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Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance Research Articles

Xuerao Wang, Qingling Wang, Changyin Sun,wangxuerao@seu.edu.cn,qlwang@seu.edu.cn,cysun@seu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 986-1001 doi: 10.1631/FITEE.2000145

Abstract: In this paper, an observer-based adaptive tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without . A novel finite-time is constructed to estimate the system uncertainties and external disturbances. To guarantee the , an error transformation is applied to transfer the time-varying constraints into a constant constraint. Then, by employing a barrier Lyapunov function and the backstepping technique, an observer-based tracking control strategy is presented. It is proven that using the proposed algorithm, all the closed-loop signals are bounded, and the tracking errors satisfy the predefined time-varying performance requirements. Finally, simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

Keywords： 自适应跟踪控制；预设性能；输入饱和；干扰观测器；神经网络    

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Decentralized fault-tolerant cooperative control of multipleUAVs with prescribed attitude synchronization tracking performance under directed communication topology Regular Papers

Zi-quan YU, Zhi-xiang LIU, You-min ZHANG, Yao-hong QU, Chun-yi SU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5,   Pages 685-700 doi: 10.1631/FITEE.1800569

Abstract:

In this paper, a decentralized fault-tolerant cooperative control scheme is developed for multiple unmanned aerial vehicles (UAVs) in the presence of actuator faults and a directed communication network. To counteract in-flight actuator faults and enhance formation flight safety, neural networks (NNs) are used to approximate unknown nonlinear terms due to the inherent nonlinearities in UAV models and the actuator loss of control effectiveness faults. To further compensate for NN approximation errors and actuator bias faults, the disturbance observer (DO) technique is incorporated into the control scheme to increase the composite approximation capability. Moreover, the prediction errors, which represent the approximation qualities of the states induced by NNs and DOs to the measured states, are integrated into the developed fault-tolerant cooperative control scheme. Furthermore, prescribed performance functions are imposed on the attitude synchronization tracking errors, to guarantee the prescribed synchronization tracking performance. One of the key features of the proposed strategy is that unknown terms due to the inherent nonlinearities in UAVs and actuator faults are compensated for by the composite approximators constructed by NNs, DOs, and prediction errors. Another key feature is that the attitude synchronization tracking errors are strictly constrained within the prescribed bounds. Finally, simulation results are provided and have demonstrated the effectiveness of the proposed control scheme.

Keywords： Fault-tolerant control     Decentralized control     Prescribed performance     Unmanned aerial vehicle     Neural network     Disturbance observer     Directed topology    

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Constant-gain nonlinear adaptive observers revisited: an application to chemostat systems Research Articles

Jorge A. Torres, Arno Sonck, Sergej Čelikovský, Alma R. Dominguez,jtorres@ctrl.cinvestav.mx,gsonck@ctrl.cinvestav.mx,celikovs@utia.cas.cz,adomin@cinvestav.mx

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 1,   Pages 1-140 doi: 10.1631/FITEE.2000368

Abstract: This study deals with constant-gain for nonlinear systems, for which relatively few solutions are available for some particular cases. We introduce an asymptotic observer of constant gain for nonlinear systems that have linear input. This allows the observer design to be formulated within the linear matrix inequality paradigm provided that a strictly positive real condition between the input disturbance and the output is fulfilled. The proposed observer is then applied to a large class of nonlinear dynamical systems that are widely used in the fermentation process, cell cultures, medicine, etc. In fact, under standard practical assumptions, the necessary change of the state coordinates exists, allowing use of the constant-gain observer. Finally, the developed theory is illustrated by estimating pollutant concentration in a wastewater treatment facility.

Keywords： Nonlinear observers     Adaptive observers     Coordinate change     Chemostat     Pollutant observation    

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Fuzzy impedance control of an electro-hydraulic actuator with an extended disturbance observer Regular Papers-Research Articles

Ming-jie LI, Jian-hua WEI, Jin-hui FANG, Wen-zhuo SHI, Kai GUO

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1221-1233 doi: 10.1631/FITEE.1800155

Abstract: In this paper, we deal with both velocity control and force control of a single-rod electro-hydraulic actuator subject to external disturbances and parameter uncertainties. In some implementations, both velocity control and force control are required. Impedance control and an extended disturbance observer are combined to solve this issue. Impedance control is applied to regulate the dynamic relationship between the velocity and output force of the actuator, which can help avoid impact and keep a proper contact force on the environment or workpieces. Parameters of impedance rules are regulated by a fuzzy algorithm. An extended disturbance observer is employed to account for external disturbances and parameter uncertainties to achieve an accurate velocity tracking. A detailed model of load force dynamics is presented for the development of the extended disturbance observer. The stability of the whole system is analyzed. Experimental results demonstrate that the proposed control strategy has not only a high velocity tracking performance, but also a good force adjustment performance, and that it should be widely applied in construction and assembly.

Keywords： Fuzzy control     Impedance control     Disturbance observer     Parameter uncertainties     Electro-hydraulic actuator    

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Observer-based control for fractional-order singular systems with order Research Article

Bingxin LI, Xiangfei ZHAO, Xuefeng ZHANG, Xin ZHAO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1862-1870 doi: 10.1631/FITEE.2200294

Abstract: In this paper, for fractional-order with order (0<<1) and is studied. On the basis of the Smith predictor and approximation error, the system with is approximately equivalent to the system without . Furthermore, based on the (LMI) technique, the necessary and sufficient condition of is proposed. Since the condition is a nonstrict LMI, including the equality constraint, it will lead to some trouble when solving problems using toolbox. Thus, the strict LMI-based condition is improved in the paper. Finally, a numerical example and a direct current motor example are given to illustrate the effectiveness of the strict LMI-based condition.

Keywords： Observer-based control     Singular systems     Fractional order     Input delay     Linear matrix inequality    

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Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters Research

Fan XU, Jin WANG, Guo-dong LU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1316-1327 doi: 10.1631/FITEE.1601707

Abstract:

The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate translational base frame parameters, internal force, modeling uncertainties, joint friction, and external disturbances. A radial basis function neural network is adopted for all kinds of dynamical estimation, including undesired internal force. To validate the effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this approach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Specialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the face of inaccurate base frame parameters and the aforementioned uncertainties.

Keywords： Cooperative manipulators     Neural networks     Inaccurate translational base frame     Adaptive control     Robust control    

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Disturbance rejection via iterative learning controlwith a disturbance observer for active magnetic bearing systems None

Ze-zhi TANG, Yuan-jin YU, Zhen-hong LI, Zheng-tao DING

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 1,   Pages 131-140 doi: 10.1631/FITEE.1800558

Abstract:

Although standard iterative learning control (ILC) approaches can achieve perfect tracking for active magnetic bearing (AMB) systems under external disturbances, the disturbances are required to be iteration-invariant. In contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variant disturbances that are in different channels from the control inputs. A disturbance observer based ILC scheme is proposed that consists of a universal extended state observer (ESO) and a classical ILC law. Using only output feedback, the proposed control approach estimates and attenuates the disturbances in every iteration. The convergence of the closed-loop system is guaranteed by analyzing the contraction behavior of the tracking error. Simulation and comparison studies demonstrate the superior tracking performance of the proposed control approach.

Keywords： Active magnetic bearings (AMBs)     Iterative learning control (ILC)     Disturbance observer    

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Research on Tracing Evaluation System in Virtual Enterprise Based on Neural Network

Wang Shuo,Tang Xiaowo

Strategic Study of CAE 2003, Volume 5, Issue 4,   Pages 65-69

Abstract:

The paper designed tracing evaluation index system in virtual enterprise and established neural network trace evaluation model. As a result, it was simple and nicety than traditional method, so it had wider application foreground.

Keywords： virtual enterprise     neural network     trace evaluation     system    

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Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals None

Jing-lin HU, Xiu-xia SUN, Lei HE, Ri LIU, Xiong-feng DENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 9,   Pages 1086-1097 doi: 10.1631/FITEE.1601801

Abstract:

A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.

Keywords： Multiagent system     Adaptive output feedback     Formation tracking     Hysteretic quantizer    

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Current Source Active Power Filter Control UsingNeural Network Technologies

Wang Ping,Zhang Ke, Xu Huijun

Strategic Study of CAE 2007, Volume 9, Issue 1,   Pages 40-43

Abstract:

In this paper,the application of neural network to power converter control is discussed.A new hysteresis comparator constructed by using neural network is introduced.Hysteresis band control is an effective and simple control method.It can easily run without many system parameters.But the switch frequency of system is not fixed.So it not only makes the system unstable but also may lessen the life span of the switches. The control method that combines the neural network technology with the hysteresis band technology has a high performance in response of current.Through training the neural network can learn the control rules by itself and can replace the real hysteresis comparator in power converter control.The computer simulation results are given in this paper and they can demonstrate the effectiveness of the proposed method.The neural network is realized by using DSP.

Keywords： source filter     neural network     hysteresis comparator    

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Active fault-tolerant tracking control of a quadrotorwith model uncertainties and actuator faults None

Yu-jiang ZHONG, Zhi-xiang LIU, You-min ZHANG, Wei ZHANG, Jun-yi ZUO

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 1,   Pages 95-106 doi: 10.1631/FITEE.1800570

Abstract:

This paper presents a reliable active fault-tolerant tracking control system (AFTTCS) for actuator faults in a quadrotor unmanned aerial vehicle (QUAV). The proposed AFTTCS is designed based on a well-known model reference adaptive control (MRAC) framework that guarantees the global asymptotic stability of a QUAV system. To mitigate the negative impacts of model uncertainties and enhance system robustness, a radial basis function neural network is incorporated into the MRAC scheme for adaptively identifying the model uncertainties online and modifying the reference model. Meanwhile, actuator dynamics are considered to avoid undesirable performance degradation. Furthermore, a fault detection and diagnosis estimator is constructed to diagnose lossof-control-effectiveness faults in actuators. Based on the fault information, a fault compensation term is added to the control law to compensate for the adverse effects of actuator faults. Simulation results show that the proposed AFTTCS enables the QUAV to track the desired reference commands in the absence/presence of actuator faults with satisfactory performance.

Keywords： Model reference adaptive control     Neural network     Quadrotor     Fault-tolerant control     Fault detection and diagnosis    

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Adaptive neural network based boundary control of a flexible marine riser system with output constraints Research Article

Chuyang YU, Xuyang LOU, Yifei MA, Qian YE, Jinqi ZHANG,sunrise_ycy@stu.jiangnan.edu.cn,Louxy@jiangnan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1229-1238 doi: 10.1631/FITEE.2100586

Abstract: In this study, we develop an adaptive based method for a flexible with unknown nonlinear disturbances and s to suppress vibrations. We begin with describing the dynamic behavior of the riser system using a distributed parameter system with s. To compensate for the effect of nonlinear disturbances, we construct a based ler using a radial basis to reduce vibrations. Under the proposed ler, the state of the riser is guaranteed to be uniformly bounded based on the Lyapunov method. The proposed methodology provides a way to integrate s into for other flexible robotic manipulator systems. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed control method.

Keywords： Marine riser system     Partial differential equation     Neural network     Output constraint     Boundary control     Unknown disturbance    

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Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game Research Article

Hongyang LI, Qinglai WEI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1010-1019 doi: 10.1631/FITEE.2200010

Abstract: This paper presents a novel method for with . The multi-agent game theory is introduced to transform the problem into a multi-agent . Then, the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman (HJB) equations with nonquadratic input energy terms. A novel method is presented to obtain the Nash equilibrium solution without the system models, and the critic neural networks (NNs) and actor NNs are introduced to implement the presented method. Theoretical analysis is provided, which shows that the iterative control laws converge to the Nash equilibrium. Simulation results show the good performance of the presented method.

Keywords： Optimal synchronization control     Multi-agent systems     Nonzero-sum game     Adaptive dynamic programming     Input saturation     Off-policy reinforcement learning     Policy iteration    

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Pressure in Gas-assisted Injection Molding

Ou Changjin

Strategic Study of CAE 2007, Volume 9, Issue 5,   Pages 27-32

Abstract:

In this study,  an effective control method and strategy based on fuzzy neural network has been developed for gas injection pressure accurate control in gas-assisted injection. A fuzzy neural network controller with five layers and its control algorithm are established.  The learning ability of neural network is used to optimize the rules of the fuzzy logic so as to improve the adaptability of system.  The simulation of the system capability and three segmental injected pressure control are carried out under the environment of MATLAB and the results show that this theoretic model is feasible, and the control system has good characteristics and control action.

Keywords： gas-assisted injection molding     fuzzy neural network     gas-injection pressure control    

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Adaptive tracking control for air-breathing hypersonic vehicles with state constraints Article

Gong-jun LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 599-614 doi: 10.1631/FITEE.1500464

Abstract: We investigate the adaptive tracking problem for the longitudinal dynamics of state-constrained airbreathing hypersonic vehicles, where not only the velocity and the altitude, but also the angle of attack (AOA) is required to be tracked. A novel indirect AOA tracking strategy is proposed by viewing the pitch angle as a new output and devising an appropriate pitch angle reference trajectory. Then based on the redefined outputs (i.e., the velocity, the altitude, and the pitch angle), a modified backstepping design is proposed where the barrier Lyapunov function is used to solve the state-constrained control problem and the control gain of this class of systems is unknown. Stability analysis is given to show that the tracking objective is achieved, all the closed-loop signals are bounded, and all the states always satisfy the given constraints. Finally, numerical simulations verify the effectiveness of the proposed approach.

Keywords： Hypersonic vehicle     Constraints     Output redefinition     Barrier Lyapunov function    

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Title Author Date Type Operation

Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance

Xuerao Wang, Qingling Wang, Changyin Sun,wangxuerao@seu.edu.cn,qlwang@seu.edu.cn,cysun@seu.edu.cn

Journal Article

Decentralized fault-tolerant cooperative control of multipleUAVs with prescribed attitude synchronization tracking performance under directed communication topology

Zi-quan YU, Zhi-xiang LIU, You-min ZHANG, Yao-hong QU, Chun-yi SU

Journal Article

Constant-gain nonlinear adaptive observers revisited: an application to chemostat systems

Jorge A. Torres, Arno Sonck, Sergej Čelikovský, Alma R. Dominguez,jtorres@ctrl.cinvestav.mx,gsonck@ctrl.cinvestav.mx,celikovs@utia.cas.cz,adomin@cinvestav.mx

Journal Article

Fuzzy impedance control of an electro-hydraulic actuator with an extended disturbance observer

Ming-jie LI, Jian-hua WEI, Jin-hui FANG, Wen-zhuo SHI, Kai GUO

Journal Article

Observer-based control for fractional-order singular systems with order

Bingxin LI, Xiangfei ZHAO, Xuefeng ZHANG, Xin ZHAO

Journal Article

Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters

Fan XU, Jin WANG, Guo-dong LU

Journal Article

Disturbance rejection via iterative learning controlwith a disturbance observer for active magnetic bearing systems

Ze-zhi TANG, Yuan-jin YU, Zhen-hong LI, Zheng-tao DING

Journal Article

Research on Tracing Evaluation System in Virtual Enterprise Based on Neural Network

Wang Shuo,Tang Xiaowo

Journal Article

Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals

Jing-lin HU, Xiu-xia SUN, Lei HE, Ri LIU, Xiong-feng DENG

Journal Article

Current Source Active Power Filter Control UsingNeural Network Technologies

Wang Ping,Zhang Ke, Xu Huijun

Journal Article

Active fault-tolerant tracking control of a quadrotorwith model uncertainties and actuator faults

Yu-jiang ZHONG, Zhi-xiang LIU, You-min ZHANG, Wei ZHANG, Jun-yi ZUO

Journal Article

Adaptive neural network based boundary control of a flexible marine riser system with output constraints

Chuyang YU, Xuyang LOU, Yifei MA, Qian YE, Jinqi ZHANG,sunrise_ycy@stu.jiangnan.edu.cn,Louxy@jiangnan.edu.cn

Journal Article

Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game

Hongyang LI, Qinglai WEI

Journal Article

Pressure in Gas-assisted Injection Molding

Ou Changjin

Journal Article

Adaptive tracking control for air-breathing hypersonic vehicles with state constraints

Gong-jun LI

Journal Article