The issue of fast finite-time adaptive control is studied for quantized stochastic nonlinear systems. Unlike the existing works about fast finite-time control, the input signals are quantized, and the stochastic disturbances and nonlinear functions are unknown. According to universal approximation capacity of fuzzy logic system, combined with backstepping technique, a novel fast finite-time adaptive fuzzy control strategy of quantized stochastic nonlinear system is proposed. The nonlinear decomposition method is introduced to set up the relationship among the control signals and the quantization signals, which overcomes the technical difficulties result from the piecewise quantization input. The proposed tactics can assure the tracking error situate in a neighborhood of the origin point and the closed-loop system signals keep bounded. Finally, an algorithm simulation is conducted to test the validity of the method.
SummaryThis article investigates an adaptive fast finite‐time control problem for a class of nonlinear uncertain systems. First, to reduce the transmission load, an event‐triggering mechanism is introduced into the channel from the controller to the actuator. Second, the observer is employed to estimate the unmeasurable state variables. Third, considering that the nonlinear functions of systems are completely unknown, neural networks are introduced to overcome the obstacles caused by unknown nonlinearities. Finally, an event‐triggered adaptive fast finite‐time output‐feedback control strategy is proposed by means of the fast finite‐time stability criterion and backstepping technique. The theoretical analysis illustrates that under the proposed control strategy, all signals in the closed‐loop systems converge to a bounded domain within a finite time. Furthermore, the Zeno phenomenon can be avoided effectively. The main innovation is to design the adaptive controller from a new perspective. The validity of results is elaborated by numerical simulation.
This paper concentrates upon the issue of adaptive fuzzy tracing control for a class of nonstrict‐feedback nonlinear systems output with hysteresis via an event‐triggered strategy. To handle the difficulty caused by the nonstrict nonlinear systems, the variable separation technique is introduced. The design difficulty of output hysteresis is addressed by employing a hysteresis inverse function and Nussbaum function to compensate unmeasurable state signal. Meanwhile, the fuzzy logic system (FLS) is used to estimate the unknown function at each step of recursion. Moreover, by devising the relative threshold event‐triggered mechanism (ETM), the frequency of actuators and controllers can be largely decreased. Thus, the adaptive fuzzy event‐triggered tracing control strategy is proposed by combining the barrier Lyapunov function and backstepping technique. With the proposed scheme, it is theoretically demonstrated that all signals in the closed‐loop system are bounded, and the tracing errors are driven to a small neighborhood of the origin under the output constraint. Eventually, two examples demonstrate the efficacy of the proposed control strategy.
This paper investigates the fast finite-time adaptive fuzzy control issue for the nonlinear systems with external disturbance and output hysteresis. The output hysteresis and the nonlinearities are completely unknown. The hysteresis inverse and asymmetric barrier Lyapunov function are employed to handle the difficulty caused by output hysteresis, the unknown nonlinearities are tackled by utilizing the fuzzy logic systems. Furthermore, the adaptive fuzzy fast finite-time controller is designed based on backstepping technique, such that the signals of closed-loop systems are bounded, and the tracking error remain in the defined set around zero after a finite period of time under the output constraint. This control strategy skillfully unify the control of output hysteresis and fast finite-time stability theorem for the first time. Finally, a numerical simulation confirms the effectiveness of presented control method.
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