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. Note to Practitioners—The robotic manipulators are widely used in industrial production and intelligent manufacturing, which better promotes the development of production automation and intelligence. So the stabilization study for the robotic manipulator has some physical significance. In practice, the input powers of measured states may change due to the inaccuracy of measurement tools and external disturbances. In addition, when the input signal of system is too large, it may lead to the limitation of system output, thereby affecting the normal operation of system. At the same time, in industrial production, it is very important to improve control performance and save resource consumption. Therefore, for the robotic manipulator with unknown measurement powers and input saturation, this article develops an adaptive control scheme with fast convergence performance based on quantized states. Compared with traditional control methods, the presented quantized control scheme can reduces resource loss well and enables the control error converge to a preset interval within a predefined time. The presented quantized control scheme has a wide application prospect and practical value in industrial robotic manipulators.