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Hardware-in-the-loop attitude control via a high-order sliding mode controller/observer

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2018年第10期232卷 1944-1960页

作者： Malekzadeh, Maryam Rezayati, Maryam Saboohi, Mobin Univ Isfahan Dept Mech Engn Esfahan *** Iran

In this study, a hardware-in-the-loop implementation of attitude control algorithms based on high-order sliding mode theory is performed and then both compared in terms of performance and functionality. The spacecraft simulator consists of a free-floating platform hinged on a spherical air-bearing support. In order to compensate for the unreliable performance of the rate gyros, a super-twisting sliding mode observer is incorporated to estimate the angular velocities fed back to the controller unit. The proposed scheme makes full use of novel sliding mode methods to alleviate the chattering effects without increasing the control effort;both controllers and observer are tuned to deal with the saturation of reaction wheels with respect to momentum and its rate. The ab initio simulations compared well with the simulator responses, implying that involved instruments including actuators and sensors have been properly emulated. This also proved that the external disturbances were modeled in a reliable manner. The robustness and effectiveness of the proposed scheme have been validated experimentally under extreme circumstances of disturbances and levels of uncertainties.

关键词： Spacecraft simulator high-order observer super-twisting algorithm terminal sliding mode reaction wheel saturation

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Attitude trajectory tracking of quadrotor UAV using super-twisting observer-based adaptive controller

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2021年第10期235卷 1146-1157页

作者： Chen, Ai-Jun Sun, Ming-Jian Wang, Zhen-Hua Feng, Nai-Zhang Shen, Yi Harbin Inst Technol Sch Astronaut Harbin 150000 Peoples R China

The successful implementation of high-level decision algorithm on quadrotor depends on the accurate trajectory tracking performance. In this paper attitude estimation and trajectory tracking control problem of quadrotor unmanned aerial vehicle (UAV) with endogenous and exogenous disturbance are considered, where the lumped disturbance characteristic does not have a probabilistic illustration but instead the dynamics are known to have a bound. The problem is handled by developing disturbance estimator and control strategy. In order to estimate lumped disturbance precisely, a globally finite time stable extended state observer is proposed based on super-twisting algorithm. Stability analysis and observer's parameters selection rule are discussed by using Lyapunov's stability theory. The proposed observer strategy achieves accurate observing performance of disturbance without increasing observer's order, and chattering effect is also reduced by applying super-twisting algorithm. Furthermore, a super-twisting sliding mode control law is proposed to guarantee the asymptotic convergence of the drone's orientation with respect to the reference. Finally, a numerical study based on simulations is presented to analyze the performance of proposed approach.

关键词： Trajectory tracking super-twisting algorithm sliding mode adaptive control

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Comparative investigations of nonlinear and linear observers for a highly manoeuvrable target in sliding mode guidance

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2017年第2期65卷 233-245页

作者： Wang, Y. Sun, M. Du, S. Chen, Z. AVIC LEHIUA Elect Technol Res Inst Wuxi 214063 Peoples R China Nankai Univ Coll Comp & Control Engn Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China Tshwane Univ Technol Dept Mech Engn ZA-0001 Pretoria South Africa

Target manoeuvre is one of the key factors affecting guidance accuracy. To intercept highly maneuverable targets, a second-order sliding-mode guidance law, which is based on the super-twisting algorithm, is designed without depending on any information about the target motion. In the designed guidance system, the target estimator plays an essential role. Besides the existing higher-order sliding-mode observer (HOSMO), a first-order linear observer (POLO) is also proposed to estimate the target manoeuvre, and this is the major contribution of this paper. The closed-loop guidance system can be guaranteed to be uniformly ultimately bounded (UUB) in the presence of the POLO. The comparative simulations are carried out to investigate the overall performance resulting from these two categories of observers. The results show that the guidance law with the proposed linear observer can achieve better comprehensive criteria for the amplitude of normalised acceleration and elevator deflection requirements. The reasons for the different levels of performance of these two observer-based methods are thoroughly investigated.

关键词： guidance law second-order sliding mode super-twisting algorithm linear observer target manoeuvre

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An intelligent fixed-time super-twisting sliding mode control of the uncertain hybrid mechanism

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ROBOTICA 2024年第10期42卷 3532-3551页

作者： Li, Xue Gao, Guoqin Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Jiangsu Peoples R China

In order to improve the global convergence performance of the super-twisting sliding mode control (STSMC) for the uncertain hybrid mechanism, especially in the high-speed scenario, and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes, an intelligent fixed-time super-twisting sliding mode control (IFTSTSMC) is proposed. Firstly, a fixed-time super-twisting sliding mode control (FTSTSMC) algorithm is designed by adding the exponential power terms with the fixed-time performance parameters in sliding variables and the transcendental function of the super-twisting algorithm in order to enhance the global convergence performance of the STSMC. Secondly, the existence condition of FTSTSMC for the uncertain hybrid mechanism is analyzed. The IFTSTSMC is designed by introducing RBF neural network to break through the limited range of uncertainties in FTSTSMC and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes. Then, the Lyapunov stability of the proposed method and the global fixed-time convergence of the system are proved theoretically. Finally, the effectiveness and superiority of the proposed control method are verified by the simulation and the automobile electro-coating conveying prototype experiment comparing with two classical finite-time sliding mode control methods.

关键词： fixed-time convergence hybrid mechanism neural network super-twisting algorithm sliding mode control

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Robust identification of unknown inputs in electrical stimulation of ex-vivo animal models

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2019年第0期52卷 103-110页

作者： Salgado, Ivan Alfaro-Ponce, Mariel Camacho, Oscar Chairez, Isaac Inst Politecn Nacl Ctr Innovac & Desarrollo Tecnol Comp Mexico City 07738 DF Mexico Tecnol Monterrey Sch Engn & Sci Calle Puente 222 Mexico City 14380 DF Mexico Inst Politecn Nacl Unidad Profes Interdisciplinaria Biotecnol Acueducto S-N Col Barrio La Laguna Ticoman Mexico

The non-parametric identification problem aims to estimate a suitable model based on the response produced by a given stimulus on an uncertain model. Complementary, input estimation considers a different problem where the model and the output are known. However, if neither model nor input is known, the identification problem seems to be more complicated. This is a major challenge in electrophysiological systems where the output signal can be measured, but the biological system is uncertain and the stimuli are unknown. The aim of this study was to introduce a novel methodology that may estimate the uncertain input in this more complex situation where the biological model of the system under analysis is uncertain and the input stimuli must be estimated accurately. A two step non-parametric identification scheme based on differential neural networks (DifNN) and the second order super-twisting sliding mode algorithm (STA) identified the input stimulus. The STA makes a robust exact estimation of the output signal. The DifNN produces an internal non-parametric mathematical modeling of the input-output relationship executed without the input information. Learning laws for both levels of DifNN modeling were presented as part of the uncertain input identification process. The estimation of uncertain visual stimulus applied over the retina in an ex-vivo avian model (EVAM) served to test the method proposed in this study. The output information corresponded to the electrophysiological voltage variation in the optical nerve. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Electrophysiological response Differential neural network Sliding mode super-twisting algorithm Unknown input observers

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A Robust Double Closed-Loop Control Scheme for PMLSM Drives

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IEEE ACCESS 2018年 6卷 62645-62654页

作者： Wang, Ming-Yi Niu, Yu-Jie Yang, Rui Tan, Qiang Jiang, Jia-Lin Li, Li-Yi Harbin Inst Technol Dept Elect Engn Harbin 150001 Heilongjiang Peoples R China

In this paper, a novel velocity and current double closed-loop control scheme applied to the permanent magnet linear synchronous motor (PMLSM) drives is proposed, and to suppress various uncertainties and disturbances, the advanced observation technique is introduced. First, as the inner loop, the current loop adopts the predictive current control method, which can increase the current control bandwidth and tracking precision. To improve the dynamic response and robustness of the velocity loop, a super-twisting sliding-mode control strategy is presented. Second, to overcome the parameter mismatch issue in the current loop and suppress different force disturbances in the velocity loop, their respective super-twisting sliding-mode observers are structured, and the corresponding estimated values are injected into the double closed loops by the feedforward method. At last, based on the precise PMLSM testing platform, the experimental studies are carried out to prove the effectiveness and correctness of the proposed scheme.

关键词： Permanent magnet linear synchronous motor (PMLSM) predictive current control sliding-mode control super-twisting algorithm observer

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Q-learning-based practical disturbance compensation control for hypersonic flight vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2023年第8期237卷 1916-1929页

作者： Li, Xu Zhang, Ziyi Ji, Yuehui Liu, Junjie Gao, Qiang Tianjin Univ Technol Sch Elect Engn & Automation Bldg 29391 Binshuixi Rd Tianjin Peoples R China Tianjin Key Lab Control Theory & Applicat Complica Tianjin Peoples R China

Aiming at the attitude control problem of hypersonic flight vehicle, a compound control strategy based on the disturbance compensation technique and Q-learning optimization is proposed. Firstly, a three-channel independent control scheme based on the super-twisting extended state observer (STESO) is proposed such that coupling among channels, uncertainties, and external disturbances can be estimated and compensated in real time. Secondly, the Q-learning mechanism is introduced to optimize the control parameters while keeping the control structure unchanged. Lastly, the convergence of the STESO is analyzed by the Lyapunov theory, and some numerical simulation results are carried out to verify the effectiveness of the proposed strategy. Compared with the traditional linear active disturbance rejection control, the proposed method can avoid a lot of manual parameter tuning process and also has better performance.

关键词： Hypersonic flight vehicle attitude control super-twisting algorithm extended state observer Q-learning

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super-twisting-based continuous neural networks modelling of second-order interconnected systems

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MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 2017年第2期23卷 156-176页

作者： Juarez-Lopez, Salvador Camacho, Oscar Chairez, Isaac Univ Autonoma Estado Mexico Dept Comp Ecatepec Mexico Inst Politecn Nacl Ctr Invest Comp Mexico City DF Mexico Inst Politecn Nacl Unidad Profes Interdisciplinaria Biotecnol Mexico City DF Mexico

The aim of this work was to design a non-parametric model of interconnected systems represented by uncertain second-order systems with incomplete information (only the generalized position vector is measurable). Artificial neural networks appeared to be a plausible alternative to get a non-parametric representation of the aforementioned interconnected systems. The modelling strategy used a set of spatial distributed second-order continuous neural networks (CNN). Each node in the interconnected system was represented as a second-order continuous neural network added by the super-twisting discontinuous sliding mode algorithm. The non-parametric modelling problem was reduced to design a feasible expression for the CNN weights in order to reproduce the states (including the generalized derivative of position vector) of all the nodes dynamics together and simultaneously. The adaptive laws for the CNN weights ensured the convergence of the CNN trajectories to the states of the uncertain interconnected system. To investigate the qualitative behaviour of the suggested methodology, two numerical examples were proposed. The first one represents the interconnection of three mass-spring-damper mechanical systems. The second example considers the problem of the non-parametric modelling problem for a wave partial differential equation. A set of three-dimensional graphic representations were used to demonstrate the identification abilities achieved by the CNN designed in this study for the second case.

关键词： Continuous neural networks interconnected systems super-twisting algorithm uncertain systems distributed systems

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Improved output-feedback second order sliding mode control design with implementation for underactuated slosh-container system having confined track length

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IET CONTROL THEORY AND APPLICATIONS 2017年第8期11卷 1316-1323页

作者： Thakar, Parth S. Bandyopadhyay, Bijnan Gandhi, Prasanna Indian Inst Technol Interdisciplinary Program Syst & Control Engn Bombay 400076 Maharashtra India Indian Inst Technol Dept Mech Engn Bombay 400076 Maharashtra India

In this study, a new second order sliding mode controller is designed through a novel non-linear sliding surface proposal for a slosh-container system. A linear sliding surface based controller using second order sliding mode is also derived for the comparison purpose. Both the controllers need only output measurements. The super-twisting algorithm is employed to ensure the finite time convergence of system trajectories to a second order sliding set. In sliding phase, the asymptotic stability of the system states to a desired origin is proved by proposing a new Lyapunov function for the system. A slosh-container system representing underactuated interconnected non-linear dynamics is considered. It is shown that how the consideration of limited track length of container affects the stability conditions. To generate a position trajectory for container from the control input signal, a new approach is employed for the implementation purpose. It is demonstrated through simulations and experimental results that the proposed controller based on the novel non-linear sliding surface outperforms the other controller based on the linear surface and it is more suitable on various practical aspects. This also validates the theoretical findings of the study.

关键词： variable structure systems feedback control system synthesis nonlinear control systems linear systems asymptotic stability Lyapunov methods interconnected systems sloshing nonlinear dynamical systems flow control improved output-feedback second order sliding mode control design underactuated slosh-container system confined track length nonlinear sliding surface linear sliding surface based controller super-twisting algorithm finite time convergence system trajectory second order sliding set asymptotic stability Lyapunov function underactuated interconnected nonlinear dynamics stability conditions position trajectory control input signal

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Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control

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NEURAL NETWORKS 2020年 126卷 163-169页

作者： Sun, Bo Wang, Shengbo Cao, Yuting Guo, Zhenyuan Huang, Tingwen Wen, Shiping Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Hunan Univ Coll Math & Econometr Changsha 410082 Hunan Peoples R China Texas A&M Univ Qatar Sci Program Doha 23874 Qatar

In the paper, exponential synchronization issue is considered for memristive neural networks (MNNs) with time-varying delays via quantized sliding-mode algorithm. Quantized Sliding-mode controller is introduced to ensure the slave system can be exponentially synchronized with the host system via the super-twisting algorithm, which has been proved in the main results. Quantization function consists of uniform quantizer and logarithmic quantizer. Simulation results are given with comparisons between two quantizers in the end. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Memristive neural network Time-varying delay super-twisting algorithm Quantization function Exponential synchronization

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