Adaptive fuzzy finite-time decentralized control for large-scale stochastic nonlinear systems

An adaptive finite-time decentralized control algorithm for a class of large-scale stochastic nonlinear systems is presented. The fuzzy logic system is used to estimate uncertain nonlinearities. One advantage of the developed scheme is that each subsystem only needs to update one adaptive parameter, which alleviates the burden of online estimation. The dynamic surface control method is employed to reduce the “complexity explosion” caused by the repetitive derivation of the intermediate variable function in the backstepping control scheme. A new decentralized controller is designed so that all signals of the controlled system are bounded and the tracking error converges to a small residual set around the origin within a finite time. The simulation results of a numerical example illustrate the effectiveness of the method.     

Composite adaptive finite-time fuzzy control for switched nonlinear systems with preassigned performance

This article investigates the composite adaptive fuzzy finite-time prescribed performance control issue of switched nonlinear systems subject to the unknown external disturbance and performance requirement. First, by utilizing the compensation and prediction errors, the piecewise switched composite parameter update law is employed to improve the approximation accuracy of the unknown nonlinearity. Then, the improved fractional-order filter and error compensation signal are introduced to cope with the influences caused by the explosive calculation and filter error, respectively. Meanwhile, the effect of the compound disturbances consisting of the unknown disturbances and approximation errors is reduced appropriately by designing the piecewise switched nonlinear disturbance observer. Moreover, stability analysis results prove that the proposed preassigned performance control scheme not only ensures that all states of the closed-loop system are practical finite-time bounded, but also that the tracking error converges to a preassigned area with a finite time. Ultimately, the simulation examples are given to demonstrate the effectiveness of the proposed control strategy.     

Adaptive control of feedforward nonlinear systems with uncertain time-varying parameters and an unknown time-varying delay in the input

In this paper, we consider a global regulation problem for a class of feedforward nonlinear systems. The key features of our considered system are identified by the presence of uncertain time-varying parameters associated with main diagonal states and input and an unknown time-varying delay in the input. Moreover, the growth rates of nonlinearity and the input-delay are only known to be finite. To solve our considered problem, we give a process to obtain a compact set that contains the allowed time-varying parameters. Then, we propose an adaptive controller which handles both unknown growth rate of nonlinearity and input-delay for system regulation. We carry out the rigorous system analysis and show the effectiveness of our proposed control scheme via an application example.     

Adaptive control of robots by linear time-varying dynamic position feedback

A simple linear adaptive controller based on a reduced-order observer is proposed to treat the trajectory tracking control problem of robotic systems with unknown parameters and external disturbances. The novelty of our result lies in the fact that the controller is a linear time-varying dynamic position feedback compensator; the dimension of the observer, which is constructed only to estimate the velocity signal, is half the dimension of the robotic system and the attraction region of the closed-loop error system can not be only arbitrarily preassigned but also explicitly constructed. Moreover, as the adaptation is switched off, the boundedness of all the variables is still guaranteed and the trajectories of the closed-loop error system converge to any desired region. If the regressor matrix satisfies the persistent excitation condition, then our control algorithm also guarantees that the estimated values of the unknown parameters converge to the true ones.     

Adaptive finite-time output feedback control for Markov jumping nonlinear systems

In this article, the problem of output feedback tracking control for uncertain Markov jumping nonlinear systems is studied. A finite-time control scheme based on command filtered backstepping and adaptive neural network (NN) technique is given. The finite-time command filter solves the problem of differential explosions for virtual control signals, the NN is utilized to approximate the uncertain nonlinear dynamics and the adaptive NN observer is applied to restructure the state of system. The finite-time error compensation mechanism is established to compensate the errors brought by filtering process. The proposed finite-time tracking control algorithm can ensure that the solution of the closed-loop system is practically finite-time stable in mean square. Two simulation examples are employed to demonstrate the effectiveness of the proposed control algorithm.     

Adaptive finite-time control for output regulation of nonlinear systems with completely unknown control directions

This article studies the finite-time output regulation problem for nonlinear strict-feedback systems with completely unknown control directions and unknown functions. First, according to the necessary conditions for the solvability of the output regulation problem, the output regulation problem of nonlinear strict-feedback systems and the external system is transformed into a stabilization problem of nonlinear systems. Second, an internal model with external signals is designed. Third, based on finite time, fuzzy control, output feedback control, and Nussbaum gain functions, the control law is designed so that all signals of the closed-loop system are the semi-global practically finite-time stable (SGPFS), and the tracking error converges to a small neighborhood of the origin in a finite-time. Finally, the proposed algorithm is applied to the finite-time tracking problem of Chua's oscillator system.     

Finite-time asynchronous control of linear time-varying switched systems

In the framework of asynchronous control, finite-time stabilization and finite-time bounded stabilization of linear time-varying (LTV) switched systems are investigated. A necessary and sufficient condition for finite-time stability (FTS) of LTV switched systems is proposed based on differential matrix inequalities (DMIs). Moreover, we extend the FTS result to the case of finite-time boundedness (FTB), and present a sufficient condition of FTB for LTV switched systems using the average dwell time method which can be used for design proposes. Then we turn to the design problem of asynchronously switched control to guarantee FTS or FTB of LTV switched systems and give a sufficient condition for the state feedback stabilization in the form of nonlinear DMIs (NDMIs). A numerical method is proposed to solve NDMIs approximately. Finally, two examples are given to illustrate the validity of the results.     

Adaptive finite-time tracking control for output-constrained nonlinear systems with non-strict-feedback structure

This article investigates the issue of adaptive finite-time tracking control for a category of output-constrained nonlinear systems in a non-strict-feedback form. First, by utilizing the structural characteristics of radial basis function neural networks (RBF NNs), a backstepping design method is extended from strict-feedback systems to a kind of more general systems, and NNs are employed to approximate unknown nonlinear functions. In addition, the system output is constrained to the specified region by applying the barrier Lyapunov function (BLF) technique. Furthermore, the finite-time stability of the system is proved by employing the Bhat and Bernstein theorem. As a result, an adaptive finite-time tracking control scheme for the output-constrained nonlinear systems with non-strict-feedback structure is proposed by applying RBF NNs, BLF, finite-time stability theory, and adaptive backstepping technique. It is demonstrated the finite-time stability of the system, the prescribed convergence of the system output and tracking error, the boundedness of adaptive parameters and state variables. Finally, a simulation example is implemented to illustrate the effectiveness of the presented neural control scheme.     

Adaptive fuzzy finite-time quantized control for stochastic nonlinear systems with full state constraints

This article studies the adaptive fuzzy finite-time quantized control problem of stochastic nonlinear nonstrict-feedback systems with full state constraints. During the control design process, fuzzy logic systems are used to identify the unknown nonlinear functions, integral barrier Lyapunov functions are employed to solve the state constrained problem. In the frame of backstepping design, an adaptive fuzzy finite-time quantized control scheme is developed. Based on the stochastic finite-time Lyapunov stability theory, it can be guaranteed that the closed-loop system is semiglobal finite-time stable in probability, and the tracking errors converge to a small neighborhood of the origin in a finite time. Finally, two simulation examples are provided to testify the effectiveness of the developed control scheme.     

Adaptive finite-time tracking control for parameterized nonlinear systems with full state constraints

In this article, the issue of adaptive finite-time dynamic surface control (DSC) is discussed for a class of parameterized nonlinear systems with full state constraints. Using the property of logarithmic function, a one-to-one nonlinear mapping is constructed to transform a constrained system into an unconstrained system with the same structure. The nonlinear filter is constructed to replace the first-order linear filter in the traditional DSC, and the demand on the filter time constant is reduced. Based on finite-time stable theory and using modified DSC, the finite-time controller is designed via DSC. Theoretical analysis shows that all the signals in the closed-loop system are semiglobal practical finite-time stable. Furthermore, none of the states are outside the defined open set. In the end, simulation results are presented to demonstrate the effectiveness of the proposed control schemes with both linear filters and nonlinear filters.     

Adaptive fuzzy finite-time consensus tracking for multiple Euler-Lagrange systems with unknown control directions

In this paper, the problem of adaptive fuzzy finite-time consensus tracking control for multiple Euler-Lagrange systems (ELSs) with uncertain dynamics and unknown control directions (UCDs) is investigated. The computational complexity problem in conventional backstepping is avoided by using finite-time command filter (FTCF), and the error in the filtering process is eliminated through error compensation signals. The fuzzy logic system combined with the adaptive control technique is applied to approximate and estimate the unknown nonlinear dynamics of ELS. The Nussbaum function-based continuous and nonsmooth input control torque is established to eliminate the influence of UCDs, and the proposed control scheme can guarantee the consensus tracking errors converge to the desired neighborhood of the origin within a finite time. Numerical simulation is used to test the effectiveness of the given algorithm.     

基于任务空间双模式结合的遥操作机器人系统

遥操作技术是空间、深海以及遥远距离等特殊环境下机器人完成作业任务的关键技术。构建了基于PHANTOM手控器及MOTOMAN机器人的遥操作机器人系统平台,能够实现对象的主从抓取、自主抓取以及两者结合抓取等几种抓取任务,并完成人性化用户界面的设计。针对遥操作机器人主从双边存在的时延问题,设计了基于任务空间检测的自主模式和主从模式相结合的遥操作机器人系统工作模式,相比于传统的遥操作机器人系统所采用的工作模式更加省时高效。实验结果表明所提出的工作模式能够满足存在通信时延情况下遥操作机器人完成任务的需要。     

Novel fuzzy adaptive finite-time nonsmooth control for uncertain nonlinear systems

In this article, a novel fuzzy adaptive finite-time nonsmooth controller is developed to handle the finite-time tracking problem for a class of uncertain nonlinear systems. Different from traditional fuzzy adaptive approximation methods, proposed method contains only one adaptive parameter, no matter how many states there are in the system. By constructing a new Lyapunov function with prescribed performance bound, the transient and steady performances of control system can be ensured. Further, based on a criterion of finite-time semiglobal practical stability and backstepping technology, a novel fuzzy adaptive finite-time nonsmooth control method is designed. It can be demonstrated that proposed control can effectively ensure tracking error tends to small neighborhood in a finite time. Finally, two examples have been simulated by the proposed control method, and it shows effective tracking performance.     

Adaptive finite-time tracking control for robotic manipulators with funnel boundary

The finite-time tracking control problem with the output-constraint property of robotic manipulators subjected to system uncertainties is addressed. Specifically, the radial basis function neural network is employed to compensate for system uncertainties. The finite-time stability theorem is used for the backstepping design process, by which the limit of the settling time is set. A funnel boundary is used to limit the output overshoot. The proposed controller guarantees that all the signals are semi-globally practically finite-time bounded, while the tracking errors are enveloped by the funnel boundary. The performance of the proposed control method is illustrated by a numerical simulation of a 3-DOF manipulator. It is shown that the tracking errors are bounded by prescribed funnel boundaries. In the meantime, the manipulator is stabilized within a finite period of time.     

基于凸组合方法的变时滞电力系统稳定性判据

在广域环境下,控制信号在传输和处理过程中产生的时滞现象会对电力系统的稳定运行产生难以忽略的负面影响。给出了一种基于凸组合方法的变时滞电力系统稳定性判别方法。构造了一种新的Lyapunov-Krasovskii泛函,通过证明其正定性,从而放松了对泛函参数项的约束。使用自由权矩阵和凸组合方法对泛函的导函数进行估计,得到了基于线性矩阵不等式的变时滞电力系统的稳定性判据,降低了现有结论的保守性。通过实例仿真表明,所使用的方法在保守性上优于现存的一些方法。     

Adaptive predictive control of systems with time-varying time delay

This paper discusses three different methodologies for performing adaptive predictive control in the presence of unknown or time-varying time delays. The first methodology is the well-known extended B-polynomial. The second methodology consists of describing the process dynamics by a network of Laguerre functions. The third methodology, named the variable regressor estimator (VRE), consists of an explicit time delay estimator running in parallel with a standard recursive estimator. Theoretical as well as practical aspects of these methodologies will be discussed. Finally, our industrial experience with these is discussed.     

Adaptive control of pure-feedback nonlinear systems with full-state time-varying constraints and unmodeled dynamics

This paper focuses on the problem of adaptive control for a class of pure-feedback nonlinear systems with full-state time-varying constraints and unmodeled dynamics. By introducing a one-to-one nonlinear mapping, the constrained pure-feedback nonlinear system with state and input unmodeled dynamics is transformed into unconstrained pure-feedback system. The controller design based on the transformed novel system is proposed by using a modified dynamic surface control method. Dynamic signal and normalization signal are designed to handle dynamical uncertain terms and input unmodeled dynamics, respectively. By adding nonnegative normalization signal into the whole Lyapunov function and using the introducing compact set in the stability analysis, all signals in the whole system are proved to be semiglobally uniformly ultimately bounded, and all states can obey the time-varying constraint conditions. A numerical example is provided to demonstrate the effectiveness of the proposed approach.     

网络控制系统的滑模变结构预估控制器的设计

网络闭环控制系统中长时延存在的情况下 ,在状态预估的基础上 ,设计出的滑模控制器使闭环系统具有较好的性能。对直流电机的仿真结果证明了该方法的有效性     

Adaptive finite-time consensus tracking control for nonlinear multiagent systems in nonstrict feedback form with full-state constraints

In this article, the fuzzy adaptive finite-time consensus tracking control problem for nonstrict feedback nonlinear multiagent systems with full-state constraints is studied. The finite-time control based on command filtered backstepping is proposed to guarantee the finite-time convergence and eliminate the explosion of complexity problem caused by backstepping process, and the errors in the filtering process are compensated by using error compensation mechanism. Furthermore, based on the fuzzy logic systems, the uncertain nonlinear dynamics are approximated and the problem of state variables in nonstrict feedback form is solved by using the property of basis functions. The barrier Lyapunov functions are introduced to guarantee that all system states and compensated tracking error signals are constrained in the designed regions. A simulation example is given to verify the superiority of the proposed algorithm.     

一类时变互联不确定大系统的鲁棒分散自适应控制

研究了一类时变不确定大系统的分散镇定问题,其不确定性是范数有界不确定性和不确定性的界未知,且不满足匹配条件。基于Lyapunov稳定性理论,设计了两类分散自适应无记忆状态反馈控制,给出了系统状态渐近稳定的充分条件。通过数值例子验证了所提方法的有效性。