Dynamic Control of Soft Robotic Arm
This work addresses the control problem for soft robotic arms, which is incremental as it builds on existing modeling efforts to improve practical implementation in scenarios with sensor noise, actuator limitations, and hysteresis effects.
The paper investigated control approaches for a pneumatically actuated soft robotic arm, comparing kinematic control, PD+feedback linearization, passivity control, and adaptive passivity control, with simulation results showing that adaptive passivity control with sigma modification terms and a high-gain observer performed better than other methods.
In this article, the control problem of one section pneumatically actuated soft robotic arm is investigated in detail. To date, extensive prior work has been done in soft robotics kinematics and dynamics modeling. Proper controller designs can complement the modeling part since they are able to compensate other effects that have not been considered in the modeling, such as the model uncertainties, system parameter identification error, hysteresis, etc. In this paper, we explored different control approaches (kinematic control, PD+feedback linearization, passivity control, adaptive passivity control) and summarized the advantages and disadvantages of each controller. We further investigated the robot control problem in the practical scenarios when the sensor noise exists, actuator velocity measurement is not available, and the hysteresis effect is non-neglectable. Our simulation results indicated that the adaptive passivity control with sigma modification terms, along with a high-gain observer presents a better performance in comparison with other approaches. Although this paper mainly presented the simulation results of various controllers, the work will pave the way for practical implementation of soft robot control.