Actuators used in robot arms need to be powerful, precise, and safe. We present the design, implementation, and control of a novel rotary hybrid pneumatic-electric actuator (HPEA) for use in robot arms, and collaborative robots in particular (also known as ''cobots''). This HPEA is capable of producing torque 3.5 times larger than existing HPEA designs while maintaining low mechanical impedance (due to low values of friction and inertia) and inherent safety. The HPEA prototype has 450 times less inertia and 15 times less static friction in comparison to a conventional robot actuator with similar maximum continuous output torque. The HPEA combines the large slow torque generated by four pneumatic cylinders, connected to the output shaft via rack and pinion gears, with the small fast torque generated by a small DC motor directly connected to the output shaft. The direct connection of the motor avoids the higher cost and lower precision caused by a gearbox or harmonic drive. The control system consists of an outer position control loop and two inner pressure control loops. High precision position tracking control is achieved due to the combination of a model-based pressure controller, model-based position controller, adaptive friction compensator, and offline payload estimator. Experiments were performed with the actuator prototype rotating a link and payload in the vertical plane. Averaged over five tests, a root-mean-square error of 0.024 • and a steady-state error (SSE) of 0.0045 • were achieved for a fast multi-cycloidal trajectory. This SSE is almost ten times smaller than the best value reported for previous HPEAs. An offline payload estimation algorithm is used to improve the control system's robustness. Finally, the superior safety of the HPEA is shown by modeling and simulating a constrained head-robot impact, and comparing the result with similar electric and pneumatic actuators. INDEX TERMS Collaborative robots, hybrid pneumatic-electric actuator, impact modeling, pneumatic actuators, position control, robot control.

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The increasing use of robots operating close to people has made human-robot collisions more likely. In this paper, strategies intended to reduce the impact force to a safe level, without sacrificing the robot's performance, are investigated. The strategies can be applied to a robot arm without modifying its internal hardware. They include the existing strategies: lowering the actuator controller's stiffness; actuator switched off upon impact detection; withdrawing the arm upon impact detection; and adding a compliant cover. We also propose the novel strategy of limiting the controller's feedback term. The collision scenario studied is a robot arm colliding with a person's constrained head. An improved lumped parameter model of the constrained impact is proposed. Simulation results are included for a UR5 collaborative robot. Sixteen combinations of the impact force reduction strategies are compared. The results show that using a high stiffness controller with a feedback limit and compliant cover reduces the impact force to a safe level, and achieves precise trajectory tracking.

It has been shown that hybrid pneumatic electric actuators (HPEAs) can provide both accurate position control and high inherent safety, due to their low mechanical impedance; making them a suitable choice to be used in applications such as collaborative robots. HPEAs are redundant actuators that combine the large force, low bandwidth characteristics of pneumatic actuators with the large bandwidth, small force characteristics of electric actuators. If these characteristics are mathematically modelled, input allocation techniques can improve the HPEA performance by intelligently distributing the required input (force or torque) between the redundant actuators. In this study, after developing a model for a HPEA-driven system, a model-predictive control (MPC) approach is designed that employ this model and solve the position tracking and input allocation problem using convex optimization. Another approach based on conventional linear controllers is included and compared. Although the linear controller was more computationally-efficient, it was inferior to the MPC-based controller in position tracking and force allocation performance. The MPC-based controller with a two-layer structure reduced the position RMSE by 59%, the mean absolute electric actuator force by 36%, and the mean absolute pneumatic actuator force by 24% relative to the linear controller. It can also be computed fast enough for real-time operation.

Hybrid pneumatic–electric actuators (HPEAs) are redundant actuators that combine the large force, low bandwidth characteristics of pneumatic actuators with the large bandwidth, small force characteristics of electric actuators. It has been shown that HPEAs can provide both accurate position control and high inherent safety, due to their low mechanical impedance, making them a suitable choice for driving the joints of assistive, collaborative, and service robots. If these characteristics are mathematically modeled, input allocation techniques can improve the HPEA’s performance by distributing the required input (force or torque) between the redundant actuators in accordance with each actuator’s advantages and limitations. In this paper, after developing a model for a HPEA-driven system, three novel model-predictive control (MPC) approaches are designed that solve the position tracking and input allocation problem using convex optimization. MPC is utilized since the input allocation can be embedded within the motion controller design as a single optimization problem. A fourth approach based on conventional linear controllers is included as a comparison benchmark. The first MPC approach uses a model that includes the dynamics of the payload and pneumatics; and performs the motion control using a single loop. The latter methods simplify the MPC law by separating the position and pressure controllers. Although the linear controller was the most computationally efficient, it was inferior to the MPC-based controllers in position tracking and force allocation performance. The third MPC-based controller design demonstrated the best position tracking with RMSE of 46%, 20%, and 55% smaller than the other three approaches. It also demonstrated sufficient speed for real-time operation.