Nonlinear Estimation and Control of Bending Soft Pneumatic Actuators Using Feedback Linearization and UKF

Abstract

In this article, we combine nonlinear estimation and control methods for precise bending angle control in soft pneumatic actuators driven by a pressure source and single low-cost <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> / <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> solenoid valve. First, a complete model for the soft actuator is derived, which includes both the motion and pressure dynamics. An unscented Kalman filter (UKF) is used to estimate the velocity state and filter noisy measurements from a pressure sensor and an embedded resistive flex sensor. Then, a feedback linearization approach is used with pole placement and linear quadratic regulator (LQR) controllers for bending angle control. To compensate for model uncertainties and improve reference tracking, integral action is incorporated to both controllers. The closed-loop performance of the nonlinear estimation and control approach is experimentally evaluated with a soft pneumatic network actuator. The simulation and experimental results show that the UKF provides accurate state estimation from noisy sensor measurements. The results demonstrate the effectiveness and robustness of the proposed observer-based nonlinear controllers for bending angle trajectory tracking.