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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.

In this paper, a worm-like micro robot designed for inpipe application with intelligent active force control (AFC) capability is modelled and simulated. The motion of the micro robot is based on an impact drive mechanism (IDM) that is actuated using piezoelectric device. The trajectory tracking performance of the modelled micro robot is initially experimented via a conventional proportionalintegral- derivative (PID) controller in which the dynamic response of the robot system subjected to different input excitations is investigated. Subsequently, a robust intelligent method known as active force control with fuzzy logic (AFCFL) is later incorporated into the PID scheme to enhance the system performance by compensating the unwanted disturbances due to the interaction of the robot with its environment. Results show that the proposed AFCFL scheme is far superior than the PID control counterpart in terms of the system-s tracking capability in the wake of the disturbances., {"references":["Aoshima, S., Tsujimura, T., & Yabuta, T. (1993). A miniature mobile\nrobot using piezo vibration for mobility in a thin tube. ASME J.\nDynam. Syst , 115, 270-278.","Idogaki, T., Kanayama, H., Ohya, N., Suzuki, H., & Hattori, T. (1995).\nCharacteristics of piezoelectric locomotive mechanism for an in-pipe\nmicro inspection machine. IEEE 6th Int. Symp. Micro Machine and\nHuman Sciences, (pp. 193-198).","Matsumoto, T., Okamoto, H., Asano, M., Mitsuishi, S., & Matsui, T.\n(1994). A prototype model of micro mobile machine with piezoelectric\ndriving force actuator. IEEE 5th Int. Symp. Micro Machine and Human\nSciences, (pp. 47-54).","Fukuda, T., Hosokai, H., Ohyama, H., Hashimoto, H., & Arai, F.\n(1991). Giant magnetostrictive alloy (GMA) applications to micro\nmobile robot as a micro actuator without power supply cables. IEEE\nInt. Workshop Micro Electro Mechanical Systems (MEMS), (pp. 210-\n215).","Suzumori, K., & Abe, T. (1993). Applying a flexible microactuators to\npipeline inspection robots. IMACS/SICE Int. Symp. Robotics and\nManufacturing Systems, (pp. 515-520). The Netherlands.","Takahashi, M., I.Hayashi, N.Iwatsuki, Suzumori, K., & Ohki, N.\n(1994). The development of an in-pipe microrobot applying the motion\nof an earthworm. IEEE 5th Int. Symp. Micro Machine and Human\nSciences, (pp. 35-40).","Iwashita, S., Hayashi, I., Iwatsuki, N., & Nakamura, K. (1994).\nDevelopment of in-pipe operation micro robots. IEEE 5th Int. Symp.\nMicro Machine and Human Sciences, (pp. 41-45).","Bart, S., Lober, T., Howe, R., Lang, J., & Schlecht, M. (1988). Design\nConsiderations for Microfabricated Electric Actuators. 14 (3), 269-292.","Hayashi, T. (2000). Research and Development of Micromechanisms\n(Vol. 1). Journal of ultrasonics.\n[10] Idogaki, T., Kanayama, H., Ohya, N., Suzuki, H., & Hattori, T. (1995).\nCharacteristics of piezoelectric locomotive mechanism for an in-pipe\nmicro inspection machine. IEEE 6th Int. Symp. Micro Machine and\nHuman Sciences, (pp. 193-198).\n[11] Johnson, C. (1971). Accomodation of external disturbances on linear\nregulator and servomechanism problems. IEEE Trans. Automat.\nControl , 635-644.\n[12] Davison, E. (1976). Multivariable Tuning Regulators: The\nFeedforward and Robust Control of a General Servomechanism\nProblem. IEEE Trans. Automat. Control, 35-47.\n[13] Hewit, J. R., & Burdess, J. S. (1981). Fast Dynamic Decoupled Control\nfor Robotics using Active Force Control. Trans. Mechanism and\nMachine Theory , 535-542.\n[14] Mailah, M., & Ong, M. (2001). Intelligent Adaptive Active Force\nControl of A Robot Arm With Embedded Iterative Learning\nAlgorithms. (pp. 85-89). Jurnal Teknologi (A).\n[15] Pitowarno, E., Mailah, M., & Jamaluddin, H. (2002). Knowledge-\nBased Trajectory Error Pattern Method Applied to An Active Force\nControl Scheme. International Journal of Engineering and\nTechnology, 2, 1-15.\n[16] Mailah, M. (1998). Intelligent Active Force Control of a Rigid Robot\nArm Using Neural Network and Iterative Learning Algorithms.\nUniversity of Dundee: UK: Ph.D Thesis.\n[17] Priyandoko, G., & Mailah, M. (2007). Simulation of A Suspension\nSystem With Adaptive Fuzzy Active Force Control. International\nJournal on Simulation Modelling , 6, 25-36.\n[18] Jamshidi, M., Vadiee, N., J.Ross, T., Fuuzy Logic and Control,\nPrentice-Hall International, Inc., 1993"]}