Achieving accurate closed-loop position control of soft robots remains an ongoing research problem, due to the challenges posed by underactuation, elastic nonlinearities, and material creep. Although soft driving technologies relying on tendons and smart material transducers (e.g., dielectric elastomers, shape memory alloys) offer more ease of controllability compared to pneumatics, the corresponding controller design problem becomes even more challenging because of additional nonlinear effects. Those include a configuration-dependent actuation matrix, that stems from the kinematics of the actuation, and control input saturation, which is especially critical for smart material actuators. In this article, we investigate for the first time the closed-loop position control of a soft-robotic system driven by dielectric elastomer actuators. The objective is to regulate the robot state to a constant setpoint, accounting for the effects of open-loop instability, underactuation, control input saturation, and constant external disturbances. To achieve this goal, we propose a model-based feedback scheme, which combines a stabilizing energy-shaping controller with a robustifying PI-like law. After presenting the general theory, a linear matrix inequalities algorithm is proposed to practically address the controller design in spite of strong model nonlinearities. Experimental validation conducted on a prototype of the soft-robotic system confirms the effectiveness of the proposed control approach.

We present theoretical and experimental results regarding the development and the control of a two-link robotic arm with remotized actuation via rolling diaphragm hydrostatic transmissions. We propose a dynamical model capturing the essential dynamics of the developed transmission/robot ensemble and implement a control strategy consisting of two nested loops, the inner one performing high-bandwidth joint torque regulation and the outer one producing various types of compliance responses for effective human–robot interactions. Extensive sets of experiments, testing both the low-level torque controller and the high-level compliance controller, confirm the effectiveness of the proposed hardware-software remotization architecture.

This paper presents the design and the realization of an innovative handling system for plane work pieces in production and assembly industry. The system consists of an adaptive end-effector endowed with energy-efficient vacuum grippers. Shape memory alloy (SMA) actuator wires are used to actively reconfigure the gripping arms as well as to activate the vacuum suction cups. SMA technology enables the system to be versatile, and the inherent self-sensing features allow the implementation of advanced maintenance and sensorless control strategies. For each of the two components of the system (i.e., reconfigurable gripper and suction cup), design and operating principle are discussed in detail. Experimental results are then presented, and system performance is finally discussed.

In this article, we present a sensorless stiffness control (SC) architecture for a soft dielectric elastomer (DE) membrane actuator. The method relies on a self-sensing algorithm that exploits measurements of electrical quantities (i.e., membrane voltage and current) to perform a real-time estimation of DE displacement and force. By combining self-sensing feedback with a SC algorithm, active shaping of the membrane force-displacement response is achieved without introducing additional electro-mechanical sensors in the system, thus, making it possible to design compact, lightweight, and low-cost DE robotic systems. A description of the novel self-sensing scheme is initially performed. To cope with the strong system nonlinearity, a robust design method to synthesize a SC law is subsequently proposed. An extensive experimental campaign is, then, carried out, with the goal of evaluating the performance of both sensor-based and sensorless SC. Quantitative accuracy of both control architectures is finally assessed and compared.

In this paper, a new optimal control scheme is presented to minimize the energy required by a positioning system based on a dielectric elastomer membrane actuator. In order to address the strongly nonlinear model of the device, an approximated approach is required. In here, an Adaptive Dynamic Programming based algorithm is proposed, capable of minimizing a family of cost functions related to the energy consumption with respect to a single reference value. Furthermore, in order to handle the optimization of arbitrary reference trajectories without solving an infinite number of optimal control problems, a fuzzy logic approach is proposed. Simulation results are finally presented to show the advantages of the proposed method, compared with alternative solutions.

This paper deals with the modeling and position control of an ElectroActive Polymer (EAP) actuator system consisting in a combination of an EAP membrane and a mass-spring biasing mechanism. First, a description of the biasing system is provided, and a physical-based dynamic model is derived for the whole system. Then, the model is used for the identification of an experimental actuator, and for the subsequent design of a position control. A simple but effective method is presented in order to compensate the model nonlinearities and achieve fast positioning. Simulation stage is used for testing several control strategies, in order to choose the most suitable one to be integrated in the experimental system.