Fuzzy Adaptive Prescribed-Time Secure Control for Constrained Human–Robot Cotransportation: A Novel Self-Triggered Quantized Control Strategy

It is quite desirable yet challenging to guarantee secure tracking within a given shortest possible time for human–robot cotransportation (HRCT) in a shared environment with obstacles if unmeasured velocity and limited communication occupation of the control channel are considered. Here, a novel tracking control solution presented in this article is capable of fulfilling such expectation. Therein, by constructing a fuzzy velocity observer with the idea of the fuzzy logic technique, the previous output-feedback control results for nonlinear systems are extended to HRCT control with unmeasured velocity and modeling uncertainties. Also, a novel self-triggered quantized (STQ) controller is developed, which can better balance the relationship between system performance and resource utilization. Under the STQ control framework, both the settling time regulator and specific system transformation ways are incorporated into the dynamic surface-based backstepping design. It results in a new set of regulation with less communication occupation that allow the position and velocity signals to be within preset restriction ranges, while the convergence time and accuracy are preassigned by the designer. Meanwhile, benefiting from the integral multiplicative barrier Lyapunov function, the adaptive obstacle avoidance behavior of the system is guaranteed. Eventually, the benefits and efficacy of the resultant scheme are fully verified via a practical example.