output Voltage Practical Tracking Control fo...

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35th Chinese control and Decision Conference (CCDC)

作者： Wu, Ben Liu, Wei Sun, Weijie South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist Educ Sch Automat Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350334722

In this paper, by using the output regulation theory, we research the output voltage practical tracking problem for uninterruptible power supply systems under event-triggered control technique. By configuring the desired poles for a controllable pair and solving the regulator equations, a state feedback gain and a feedforward gain can be obtained for designing the control law. Further, under event-triggered mechanism, a control law that can be applied to a digital platform directly is designed to solve our problem. Moreover, it can be verified that the voltage tracking error can converge to a pre-specified neighborhood of the origin.

关键词： Uninterruptible power supply output regulation event-triggered control

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Fixed-Time Neural control of Robot Manipulator With Global Stability and Guaranteed Transient Performance

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第1期70卷 803-812页

作者： Zhu, Chengzhi Jiang, Yiming Yang, Chenguang South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control Changsha COLOR Peoples R China

Nowadays, due to many limitations in reality, the optimization of tracking precision and convergence time has attracted the attention of researchers in robotics community. In this article, a fixed-time adaptive neural network (NN) controller is proposed for unknown robot manipulators. A switching mechanism is integrated into the control design such that the semiglobal stability of the conventional NN control systems can be extended to global stability. The time-varying barrier Lyapunov function and the fixed-time control technique are incorporated into the controller design to guarantee the prescribed motion constraints and the fixed-time convergence simultaneously. Compared with some existing fixed-time NN control algorithms, the assumption that NN weight should be upper bounded can be relaxed in our work. Finally, the simulation and experiment studies are respectively carried out based on an unknown 2-degree of freedom robot and a Baxter robot to demonstrate the effectiveness and superiority of the proposed control scheme.

关键词： Robots Artificial neural networks Convergence Transient analysis Stability criteria Manipulators Trajectory Barrier Lyapunov functions (BLFs) fixed-time convergence global stability neural network (NN) control robot manipulator

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Deterministic Learning-Based Robust Practical Output Regulation for Second-Order Nonlinear systems

Deterministic Learning-Based Robust Practical Output Regulat...

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2024 International Annual Conference on Complex systems and Intelligent Science, CSIS-IAC 2024

作者： Hu, Qingxi Liu, Wei Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798331504755

This article intends to investigate the semi-global robust practical output regulation problem for a class of second-order nonlinear systems with parametric and non-parametric uncertainties based on deterministic learning method. First, by constructing the internal models, the robust output regulation problem is transformed into the semi-global robust practical stabilization problem. Second, we propose an adaptive neural control (ANC) strategy to solve the stabilization problem and utilize the radial basis function neural networks (RBF NNs) to identify unknown dynamic. Moreover, under the persistently exciting (PE) property, the unknown dynamics can be identified and the learned knowledge is saved as some constant NN weights. Finally, we design an deterministic learning-based adaptive neural control (DLANC) law with the learned knowledge to solve the output regulation problem and improve control performance. © 2024 IEEE.

关键词： Contrastive Learning

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ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Network for Multi-Step Prediction of Wastewater Quality Variables 7

ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Net...

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7th International Conference on Robotics, control and Automation Engineering, RCAE 2024

作者： Sun, Yue Liu, Yiqi South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering Guangzhou510640 China

ISBN: (纸本)9798350355642

Given the limitations of hardware sensors together with strong nonlinearities and nexus between variables in wastewater treatment processes (WWTPs), the measurement of key quality variables is usually inaccurate or even impossible. Moreover, due to the short data sampling period, the single-output single-step prediction model cannot meet the actual demand. Therefore, this paper proposes a new soft sensor, termed as the Echo State Multi-Scale Convolutional Neural Network (ES-MSCNN), designed for accurate multivariate multi-step prediction. ES-MSCNN first generates an echo state representation (ESR) containing temporal information through an echo state network (ESN). Subsequently, a convolutional neural network (CNN) is employed to avoid the shallowness of ESN and further extract high-dimensional feature information from the data. Besides, a multi-scale learning strategy is used in the convolutional module to capture multi-timescale features and improve the multivariate prediction accuracy. Validation on the benchmark simulation model 2 platform and comparison with several models indicate that the proposed ES-MSCNN has higher accuracy and stronger robustness in multi-step prediction tasks with multi-input and multi-output. © 2024 IEEE.

关键词： Convolutional neural networks

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An Online Strategy for Nonlinear Vector Autoregression 7

An Online Strategy for Nonlinear Vector Autoregression

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7th International Conference on Robotics, control and Automation Engineering, RCAE 2024

作者： Fang, Gang Liu, Yiqi South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering Guangzhou510640 China

ISBN: (纸本)9798350355642

Nonlinear vector autoregression (NVAR), as an effective alternative to traditional reservoir computing (RC), has garnered significant attention in the field of time series prediction in recent years. Unlike conventional RC, NVAR does not require the use of random sampling matrices to define the underlying recurrent neural network, and it features fewer hyperparameters. However, NVAR is an offline model, making it challenging to meet the real-time monitoring requirements of industrial processes. Inspired by the Woodbury matrix identity, this paper proposes an online NVAR that can update the model using data arriving in blocks. Subsequently, this study constructs a soft sensing model based on the online NVAR to perform real-time monitoring of industrial processes. Finally, the effectiveness of the proposed method is validated using a real-world dataset from a wastewater treatment plant. © 2024 IEEE.

关键词： Wastewater treatment

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