Quantified-state-feedback-based Nonlinear pure-feedback MIMO systems benefit from adaptive neural control

In the case of state quantizers for MIMO nonlinear block-triangular pure-feedback systems, there is a certain sort of uncertainty that may be tolerated, we describe a quantized state feedback tracking mechanism. All state variables that may be measured for feedback are thought to benefit from uniform quantizers. In this study, we focus on the problem of tracking of output for a certain kind of MIMO nonlinear systems composed of interconnected modules with widely varying degrees of uncertainty. To begin, the total disturbance in the subsystems' control channels is refined to account for all uncertainties impacting the performance of the controlled outputs, such as internal unmodeled dynamics, external disturbances, and unknown nonlinear interactions between subsystems. It is shown that the total disturbance level is low enough for a real-time estimate to be made by an extended state observer (ESO) utilising the observed outputs from the separate subsystems. Stability study of the closed-loop system with quantized state feedback is also performed using the Lyapunov stability theorem. Finally, illustrative simulation examples, such as a network of inverted pendulums, are shown to prove that the suggested control approach works as intended.