共查询到20条相似文献，搜索用时 15 毫秒
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This article devises a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems containing partially unmeasurable states and time-varying output constraints. Radial basis function neural networks, as function approximators, are utilized to model the unknown functions, and the partially unmeasurable states of the systems are estimated by a reduced-order observer. By constructing a transferred function, system outputs are directly constrained in a time-varying constraint bound. Meanwhile, the first-order sliding mode differentiators are utilized to reduce the computational burden caused by the repeated differentiations of virtual controllers. Under the Lyapunov function and the fixed-time theory, the decentralized adaptive fixed-time controllers are constructed. It is proved that the closed-loop systems are fixed-time stable and the output signals are restricted in the bounded compact set. Finally, two simulation examples demonstrate the validity of the proposed control scheme. 相似文献
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吴锦娃;刘勇华;苏春翌;鲁仁全 《自动化学报》2024,50(5):1015-1023
针对一类具有不确定控制增益的严格反馈系统, 提出一种基于命令滤波反推技术的自适应神经网络控制方法. 该方法采用神经网络对系统中的未知非线性函数进行逼近, 并引入命令滤波反推技术克服“计算膨胀”的问题. 与现有的命令滤波反推控制文献相比, 本文通过构造自适应误差补偿系统, 同时消除滤波器产生的边界层误差和不确定控制增益对系统性能造成的影响. 仿真结果验证了所提控制方法的有效性. 相似文献
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针对一类具有复杂非线性和输出约束的输出反馈非线性系统提出一个自适应事件触发跟踪控制方法, 其中系统的复杂非线性由与输出相关的非线性函数和不可测状态耦合构成. 为了估计不可测的状态变量并补偿复杂的系统非线性, 首先, 利用增益调节技术设计一个动态高增益, 并设计一个修改的高增益K-滤波器; 然后, 对跟踪误差引入性能约束函数并设计自适应动态面输出反馈控制器. 为控制器设计一个触发条件来确定事件触发控制信号, 使得系统的通信资源得到有效节约. 研究结果表明, 利用所提出方案, 系统的复杂非线性和触发误差均能被有效处理, 输出约束能被保证, 并且跟踪误差能够收敛到一个任意小的紧集. 最终, 所提出控制方案的有效性通过高超音速飞机仿真实验得到了验证. 相似文献
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Zhuwu Shao Yujuan Wang Zeqiang Li Yongduan Song 《IEEE/CAA Journal of Automatica Sinica》2024,11(3):569-580
This work proposes an event-triggered adaptive control approach for a class of uncertain nonlinear systems under irregular constraints. Unlike the constraints considered in most existing papers, here the external irregular constraints are considered and a constraints switching mechanism(CSM) is introduced to circumvent the difficulties arising from irregular output constraints. Based on the CSM, a new class of generalized barrier functions are constructed, which allows the control results to be ... 相似文献
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In this paper, an observer-based event-triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event-triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event-triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed-loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm. 相似文献
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In this paper, an adaptive optimal control strategy is proposed for a class of strict‐feedback nonlinear systems with output constraints by using dynamic surface control. The controller design procedure is divided into two parts. One is the design of feedforward controller and the other is the design of optimal controller. To guarantee the satisfaction of output constraints in feedforward controller, nonlinear mapping is utilized to transform the constrained system into an unconstrained system. Neural‐network based adaptive dynamic programming algorithm is employed to approximate the optimal cost function and the optimal control law. By theoretical analysis, all the signals in the closed‐loop system are proved to be semi‐globally uniformly ultimately bounded and the output constraints are not violated. A numerical example illustrates the effectiveness of the proposed scheme. 相似文献
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The fixed time event-triggered control for high-order nonlinear uncertain systems with time-varying full state constraints is investigated in this paper. First, the event-triggered control (ETC) mechanism is introduced to reduce the data transmission in the communication channel. In consideration of the physical constraints and engineering requirements, time-varying barrier Lyapunov function (BLF) is deployed to make all the system states confined in the given time-varying constraints. Then, the radial basis function neural networks (RBF NNs) are used to approximate the unknown nonlinear terms. Further, the fixed time stability strategy is deployed to make the system achieve semiglobal practical fixed time stability (SPFTS) and the convergence time is independent of the initial conditions. Finally, the proposed control scheme is verified by two simulation examples. 相似文献
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In this article, an optimal command-filtered backstepping control approach is proposed for uncertain strict-feedback nonlinear multi-agent systems (MASs) including output constraints and unmodeled dynamics. One-to-one nonlinear mapping (NM) is utilized to recast constrained systems as corresponding unrestricted systems. A dynamical signal is applied to cope with unmodeled dynamics. Based on dynamic surface control (DSC), the feedforward controller is designed by introducing error compensating signals. The optimal feedback controller is produced applying adaptive dynamic programming (ADP) and integral reinforcement learning (IRL) techniques in which neural networks are utilized to approximate the relevant cost functions online with established weight updating laws. Therefore, the entire controller, including feedforward and feedback controllers, not only ensures that all signals in the closed-loop systems are cooperative semi-globally uniformly ultimately bounded (SGUUB) and the outputs maintain in the provided time-varying constraints, but also makes sure that the cost functions achieve minimization. A simulation example is presented to illustrate the feasibility of the proposed control algorithm. 相似文献
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The problem of adaptive output feedback stabilisation is addressed for a more general class of non-strict-feedback stochastic nonlinear systems in this paper. The neural network (NN) approximation and the variable separation technique are utilised to deal with the unknown subsystem functions with the whole states. Based on the design of a simple input-driven observer, an adaptive NN output feedback controller which contains only one parameter to be updated is developed for such systems by using the dynamic surface control method. The proposed control scheme ensures that all signals in the closed-loop systems are bounded in probability and the error signals remain semi-globally uniformly ultimately bounded in fourth moment (or mean square). Two simulation examples are given to illustrate the effectiveness of the proposed control design. 相似文献
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针对输出受不对称时变约束的不确定高阶严反馈系统, 提出一种基于全驱系统方法的高阶自适应动态面输出约束控制方法. 所研究的高阶严反馈系统, 每个子系统都是高阶形式, 通过非线性转换函数将原输出约束系统转换为新的无约束系统, 从而将原系统输出约束问题转化为新系统输出有界的问题. 进一步结合全驱系统方法和自适应动态面控制, 直接将每个高阶子系统作为一个整体进行控制器设计, 而不需要将其转化为一阶系统形式, 有效简化了设计步骤; 同时通过引入一系列低通滤波器来获得虚拟控制律的高阶导数, 以代替复杂的微分运算. 基于Lyapunov稳定性理论证明闭环系统所有信号是一致最终有界的, 系统输出在满足约束的条件下能有效跟踪期望的参考信号, 且可通过调整参数使得系统跟踪误差收敛到零附近的足够小的邻域内. 最后, 通过对柔性关节机械臂系统进行仿真, 验证了所提出控制方法的有效性. 相似文献
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This paper investigates the dynamic cooperative learning control for high-order output-feedback systems under the output constraints. To avoid complex computation, we use a system transformation strategy in control design. Only one neural network (NN) is employed to approximate the unknown synthetic function for each agent. Subsequently, a NN-based cooperative learning control mechanism is designed by introducing the barrier Lyapunov function (BLF). The proposed mechanism expands the NN approximation domain of the transformed systems, and the output of all subsystems remains constrained. Further, the NNs on identified uncertain system dynamics are used to construct the experience-based controllers to carry out same control tasks. Finally, the theoretical authenticity is demonstrated by a numerical example. 相似文献
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针对高阶非线性系统,开展自适应神经网络跟踪控制器设计,系统受到随机扰动的影响.首次把输入和输出约束问题引入到高阶系统的跟踪控制中,并假定系统动态是未知.首先借用高斯误差函数表达连续可微的非对称饱和模型以实现输入约束,和障碍Lyapunov函数保证系统输出受限;其次,针对高阶非线性系统,径向基函数(RBF)神经网络用来克服未知系统动态和随机扰动.在每一步的backstepping计算中,仅用到单一的自适应更新参数,从而克服了过参数问题;最后,基于Lyapunov稳定性理论提出自适应神经网络控制策略,并减少了学习参数.最终结果表明设计的控制器能保证所有闭环信号半全局最终一致有界,并能使跟踪误差收敛到零值小的邻域内.仿真研究进一步验证了提出方法的有效性. 相似文献
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本文提出一种用自组织自学习适应思想解决非线性动力系统控制问题的新方法。在每个小区域感受野,可以把非线性系统近似展开为线性,由神经元执行控制。各神经元的凝视点,感受野和功能由自组织自学习自适应方法进行调节。大量仿真结果验证了本方法的正确性和实用性。 相似文献
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This article is concerned with the problem of dynamic event-triggered prescribed performance control for nonlinear systems under signal temporal logic tasks. By utilizing the method of prescribed performance control, the constrained plant can be transformed into an unconstrained one, and a dynamic event-triggered feedback control law is generated for the transformed system to ensure that the signal temporal logic specification is satisfied. A dynamic event-triggered mechanism is designed to guarantee the event-triggered stability, safety and complex specification. Besides, Zeno phenomenon is definitely avoided. Compared with the continuous-time feedback controller, the event-triggered controller has proven to be effective in reducing sensing, communication and computation costs. Finally, two simulations are given to illustrate the effectiveness of theoretical results. 相似文献
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In this paper, an adaptive dynamic surface control scheme is proposed for a class of multi-input multi-output (MIMO) nonlinear time-varying systems. By fusing a bound estimation approach, a smooth function and a time-varying matrix factorisation, the obstacle caused by unknown time-varying parameters is circumvented. The proposed scheme is free of the problem of explosion of complexity and needs only one updated parameter at each design step. Moreover, all tracking errors can converge to predefined arbitrarily small residual sets with a prescribed convergence rate and maximum overshoot. Such features result in a simple adaptive controller which can be easily implemented in applications with less computational burden and satisfactory tracking performance. Simulation results are presented to illustrate the effectiveness of the proposed scheme. 相似文献
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In this article, the event-triggered optimal tracking control problem for multiplayer unknown nonlinear systems is investigated by using adaptive critic designs. By constructing a neural network (NN)-based observer with input–output data, the system dynamics of multiplayer unknown nonlinear systems is obtained. Subsequently, the optimal tracking control problem is converted to an optimal regulation problem by establishing a tracking error system. Then, the optimal tracking control policy for each player is derived by solving coupled event-triggered Hamilton-Jacobi (HJ) equation via a critic NN. Meanwhile, a novel weight updating rule is designed by adopting concurrent learning method to relax the persistence of excitation (PE) condition. Moreover, an event-triggering condition is designed by using Lyapunov's direct method to guarantee the uniform ultimate boundedness (UUB) of the closed-loop multiplayer systems. Finally, the effectiveness of the developed method is verified by two different multiplayer nonlinear systems. 相似文献
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本文针对一类具有未知非线性函数和未知虚拟系数非线性函数的二阶非线性系统 ,提出了一种基于神经网络的稳定自适应输出跟踪控制方法 .用李雅普诺夫稳定性分析方法证明了本文的神经网络自适应控制器能够使受控系统稳定 ,并使输出跟踪误差随时间趋于无穷而收敛到零 .仿真算例证明了该算法的有效性 相似文献