Many practical control systems contain unknown terms and parameters, and are required to complete a control task at prescribed time and operate for an infinite-time interval. This article considers a prescribed-time (PT) tracking control problem for strict feedback systems with unmodeled dynamics (UDs). A PT adjustment function is designed, which is sufficiently differentiable for an infinite-time interval. By applying the PT adjustment function, a modified PT dynamic signal is first introduced to dominate the impact of UDs. Utilizing a series of inequalities, the UDs are approximated. By using the backstepping design method and PT stability theory, a tracking controller is constructed. With the proposed control strategy, both PT and infinity-time tracking performance are guaranteed, and the output tracking error enters a neighborhood of the origin at PT, whose size can be made small enough. Finally, comparative simulation and experiment results are provided to illustrate the effectiveness of the proposed controller.

This article addresses a neural adaptive finite-time tracking control problem for n-th order strict feedback systems. By utilizing the backstepping design method, neural networks technology and finite-time stability theory, a tracking controller is constructed step by step. In contrast to the previous results, a (n−j)-th order differentiable piecewise function is introduced at step j so that the corresponding virtual controller is differentiable up to the order of n−j, all known terms in the original system and the derivatives of virtual controllers are treated as known and linear terms are introduced in virtual controllers and actual controllers. The proposed control strategy guarantees that all the closed-loop system variables remained bounded and the reference trajectory is well tracked in finite time. Finally, the efficacy of the proposed controller is verified by comparative experimental results for a 6-degree-of-freedom robotic manipulator. Test results confirm that the tracking controller with linear terms, virtual control derivatives and known terms has higher control accuracy.

This article considers a weak disturbance decoupling control problem for strict-feedback systems with unmodeled dynamics and disturbances. Utilizing a series of inequalities, the unmodeled dynamic is approximated. Using an adaptive backstepping control method, a weak disturbance decoupling controller is constructed to stabilize the closed-loop system. The primary objective of this paper is to design an adaptive controller that effectively attenuates the impact of disturbances in scenarios where such disturbances are present, while ensuring the bounded stability of system states in the absence of disturbances. Simulation and experiment results are presented to verify the effectiveness of the proposed weak disturbance decoupling strategy.