scholarly journals Preview Tracking Control for Continuous-Time Singular Interconnected Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Hao Xie ◽  
Fucheng Liao ◽  
Jiamei Deng

This paper proposes and investigates a problem of preview tracking control for a class of continuous-time singular interconnected systems. Firstly, the related items are deleted to obtain several isolated subsystems with low dimensions. An error system is constructed for each isolated subsystem so that the tracking error is included in the state vector of the error system; then, the tracking problem is transformed into a regulation problem. Secondly, the preview tracking controller is designed for each error system and obtained controllers are combined as the controller of the error system of the singular interconnected system. Thirdly, the Lyapunov function method is utilized to determine the constraints of the related terms so that the closed-loop system of the error system of the singular interconnected system is stable under the action of the controller obtained. Finally, the preview tracking controller of the singular interconnected system is obtained from the relationship between the error system and the original system. A numerical simulation algorithm for continuous-time singular systems is also proposed in this paper. The numerical simulation illustrates the effectiveness of the theoretical results.

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Fucheng Liao ◽  
Yujie Xu ◽  
Jiang Wu

This paper explicates a new method of designing a preview controller for a class of continuous-time systems. The augmented error system is constructed by the error system with the derivative of the tracking error signal, the state equation, and an identical equation of the derivative of the control input, which transforms a tracking problem into a regulation problem. Therefore, in the paper, the performance index contains the derivative of the control input. Based on the theory of optimal control, the regulator problem of the augmented error system is solved. Thus, the controller with preview compensation for the original system is deduced. The response speed of the closed-loop system is accelerated by the previewed demand output. A final numerical example is given to illustrate the validity of the proposed method.


2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Fucheng Liao ◽  
Yingxue Wu ◽  
Xiao Yu ◽  
Jiamei Deng

A finite-time bounded tracking control problem for a class of linear discrete-time systems subject to disturbances is investigated. Firstly, by applying a difference method to constructing the error system, the problem is transformed into a finite-time boundedness problem of the output vector of the error system. In fact, this is a finite-time boundedness problem with respect to the partial variables. Secondly, based on the partial stability theory and the research methods of finite-time boundedness problem, a state feedback controller formulated in form of linear matrix inequality is proposed. Based on this, a finite-time bounded tracking controller of the original system is obtained. Finally, a numerical example is presented to illustrate the effectiveness of the controller.


2020 ◽  
Vol 53 (3-4) ◽  
pp. 719-729
Author(s):  
Hao Xie ◽  
Fucheng Liao ◽  
Usman ◽  
Jiamei Deng

This article proposes and studies a problem of preview control for a type of discrete-time interconnected systems. First, adopting the technique of decentralized control, isolated subsystems are constructed by splitting the correlations between the systems. Utilizing the difference operator to the system equations and error vectors, error systems are built. Then, the preview controller is designed for the error system of each isolated subsystem. The controllers of error systems of isolated subsystems are aggregated as a controller of the interconnected system. Finally, by employing Lyapunov function method and the properties of non-singular M-matrix, the guarantee conditions for the existence of preview controllers for interconnected systems are given. The numerical simulation shows that the theoretical results are effective.


2008 ◽  
Vol 22 (19) ◽  
pp. 1859-1865 ◽  
Author(s):  
XINGYUAN WANG ◽  
DAHAI NIU ◽  
MINGJUN WANG

A nonlinear active tracking controller for the four-dimensional hyperchaotic Lorenz system is designed in the paper. The controller enables this hyperchaotic system to track all kinds of reference signals, such as the sinusoidal signal. The self-synchronization of the hyperchaotic Lorenz system and the different-structure synchronization with other chaotic systems can also be realized. Numerical simulation results show the effectiveness of the controller.


2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiao Yu ◽  
Fucheng Liao ◽  
Jiamei Deng

This paper considers the design of the robust preview controller for a class of uncertain discrete-time Lipschitz nonlinear systems. According to the preview control theory, an augmented error system including the tracking error and the known future information on the reference signal is constructed. To avoid static error, a discrete integrator is introduced. Using the linear matrix inequality (LMI) approach, a state feedback controller is developed to guarantee that the closed-loop system of the augmented error system is asymptotically stable with H∞ performance. Based on this, the robust preview tracking controller of the original system is obtained. Finally, two numerical examples are included to show the effectiveness of the proposed controller.


2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Bo Dong ◽  
Yuanchun Li

A novel decentralized reinforcement learning robust optimal tracking control theory for time varying constrained reconfigurable modular robots based on action-critic-identifier (ACI) and state-action value function (Q-function) has been presented to solve the problem of the continuous time nonlinear optimal control policy for strongly coupled uncertainty robotic system. The dynamics of time varying constrained reconfigurable modular robot is described as a synthesis of interconnected subsystem, and continuous time state equation andQ-function have been designed in this paper. Combining with ACI and RBF network, the global uncertainty of the subsystem and the HJB (Hamilton-Jacobi-Bellman) equation have been estimated, where critic-NN and action-NN are used to approximate the optimalQ-function and the optimal control policy, and the identifier is adopted to identify the global uncertainty as well as RBF-NN which is used to update the weights of ACI-NN. On this basis, a novel decentralized robust optimal tracking controller of the subsystem is proposed, so that the subsystem can track the desired trajectory and the tracking error can converge to zero in a finite time. The stability of ACI and the robust optimal tracking controller are confirmed by Lyapunov theory. Finally, comparative simulation examples are presented to illustrate the effectiveness of the proposed ACI and decentralized control theory.


Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 31
Author(s):  
Jichang Ma ◽  
Hui Xie ◽  
Kang Song ◽  
Hao Liu

The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h).


2018 ◽  
Vol 41 (10) ◽  
pp. 2726-2737 ◽  
Author(s):  
Xiao Yu ◽  
Fucheng Liao

In this paper, the problem of preview tracking control for a class of discrete-time nonlinear Lur’e systems is investigated. To overcome the difficulty arising from the sector nonlinearity, the original system is transformed into a linear parameter varying (LPV) system, and a new auxiliary method is introduced. The difference between the state vector and the auxiliary variable is used instead of the usual difference between system states. At the same time, the tracking error and the previewable future reference information are also considered in order to construct an augmented error system (AES). Then, the preview tracking problem is reduced to a robust control problem. Note that the AES is still an LPV system. Next, a state feedback controller is developed to ensure that the closed-loop system is asymptotically stable with H∞ guaranteed cost performance. Based on this, a preview controller for the original system is proposed. Finally, two numerical examples are provided to illustrate the effectiveness of the controller.


2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhao Xu ◽  
Shuzhi Sam Ge ◽  
Changhua Hu ◽  
Jinwen Hu

A novel adaptive tracking controller of fully actuated marine vessels is proposed with completely unknown dynamics and external disturbances. The model of dominant dynamic behaviors and unknown disturbances of the vessel are learned by a neural network in real time. The controller is designed and it unifies backstepping and adaptive neural network techniques with predefined tracking performance constraints on the tracking convergence rate and the transient and steady-state tracking error. The stability of the proposed adaptive tracking controller of the vessel is proven with a uniformly bounded tracking error. The proposed adaptive tracking controller is shown to be effective in the tracking control of marine vessels by simulations.


Author(s):  
Bo Xie ◽  
Bin Yao

The paper presents a new tracking control approach for a class of non-minimum phase linear systems. The proposed approach consists of two parts: trajectory planning and tracking controller design. The trajectory planning is solved as an optimization problem to improve the achievable transient performance under the fundamental constraints associated with perfect tracking of non-minimum phase systems. The recently proposed adaptive robust tracking controller for a class of non-minimum phase systems is then applied to guarantee that the tracking error dynamics can be stabilized with bounded internal states. The effectiveness of the proposed approach is illustrated through simulation on tracking control of a second order non-minimum phase linear system. Further works are underway to extend the proposed control strategy and trajectory design to a class of non-minimum phase nonlinear systems.


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