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Summary: The problem of adaptive fuzzy fixed‐time tracking control based on a category of nonlinear systems with unmodeled dynamics and dynamic disturbances is investigated in this article. By introducing the novel fixed‐time dynamic signal, the unmodeled dynamics can be disposed of. On the basis of the fuzzy logic systems (FLSs) and adaptive backstepping technology, the disposing difficulty of the unknown nonlinear portions is reduced. Then, all signals in the closed‐loop system are ensured to be bounded under the fixed‐time Lyapunov stability theory. Simultaneously, the tracking error converges to a small neighborhood of the origin. Eventually, simulation consequences reveal the validity of the presented control method. [ABSTRACT FROM AUTHOR]

This article proposes an improved adaptive finite time command filtered controller for high-precision motion control of electro-hydraulic servo systems. During the controller design process, the effects of parameter uncertainty, unmodeled disturbances, and dead zone uncertainty in the system are effectively addressed by integrating parameter adaptive laws and disturbance estimation laws. By extending the system states, this strategy eliminates the need for the controller to rely on the first-order derivative of the tracking trajectory, making it applicable in tracking non smooth curves. Through Lyapunov stability analysis and comparative simulation experiments, it is shown that even in the presence of unmodeled disturbances, unknown system parameters, and unknown dead zone characteristics in the system, the developed controller can still achieve adjustable neighborhood tracking errors around the origin in a finite time. • A controller for an electro-hydraulic servo system that comprehensively considers multiple nonlinear factors is proposed. • Improved instruction filtering controller, weakened derivative requirements, and expanded application scenarios. • The proposed controller can achieve tracking error convergence within the specified error band within a finite time. • This article considers the nonlinear factors caused by the dead zone of the servo valve in the system. [ABSTRACT FROM AUTHOR]

This paper establishes a novel fractional‐order model for n‐links flexible‐joint (FJ) robots and proposes an adaptive dynamic surface control (DSC) scheme to address the tracking control problem. The fractional‐order FJ model is built by fractional‐order viscoelastic dynamics model to have a more concise form. An adaptive DSC strategy is proposed to address the tracking control problem based on backstepping method. By selecting the appropriate orders for fractional filters, the controller could solve the "explosion of complexity" problem. The unknown nonlinearities of FJ robot systems are approximated by Radial basis function (RBF) neural networks (NNs). Based on the Lyapunov stability theory, the bounds of all signals in the closed‐loop system are achieved. The simulation results confirm the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an observer‐based adaptive neural output‐feedback control scheme is developed for a class of nonlinear stochastic nonstrict‐feedback systems with input saturation in finite‐time interval. The mean value theorem and the property of the smooth function are applied to cope with the difficulties caused by the existence of input saturation. According to the universal approximation capability of the radial basis function neural network, it will be utilized to compensate the unknown nonlinear functions. Based on the state observer, the finite‐time Lyapunov stability theorem, we propose an adaptive neural output‐feedback control scheme for nonlinear stochastic systems in nonstrict‐feedback form. The developed controller guarantees that the system output signal can track the given reference signal trajectory, and all closed‐loop signals are semi‐globally finite‐time stability in probability. The observer errors and the tracking error can converge to a small neighborhood of the origin. Finally, simulation results demonstrate the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]