Abstract: This paper presents dynamic modelling and control of a linear prismatic series of elastic actuator. The capability of generating large torques is why this actuator is increasingly used in human-assistive robotic systems. Due to having human in the loop, the ac
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Abstract: This paper presents dynamic modelling and control of a linear prismatic series of elastic actuator. The capability of generating large torques is why this actuator is increasingly used in human-assistive robotic systems. Due to having human in the loop, the actuator requires precise control. A fractional PID controller known for its improved performance is used for the control, due to having additional degrees of freedom than the classical PID. The actuator has one servo driver and five controller gains to be tuned. The gains are optimized using both Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithms (ICA). Comparison of the results from the two optimization methods illustrates that the PSO tuned FOPID controller has a slightly better performance, faster convergence and better settling time. Next, the PSO tuned controller is compared with a Genetic Algorithm (GA) tuned PID controller. It is shown that the PSO tuned FOPID controller continues to offer better performance, especially in terms of its rise time and settling time.Keywords: Series elastic actuator, Fractional order PID, Imperialist competitive algorithm, Particle swarm optimization, Control
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