Abstract
Aiming at the challenges of networked visual servo control systems, which rarely consider network communication duration and image processing computational cost simultaneously, we here propose a novel platform for networked inverted pendulum visual servo control using H∞ analysis. Unlike most of the existing methods that usually ignore computational costs involved in measuring, actuating, and controlling, we design a novel event-triggered sampling mechanism that applies a new closed-loop strategy to dealing with networked inverted pendulum visual servo systems of multiple time-varying delays and computational errors. Using the Lyapunov stability theory, we prove that the proposed system can achieve stability whilst compromising image-induced computational and network-induced delays and system performance. In the meantime, we use H∞ disturbance attenuation level γ for evaluating the computational errors, whereas the corresponding H∞ controller is implemented. Finally, simulation analysis and experimental results demonstrate the proposed system performance in reducing computational errors whilst maintaining system efficiency and robustness.
Highlights
A S ONE of the representative developments in the control field, effective manipulation and control of inverted pendulum systems (IPSs) have demonstrated promising progress in handling some challenging problems such as nonlinearity, robustness, stability and tracking [1]–[4]
In the last 10-20 years, the traditional IPSs have been transformed to networked IPSs where inverted pendulums, sensors and controllers distributed at different locations can exchange and share information via networks [5]
We present a novel networked inverted pendulum visual servo systems (NIPVSSs), jointly considering communication networks and visual servo, where the former causes network-induced delay and the latter causes image-induced computational delay and errors
Summary
A S ONE of the representative developments in the control field, effective manipulation and control of inverted pendulum systems (IPSs) have demonstrated promising progress in handling some challenging problems such as nonlinearity, robustness, stability and tracking [1]–[4]. In the last 10-20 years, the traditional IPSs have been transformed to networked IPSs where inverted pendulums, sensors and controllers distributed at different locations can exchange and share information via networks [5]. With the advances of visual sensing technology, many industrial applications, e.g. automated detection and robot control, have started integrating visual observations such as images with a networked IPS in order to create a fully functioning cyber-physical system (CPS) [6]–[11]. Among the applications, networked inverted pendulum visual servo systems (NIPVSSs) have gained large.
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