Biologically-inspired Motion Control for Kinematic Redundancy Resolution and Self-sensing Exploitation for Energy Conservation in Electromagnetic Devices

Abstract

This thesis investigates particular topics in advanced motion control of two distinct mechanical systems: human-like motion control of redundant robot manipulators and advanced sensing and control for energy-efficient operation of electromagnetic devices. Control of robot manipulators for human-like motions has been one of challenging topics in robot control for over half a century. The first part of this thesis considers methods that exploits robot manipulators’ degrees of freedom for such purposes. Jacobian transpose control law is investigated as one of the well-known controllers and sufficient conditions for its universal convergence are derived by using concepts of “stability on a manifold” and “transferability to a sub-manifold”. Firstly, a modification on this method is proposed to enhance the rectilinear trajectory of the robot end-effector. Secondly, an abridged Jacobian controller is proposed that exploits passive control of joints to reduce the attended degrees of freedom of the system. Finally, the application of minimally-attended controller for human-like motion is introduced. Electromagnetic (EM) access control systems are one of growing electronic systems which are used in applications where conventional mechanical locks may not guarantee the expected safety of the peripheral doors of buildings. In the second part of this thesis, an intelligent EM unit is introduced which recruits the selfsensing capability of the original EM block for detection purposes. The proposed EM device optimizes its energy consumption through a control strategy which regulates the supply to the system upon detection of any eminent disturbance. Therefore, it draws a very small current when the full power is not needed. The performance of the proposed control strategy was evaluated based on a standard safety requirement for EM locking mechanisms. For a particular EM model, the proposed method is verified to realize a 75% reduction in the power consumption.