Event-triggered boundary quantization control for flexible manipulator based on partial differential equations dynamic model

Abstract

In this paper, we consider boundary control for a single-link flexible manipulator system described by partial differential equations (PDEs). Existing researches on controller design rarely consider the problem of communication capacity constrains during signal transmission. To deal with this problem, an adaptive control is designed to achieve the input quantization by using the random quantizer. Besides, a triggering event is addressed on the basis of relative threshold strategy for relieving communication load between controller and actuator. The proposed scheme is able to ensure that all closed-loop signals are globally uniformly bounded, and the angular tracking error and vibration converge to a residual set. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Stabilization of Networked Control Systems Under DoS Attacks and Output Quantization

“…Stabilization of several classes of PDE systems with static quantization has been also investigated; see [11,30,32] for first-order hyperbolic systems, [18,29] for second-order parabolic systems, and [17] for third-order parabolic systems. Control methods for flexible structure systems with static input quantization have been proposed, e.g., in [14,16,39,45]. Dynamic quantizers with zooming parameters have been designed for first-order hyperbolic systems in [3].…”

Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization

“…Extensive efforts have been put on the application of input quantization, such as multi-agent systems, [30][31][32] aerospace engineering, [33][34][35] and manipulator systems. [36][37][38] Motivated by the aforementioned analyses, this paper develops an adaptive backstepping sliding mode control (ABSMC) scheme for AUVs subject to the dynamic uncertainty, external disturbance and quantization error. A hybrid quantizer is employed by combining a logarithmic quantizer and an uniform quantizer, and the control forces and moment are quantized by the hybrid quantizer.…”

“…Extensive efforts have been put on the application of input quantization, such as multi‐agent systems, [ 30–32 ] aerospace engineering, [ 33–35 ] and manipulator systems. [ 36–38 ]…”

Networked fault detection for fuzzy systems using a quantizer with event-triggered strategy

“…Chen and Zhang (2020) combined quantization with event triggering to reduce the network transmission burden and save network resources. Wang and Liu (2021) designed an adaptive control, which realized input quantization through a random quantizer, and used event triggering to reduce the communication load. However, the above methods for addressing quantization and event-triggered scheme are just linear combination of existing methods for quantization and event-triggering mechanism, respectively.…”