Pneumatic artificial muscles (PAMs) are soft and flexible linear pneumatic actuators which produce human muscle like actuation. Due to these properties, the muscle actuators have an adaptable compliance for various robotic platforms as well as medical applications. While a variety of possible actuation schemes are present, there is still a need for the development of a soft actuator that is very light-weight, compact, and flexible with high power-to-weight ratio. To achieve this, the development of the PAM actuators has become an interesting topic for many researchers. In this review, the development of the different kinds of PAM available to date are presented along with manufacturing process and the operating principle. The various force models for artificial muscle presented in the literature are broadly reviewed with the constraints. Furthermore, the applications of PAM are included and classified based on the fields of biorobotics, medicine, and industry, along with advanced medical instrumentation. Finally, the needful improvements in terms of the dynamics of the muscle are discussed for the precise control of the PAMs as per the requirements for the applications. This review will be helpful for researchers working in the field of robotics and for designers to develop new type of artificial muscle depending on the applications.

Post-neurological disorder, passive-assist training disregards human involvement during robot-aided lower-limb rehabilitation. This work presents a novel human-cooperative framework based on the admittance and trajectory control scheme to administer the subject–exoskeleton interaction for pediatric gait rehabilitation. Initially, the mechanical design and dynamic analysis of an existing exoskeleton system are briefly attended. Thereafter, an admittance model is designed in the outer loop, which recasts the desired human trajectory into a reference trajectory. As an inner-loop control scheme, a robust adaptive backstepping controller is designed to trace the reference gait trajectory under parametric uncertainties and external disturbances. Lyapunov stability analysis is solved to guarantee the uniform boundedness of the control signals. Moreover, the well-known problem of “explosion of complexity” and “overparameterization” is avoided through the design of the robust adaptive backstepping control scheme. The performance of the proposed robust adaptive backstepping-based human-cooperative control is studied under the low- and high-level admittance model. Finally, the effectiveness of the proposed control is validated with a variable structure adaptive robust-based human-cooperative control. The co-simulation results show that the proposed control with low-level admittance allows the subject to participate in the training process more frankly. The proposed robust adaptive backstepping-based human-cooperative control tracks the reference gait more promisingly than the variable structure adaptive robust-based human-cooperative control for both admittance levels.

In this work, a generalized low-level controller is presented for sensor collection, motor input, and networking with a high-level controller. In hierarchically controlled exoskeletal systems, which utilize series elastic actuators (SEAs), the hardware for sensor collection and motor command is separated from the computationally expensive high-level controller algorithm. The low-level controller is a hardware device that must collect sensor feedback, condition and filter the measurements, send actuator inputs, and network with the high-level controller at a real-time rate. This research outlines the hardware of two printed circuit board (PCB) designs for collecting and conditioning sensor feedback from two SEA subsystems and an inertial measurement unit (IMU). The SEAs have a joint and motor encoder, motor current, and force sensor feedback that can be measured using the proposed generalized low-level controller presented in this work. In addition, the high and low-level networking approach is discussed in detail, with a full breakdown of the data storage within a communication frame during the run-time operation. The challenges of device synchronization and updates rates of high and low-level controllers are also discussed. Further, the low-level controller was validated using a pendulum test bed, complete with full sensor feedback, including IMU results for two open-loop scenarios. Moreover, this work can be extended to other hierarchically controlled robotic systems that utilize SEA subsystems, such as humanoid robots, assistive rehabilitation robots, training simulators, and robotic-assisted surgical devices. The hardware and software designs presented in this work are available open source to enable researchers with a direct solution for data acquisition and the control of low-level devices in a robotic system.

In this work, a numerical analysis has been carried out to study the nonlinear dynamics of a system with pneumatic artificial muscle (PAM). The system is modeled as a single degree-of-freedom system and the governing nonlinear equation of motion has been derived to study the various responses of the system. The system is subjected to hard excitation and hence the subharmonic and superharmonic resonance conditions have been studied. The second-order method of multiple scales (MMS) has been used to find the response, stability, and bifurcations of the system. The effect of various system parameters on the system response has been studied using time response, phase portraits, and basin of attraction. In these responses, while the saddle node bifurcation is found in both super and subharmonic resonance conditions, the Hopf bifurcation is found only in superharmonic resonance condition. By changing different system parameters, it has been shown that the response with three periods leads to chaotic response for superharmonic resonance condition. This study will find applications in the design of PAM actuators.