A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control

Abstract

Due to the outstanding characteristics of the large structural flexibility and strong dexterity of soft robots, they have attracted great attention. However, the dynamic modeling and precise control of soft robots face huge challenges. Traditional model-based and model-free control methods find it difficult to obtain a balance between complexity and accuracy. In this paper, a dynamic model of a three-chamber continuous pneumatic manipulator is established based on the modal method. Moreover, a hybrid controller integrating model predictive control (MPC) and iterative learning control (ILC) is proposed, which can simultaneously perform model parameter learning and trajectory tracking control. Experimental results show that the proposed control method can optimize the parameters of the dynamic model in real time with less iterations than the traditional model-free method and have good control performance in trajectory tracking experiments. In the future, the proposed dynamic model and the hybrid controller should be verified on a multi-section manipulator.

Optimal Pneumatic Actuator Positioning and Dynamic Stability using Prescribed Performance Control with Particle Swarm Optimization: A Simulation Study

“…where𝐽is a cost function calculated from the Mean Absolute Error (MAE) percentage of each iteration (𝑘) of 𝜀(𝑡) in (13).…”

“…Each agent's position corresponds to specific rewards measured by the selected fitness function (𝑓 𝑓𝑖𝑡 ) value. Function in (13) will continuously update the current agent's velocity with reference to those reward values. The updating function can be expressed in (14).…”

“…Continuing with that, the fitness function's value for each position is evaluated. As the optimization problem is to solve Problem 1, whereby 𝑓 𝑓𝑖𝑡 = 𝐽, as given by (13), is a suitable candidate for the fitness function.…”

“…These efforts include the use of control theories through numerical methods [7], [8], integration with intelligent systems [8], [9] and parameter designed using computational methods [10], [11]. Moreover, some control strategy approaches have been done by engineers and researchers to cater to the PSS issues, such as integration controllers' methods or hybrid techniques, as done in [12], [13]. The other integration methods include cascading control, adaptive control, intelligent control, and optimal control using computational methods [14]- [17].…”

Mechatronic automatic control system of electropneumatic manipulator

“…The performance of the controller is demonstrated through simulations and experiments with two different prototypes. Researchers in China are studying how to improve the efficiency and productivity of pneumatic manipulators, optimize their speed and accuracy for various applications in manufacturing 6 . Scientists from the Republic of Korea and Japan have developed a fast, accurate and inexpensive pneumatic actuator with controlled position, which can be applied to a variety of practical positioning applications with various external loads 7 .…”