A New Method of Force Control for Unknown Environments

Abstract

Current robotic systems are expected to interact with unknown environment where controlling the interaction forces becomes an important issue. We propose a new control technique for force control on unknown environments that tunes the force controller based on online estimation of the environment parameters. However, the proposed approach overcomes the need for precise estimation of environment parameters, which are needed in many system identification-based force control approaches. This framework uses an artificial neural network (ANN)-based proportional-integral (PI)-gain scheduling force controller to track the desired force by adjusting control gains such that error in parameter estimation can be accommodated. Experimental results are presented to demonstrate the efficacy of the proposed control framework. Finally, the advantages and limitations of the proposed controller are discussed.

Adaptive Hierarchical Control for the Muscle Strength Training of Stroke Survivors in Robot-Aided Upper-Limb Rehabilitation

“…[19], the estimated results using the above FRLS method were still not satisfactory -especially for the damping coefficient -although the given environment parameters, stiffness and damping are constant. In robotaided clinical rehabilitation, the impaired limb's dynamics vary randomly and its bio-impedance parameters are time-variant throughout the whole rehabilitation process.…”

“…[19] where the robot environment was modelled as a linear timeinvariant spring-damper system, Eq. (3) can be reorganized in the discrete-time domain as:…”

Design and Implementation of a Control Architecture for Rehabilitation Robotic Systems

“…In order to ensure smooth switching, a switching mechanism that we have previously shown to guarantee bumpless switching for satisfactory force response (Mallapragada et al, 2006) is used in this work. This mechanism modifies the position reference, which is the input for the inner loop of the force controller, at the time of the switching in such a way that it is equal to the position reference at the time before switching occurred.…”

Dual-layer fuzzy control architecture for the CAS rover arm

“…Fuzzy controller is designed to maintain stability of the contact force between the robot and the environment for the unknown environment stiffness or changes by Bum et al [23,24]. Environmental parameter estimation method is proposed to weaken the requirements needed by force control of robot impedance control for the accuracy of the environmental parameters by Mallapragada, and the power control coefficient based on PI control is adjusted by neural networks at the same time [25]. Stability and setpoint control problems of constrained robot with kinematics and dynamics uncertainties are formulated and solved [26][27][28].…”