Abstract
Tuning of a proportional-integral-derivative (PID) controller for a complex multi-joint structure, such as an exoskeleton, using conventional methods is difficult and imprecise. In this paper, an optimal PID tuning method for a 3-dimensional model of a lower-limb human exoskeleton in gait training condition is presented. The dynamic equation of the human-exoskeleton is determined using a Lagrangian approach, and its transfer function is established in a closed-loop control system. PID controller gains, initialized by the Ziegler–Nichols (Z-N) method, are used as the input to an adaptive particle swarm optimization (APSO) algorithm for minimizing the multi-joint trajectory error. The optimized controller is tested in the Gazebo virtual environment and compared with the Z-N and conventional optimization methods. The numerical analysis shows that the PID controller tuned by a combination of Z-N and APSO improves the performance of a lower-limb human exoskeleton in gait training.
Highlights
Stroke and spinal cord injury are the main causes of disability in the elderly [1,2], creating problems in daily life
Daachi et al [7] controlled one degree of freedom (DoF) lower-limb knee joint orthosis using a neural network based on adaptive control using the Lyapunov approach for rehabilitation reasons
This paper presents the tuning of PID controller gains by combining Z-N and adaptive particle swarm optimization (APSO)
Summary
Stroke and spinal cord injury are the main causes of disability in the elderly [1,2], creating problems in daily life. Belkadi et al [16] developed an optimal method to obtain the parameters of the PID controller and verified the performance in a simulation using PSO with random initialization. Pan et al [20] presented the fuzzy-PID algorithm for the nonlinear dynamics and uncertainties of the human exoskeleton robot. Their method was verified in a co-simulation technique combining ADAMS and MATLAB/Simulink. Implemented the grasshopper optimization algorithm (GOA) to obtain the PID and fuzzy-PID controller parameters for a multi-area interconnected microgrid power system They compared two different PID controllers and concluded that fuzzy-PID produced better dynamic responses compared to a PID controller tuned by GOA. The tuned PID was verified in the 3-dimensional virtual simulation environment and compared with the conventional method
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