Adaptive fractional-order admittance control for force tracking in highly dynamic unknown environments

Abstract

Purpose With the increasing demands of industrial applications, it is imperative for robots to accomplish good contact-interaction with dynamic environments. Hence, the purpose of this research is to propose an adaptive fractional-order admittance control scheme to realize a robot–environment contact with high accuracy, small overshoot and fast response. Design/methodology/approach Fractional calculus is introduced to reconstruct the classical admittance model in this control scheme, which can more accurately describe the complex physical relationship between position and force in the interaction process of the robot–environment. In this control scheme, the pre-PID controller and fuzzy controller are adopted to improve the system force tracking performance in highly dynamic unknown environments, and the fuzzy controller is used to improve the trajectory, transient and steady-state response by adjusting the pre-PID integration gain online. Furthermore, the stability and robustness of this control algorithm are theoretically and experimentally demonstrated. Findings The excellent force tracking performance of the proposed control algorithm is verified by constructing highly dynamic unstructured environments through simulations and experiments. In simulations and experiments, the proposed control algorithm shows satisfactory force tracking performance with the advantages of fast response speed, little overshoot and strong robustness. Practical implications The control scheme is practical and simple in the actual industrial and medical scenarios, which requires accurate force control by the robot. Originality/value A new fractional-order admittance controller is proposed and verified by experiments in this research, which achieves excellent force tracking performance in dynamic unknown environments.

Optimized Residual Action for Interaction Control with Learned Environments

“…On the other hand, variable stiffness methods [20] deal with an online adaption law of the stiffness term, as a function of the force error. In [21], it is instead determined by a fuzzy-logic law; additionally, [22] proposes a fractional-order control law, instead of the classical second-order impedance controller.…”

“…The most challenging aspect of interaction control lies in selecting an appropriate model for the environment, whose inevitable inaccuracies constitute the aspect the aforementioned works seek to compensate: in the literature, the most common choice is adopting a linear spring model [8], [12], [17], [18], [20], [21], [22], [23], [24], [26], [27], [28] but, as specified in [8], [29], [30], it constitutes, in general, an approximation. To cope with this inherent difficulty, recent research trends lean towards leveraging AI-based and data-driven methods to devise control laws from data, rather than relying on modelbased strategies.…”

“…Novel force control techniques are typically validated by assessing their performance, in terms of force-tracking error, against specific 1-dimensional force profiles, e.g. constants [12], [17], [18], [22], [25], step functions [48], or sine waves with periods in the order of seconds [23], [24].…”

Force Tracking Control Method for Robotic Ultrasound Scanning System under Soft Uncertain Environment

“…In [27], an admittance force control system was proposed based on an F/T sensor to compensate for the force required by the sonographer during scanning. In addition, in the field of industrial robotics, a series of force tracking control strategies have been proposed, such as adaptive control [28,29], fuzzy logic [30,31], and neural networks [32].…”

Learning Synchronous Control Based on Intelligent Methods for Ultra-Precision Stage Systems

“…Modern control methods (MCM) could adapt and update the systematic parameters in real time according to the system state variables [18][19][20]. Compared to the traditional PID, MCM is more effective in dealing with complex, time-varying systems [21,22]. Since MCM is based on linearization, it requires linearization when applied to nonlinear systems, which could result in significant residual error [23][24][25].…”