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This study proposes the design and development of an in-pipe mini crawler (or robot) capable of performing its various tasks with the ability to reject undesired disturbances resulting from friction and viscosity, as it was modeled, simulated, and experimented using an iterative learning algorithm (ILA)-based active force control (AFC) strategy. The crawler motion was executed based on a rapid and successive push-pull action plus friction that causes the crawler to move in an earthworm-like manner using a linear voice-coil actuator (VCA). A novel self-adjusted mechanism was designed to ensure that the crawler remained concentric in the pipe as it slides along the inner surface of the pipe. A novel control strategy was also proposed consisting of the AFC-based controller cascaded with a proportional-integral-derivative (PID) controller to control the crawler movement and expel off the applied perturbations. An intelligent PD-type ILA was employed to automatically tune the AFC while online. For the validation part, a prototype was designed, developed, and later experimented with using the proposed technique for a given set of conditions. The system integration employed a hardware-in-the-loop (HIL) test configuration utilizing LabVIEW. Experimental results are in good agreement with the simulation counterpart, thereby indicating the practicality and feasibility of the control system in performing accurate and robust trajectory tracking. This shall serve as a good basis for designing more challenging tasks related to miniature crawling mechanism in-pipe applications.

A Novel Voice-Coil Actuated Mini Crawler for In-Pipe Application Employing Active Force Control With Iterative Learning Algorithm