Adaptive Control With Fast Convergence for State-Quantization Constrained Manipulators Under Unknown Measurement Powers

This brief focuses on the adaptive control with fast convergence performance for robot manipulators with unknown measurement powers and quantized states. Since there are some uncertain factors in the actual operation of manipulators, which lead to unknown measurement functions. This paper considers a new measurement function, i.e., the powers of the measured values are unknown time-varying functions and some auxiliary variables are introduced to transform control errors, thereby compensating unknown measurement powers. Unlike the traditional adaptive control methods, this paper designs a novel quantized control strategy based on quantized states. Since quantized states are discrete and not derivable, which poses the difficulty in the design of control algorithm. To compensate the effect of quantized states, this paper establishes the relationship between quantized signals and unquantized signals. The input saturation is handled by developing an auxiliary system. By designing a fast convergence function, the control errors under the proposed strategy can quickly enter into a tunable interval within a time set in advance. Finally, a manipulator simulation is performed to verify the validity of the control method.