Fuzzy PD-Type Iterative Learning Control of a Single Pneumatic Muscle Actuator

Pneumatic muscles actuator (PMA) is widely used in the field of rehabilitation robot for its good flexibility, light weight and high power/mass ratio as compared to traditional actuator. In this paper, a fuzzy logic-based PD-type iterative learning controller (ILC) is proposed to control the PMA to track a predefined trajectory more precisely during repetitive movements. In order to optimize the parameters of the learning law, fuzzy logic control is introduced into ILC to achieve smaller errors and faster convergence. A simulation experiment was first conducted by taking the PMA model fitted by support vector machine (SVM) as controlled target, which showed that the proposed method achieved a better tracking performance than traditional PD-type ILC. A satisfactory control effect was also obtained when fuzzy PD-type ILC was applied to actual PMA control experiment. Result showed that it takes 25 iterations for the maximum error of trajectory converges to a minimum of about 0.2

Synchronous Position and Compliance Regulation on a Bi-Joint Gait Exoskeleton Driven by Pneumatic Muscles

A previously developed pneumatic muscles’ (PMs) actuated gait exoskeleton (with only knee joint) has been demonstrated in achieving appropriate actuation torque, range of motion (ROM), and control bandwidth for task-specific gait training. While the adopted multi-input–multi-output (MIMO) sliding mode (SM) strategy has preliminarily implemented simultaneous control of the exoskeleton’s angular trajectory and compliance, its efficacy with human users during gait cycles has not been investigated. This article presents an improved bi-joint gait rehabilitation exoskeleton (BiGREX) with integrated human hip and knee joints. The results with 12 healthy subjects demonstrated that the system’s compliance can be effectively adjusted while guiding the subjects walking in predefined trajectories. Note to Practitioners —This article was motivated by achieving compliant interaction between PM-actuated exoskeletons and human when conducting task-specific gait training. Due to the intrinsic nonlinearity of PM, it is challenging to establish a mathematical model to precisely predict real-time compliance of the powered joints. This article suggests a new strategy that adopts the average pressure of flexor and extensor PMs as the feedback to synchronously realize the joint position control and compliance regulation. A novel experimental approach was adopted to validate the system capability on adjusting the compliance from human users’ perception. This article provides a new insight between the controlled PM pressure and the desired joint compliance, which would be essential for the future design of PM-actuated exoskeletons

A new dynamic modelling algorithm for pneumatic muscle actuators

Pneumatic muscle actuators (PMAs) have been widely used in wearable robots due to its high power to weight ratio and intrinsic compliance. However, dynamic modelling of PMAs, which is important to control performance, has not been researched extensively. Hence, a testing device was designed and built to investigate PMA's dynamics. The device automates the experimental process by providing motions and recording pressure, force, position and velocity data. The gathered experimental data enable the authors to validate a previous PMA dynamic model. Meanwhile, new models are developed from the original model. Statistical analysis proves that the new models can better represent the PMA dynamics during the experiments