Controlling Pneumatic Bending Actuator With Gain-Scheduled Feedforward and Physical Reservoir Computing State Estimation

Hysteresis brings challenges to both the control and state perception of soft robots. This work proposes a real-time gain-scheduled feedforward proportional controller design and a Physical Reservoir Computing (PRC) model to address hysteresis effects in the motion control and unobstructed state estimation of a dual pneumatic artificial muscle (PAM) soft bending actuator. The dual-PAM soft actuator comprises an active PAM used for actuation and a pressurized-and-sealed passive PAM serving as a physical reservoir and used for computation. The physical reservoir's state is reflected by the passive PAM's inner pressure and used for bending state estimation. Experiments exhibit the physical reservoir state's nonlinear responses to the active PAM's actuation inputs. The proposed feedforward controller improves the soft actuator's responsiveness in hysteresis dead zones by dynamically adjusting the feedforward proportional gain. The proposed controller outperforms a linear approximation-based feedforward controller in motion control, and the PRC-based bending state estimation model achieves higher accuracy than a comparative Echo State Network (ESN) with 1,000 neurons. The presented strategies are expected to benefit the precise motion control and unobstructed state estimation of soft actuators.