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This article investigates the adaptive fuzzy prescribed time tracking control problem for a class of strict-feedback systems simultaneously considering the user-defined asymmetric tracking performance, input saturation, and external disturbances. From the perspective of ensuring the reliability for control implementation, a saturation-based fixed-time funnel boundary is constructed by embedding the modification signals related to input saturation errors into a funnel function, which is capable of automatically enlarging or recovering itself when input saturation occurs or disappears, thereby reducing the risk of system singularity. Subsequently, by constructing a fixed-time tracking performance function, any known bounded tracking error is recast into a new variable with a zero initial value. With such a treatment, funnel boundaries are no longer redeveloped for different initial tracking errors, and meanwhile the behavior of the tracking error is pre-specified as needed over a finite time interval. Also, auxiliary systems are devised to generate the aforesaid modification signals, while compensating for the adverse impact resulting from input saturation. Remarkably, by feat of the backstepping design based on the fuzzy approximation, it is proven that the tracking error converges to a user-defined region within a prescribed time (known as the practically prescribed time tracking), which is achieved without the fractional power feedback of system states. Finally, two simulation examples are presented to confirm the feasibility and effectiveness of the developed approach. • A novel fixed-time tracking performance function is designed to relax the initial condition. • By a simple variable transformation, the asymmetric performance constraint is handled. • A new form of self-adjustable performance funnel is constructed to enhance the reliability of control designs. • A class of the practically prescribed time tracking problems is solved. [ABSTRACT FROM AUTHOR]

This paper studies the congestion control problem of multiple bottleneck networks. First, this paper regards the routing nodes of the network as multi-agents, and considers its own dynamic process. For the first time, the time-varying characteristics of the number of sessions in the network transmission process and the mutual influence between nodes are considered into the model, and a queue utilization rate switching model is established to describe the dynamics of a single agent. Then, considering the boundedness of the control input, combined with the prescribed performance technique, two congestion controllers are designed by using the backstepping method according to whether the controller depends on the number of sessions. The designed congestion controllers improve the robustness of the TCP/AQM network system in the case of frequent and time-varying sessions in multiple bottleneck networks. In addition, the designed controllers can stabilize different routing nodes of multiple bottleneck networks under different queue lengths, thereby improving the utilization efficiency of the network. Finally, the effectiveness and superiority of the designed controllers are verified by simulation. • TCP/IP network node queue utilization switching model is built. • The controller design takes into account the input saturation characteristics. • Two prescribed performance congestion control algorithms are designed. [ABSTRACT FROM AUTHOR]

A neural network control strategy based on the immune algorithm is proposed to address the problem of chattering and trajectory tracking performance degradation of multi-joint manipulators due to unknown backlash-like hysteresis and system uncertainty. The controller is designed based on backstepping sliding mode technology and is suitable for Euler-Lagrange nonlinear systems. Firstly, to deal with the uncertainty problem of the manipulator model, we consider using radial basis function neural networks (RBFNN) to approximate, and on this basis, we add immune algorithms to optimize the RBFNN parameters to improve the approximation ability. Secondly, to compensate for the unknown backlash-like hysteresis error and RBFNN approximation error, an adaptive reaching law is designed to improve the control accuracy and reduce chattering to a certain extent. Finally, taking a 3-DOF manipulator as the research object, based on comparative simulation results, the feasibility and superiority of this control method can be proven. [ABSTRACT FROM AUTHOR]