Neuroadptive Practical Prescribed Time Tracking Control of Fully Quantized Uncertain Strict Feedback Systems

This brief presents a tracking control method for a class of fully quantized uncertain strict feedback systems. When the input and the states are all (fully) quantized, those signals are no longer continuous, making it rather challenging to use traditional backstepping for control design. In this brief, we propose a quantized control scheme with the following characteristics: 1) By introducing dynamic surface control (DSC), the problem of non-differentiation of quantized signal is avoided; 2) A simple rate function is embedded into both the quantized adaptive laws and the controller, ensuring that the tracking error and all the other internal signals converge to an adjustable precision region within predetermined time; and 3) As one of the crucial steps to prove system stability, we exclusively establish a key lemma, with which we show that the error caused by signal replacement is bounded. Numerical simulations are carried out to illustrate the effectiveness of the suggested approach.