Biblio

In this paper, the adaptive event-triggered asymptotic tracking control with guaranteed performance for a single link robot manipulator (SLRM) system driven by the brush DC motor is studied. Fuzzy logic systems (FLS) is used to approximate unknown nonlinear functions. By introducing a finite time performance function (FTPF), the tracking error of the system can converge to the compact set of the origin in finite time. In addition, by introducing the smooth function and some positive integral functions, combined with the boundary estimation method and adaptive backstepping technique, the asymptotic tracking control of the system is realized. Meanwhile, event-triggered mechanism is introduced to reduce the network resources of the system. Finally, a practical example is given to prove the effectiveness of the theoretical research.

This paper develops an adaptive neural network (NN) asymptotic tracking control scheme for nonstrict-feedback switched nonlinear systems with unknown nonlinearities. The NNs are used to dispose the unknown nonlinearities. Different from the published results, the asymptotic convergence character is achieved based on the bound estimation method. By combining some smooth functions with the adaptive backstepping scheme, the asymptotic tracking control strategy is presented. It is proved that the fabricated scheme can guarantee that the system output can asymptotically follow the desired signal, and also that all signals of the entire system are bounded. The validity of the devised scheme is evaluated by a simulation example.

This work proposes an event-triggered adaptive asymptotic tracking control scheme for flexible robotic manipulators. Firstly, by employing the command filtered backstepping technology, the ``explosion of complexity'' problem is overcame. Then, the event-triggered strategy is utilized which makes that the control input is updated aperiodically when the event-trigger occurs. The utilized event-triggered mechanism reduces the transmission frequency of computer and saves computer resources. Moreover, it can be proved that all the variables in the closed-loop system are bounded and the tracking error converges asymptotically to zero. Finally, the simulation studies are included to show the effectiveness of the proposed control scheme.