In this paper, a finite-time tracking control scheme for perturbed undetermined nonlinear systems governed by dead-zone inputs and actuator faults is investigated. By means of dynamic surface control technique, a suitable adaptive neural network controller is introduced, which guarantees that all signals in the closed-loop system are bounded, and that all state trajectories of the error dynamics converge to a small region in the sense of semi-globally practically finite-time stabilization. Finally, a numerical simulation is taken into consideration for the reliability of the proposed methodology.
相似文献This paper investigates the observer-based adaptive finite-time neural control issue of stochastic non-strict-feedback nonlinear systems. By establishing a state observer and utilizing the approximation property of the neural network, an adaptive neural network output-feedback controller is constructed. The controller solves the issue that the states of stochastic nonlinear system cannot be measured, and assures that all signals in the closed-loop system are bounded. Different from the existing adaptive control researches of stochastic nonlinear systems with unmeasured states, the proposed control scheme can guarantee the finite-time stability of the stochastic nonlinear systems. Furthermore, the effectiveness of the proposed control approach is verified by the simulation results.
相似文献This thesis’s object is inertial memristive neural networks (IMNNs) with proportional delays and switching jumps mismatch. Different from the traditional bounded delay, the proportional delay will be infinite as t → ∞. The finite-time synchronization (FN-TS) and fixed-time synchronization (FX-TS) can be realized with the devised controllers for the drive-response systems (D-RSs). Along with the Lyapunov function and some inequalities, the synchronization criteria of D-RSs are given. This paper presents an optimization model with minimum control energy and dynamic error as objective functions, aiming to obtain more accurate and optimized controller parameters. An intelligent algorithm: particle swarm optimization with stochastic inertia weight (SIWPSO) algorithm is introduced to solve the optimization model. Meanwhile, an integrated algorithm for selecting optimal control parameters is presented as well. In this method, the optimal control parameters and the setting time of synchronization can be obtained directly. At last, some simulations are presented to verify the theorems and the optimization model.
相似文献This paper studies the problem of finite-time fuzzy adaptive dynamic surface control (DSC) design for a class of single-input and single-output (SISO) high-order nonlinear systems with output constraint. Fuzzy logic systems (FLSs) are utilized to identify the unknown smooth functions. By adopting Barrier Lyapunov function (BLF), the problem of output constrain is handled. Combining adding a power integrator and adaptive backstepping recursion design technique, a novel fuzzy adaptive finite-time DSC algorithm is proposed. Based on finite-time Lyapunov stable theory, the developed control algorithm means that all the signals of the closed-loop system are semi-global practical finite-time stable (SGPFS) and the tracking error converges to a small neighborhood of origin in finite time. In addition, the output does not violate the given constrain bound. Finally, both numerical and practical simulation examples are given to illustrate the effectiveness of the proposed control algorithm.
相似文献In this paper, fractional calculus theory is employed to inspect a finite time fault tolerant controller for robotic manipulators in the presence of uncertainties, unknown external load disturbances, and actuator faults, using fractional-order adaptive backstepping approach in order to achieve, fast response and high-precision tracking performance. Knowing the advantages of adaptive controllers an adaptive form of the above controller is then established to deal with the overall uncertainties in the system. The most important property of the proposed controller is that we do not need to have knowledge about the actuator fault, external disturbances and system uncertainties exist in system. In this study two important achievements are made. The first one is that the finite time convergence of closed-loop system is ensured irrespective of initial states values. The second one is that the effects of the actuator faults and other uncertainties are attenuated by the suggested controller. The performance of the suggested controller is then tested for a PUMA560 robot in which the first three joints are used. The simulation results validate the usefulness of the suggested finite-time fractional-order adaptive backstepping fault-tolerant (FOAB-FTC) controller in terms of accuracy of tracking, and convergent speed.
相似文献This paper studies the problem of adaptive neural network finite-time control for a class of non-triangular nonlinear systems with input saturation. Under the assumption that the nonlinearities have strict increasing smooth bounding functions, the backstepping technique can be used to design the state feedback controller and adaptive laws. Neural networks are adopted to approximate some unknown nonlinear functions. With the help of the finite-time Lyapunov stability theorem, it can be proved that the state of the closed-loop system can converge to an arbitrarily small neighborhood of the origin in a finite time. Finally, a numerical simulation example is given to show the effectiveness of the proposed design method.
相似文献研究存在输入饱和受限下的飞行器姿态控制问题, 提出一种有限时间姿态镇定方案. 针对基于修改的Rodriguez 参数模型的飞行器姿态控制系统, 基于齐次性理论和饱和控制器设计方法, 并充分利用系统的模型结构特征, 设计一类饱和的有限时间姿态控制器, 使得姿态可以在有限时间内被镇定到平衡点. 仿真结果验证了所设计姿态控制器的有效性.
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