A new neural adaptive finite-time constraint tracking control strategy for stochastic nonlinear systems with quantized input and unknown initial condition

In this paper, the concentration is on researching a neural adaptive prescribed finite-time constraint quantized tracking control strategy for a class of stochastic nonlinear systems with external disturbances, input quantization and unknown initial conditions. In this control strategy, two designs are considered, namely, the performance constraint control design circumventing system initial condition and the control design in which the control input has a zero initial value. To achieve the two designs simultaneously, a class of mapping functions, input tuning functions, and a piecewise indirect constraint performance function are proposed. In addition, a new prescribed finite-time performance function is also given to guarantee better tracking error convergence. To address the stability analysis problem of the system under the new control strategy, a new proposition is presented as a supplement to the Lyapunov stability criterion in this paper. Based on these findings, a neural adaptive finite-time performance constraint quantized controller with an initial value of zero is obtained. The proposed controller guarantees that the constrained variable enters a prescribed region within a preset time, regardless of its initial condition. All the signals in the closed-loop system are bounded in probability. The simulation results demonstrate the effectiveness and the superiority of the proposed strategy.