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.

This paper presents Artificial Neural Network (ANN) as an optimization tool in tuning Proportional-Integral-Derivative (PID) controller's gain of a multi-joints Lower Limb Exoskeleton (LLE) for gait rehabilitation. The interest in wearable post-stroke and spinal cord injury rehabilitation devices such as LLE has been increasing due to the demand for assistive technologies for paralyze patients and to meet the concerns in the increasing number of ageing society. The dynamic of three degree of freedom LLE was determined using Euler-Lagrange equation, and PID parameters were initially tuned using the Ziegler-Nichols (ZN) method. The paper compares different ANN-based algorithms in tuning PID controller's gain for LLE applications. The method compared and evaluated with other methods and dynamic systems in the literature. ANN-based algorithms, Gradient Descent, Levenberg-Marquardt, and Scaled Conjugate Gradient, are utilized for PID tuning of each joint in the LLE model. The result shows faster convergence and improves step response characteristics for each controlled joint model. The overshoot values found to be 0.3126%, 0.6335%, and 0.2619% compared to the ZN method with 10.5582%, 15.1643%, and 11.8511% for hip, knee, and ankle joints, respectively. It can be ascertained that the PID controlled of LLE has been optimally tuned significantly by different ANN methods, which reduced its steady-state errors.

It is necessary to control the movement of a complex multi-joint structure such as a robotic arm in order to reach a target position accurately in various applications. In this paper, a hybrid optimal Genetic–Swarm solution for the Inverse Kinematic (IK) solution of a robotic arm is presented. Each joint is controlled by Proportional–Integral–Derivative (PID) controller optimized with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called Genetic–Swarm Optimization (GSO). GSO solves the IK of each joint while the dynamic model is determined by the Lagrangian. The tuning of the PID is defined as an optimization problem and is solved by PSO for the simulated model in a virtual environment. A Graphical User Interface has been developed as a front-end application. Based on the combination of hybrid optimal GSO and PID control, it is ascertained that the system works efficiently. Finally, we compare the hybrid optimal GSO with conventional optimization methods by statistic analysis.

This paper presents a rigid-flexible composite of bionic hand structure design scheme solution for solving the problem of low load on the soft gripping hand. The bionic hand was designed based on the Fast Pneumatic Network (FPN) approach, which can produce a soft finger bending drive mechanism. A soft finger bending driver was developed and assembled into a human-like soft gripping hand which includes a thumb for omnidirectional movement and four modular soft fingers. An experimental comparison of silicone rubber materials with different properties was conducted to determine suitable materials. The combination of 3D printing technology and mold pouring technology was adopted to complete the prototype preparation of the bionic hand. Based on the second-order Yeoh model, a soft bionic finger mathematical model was established, and ABAQUS simulation analysis software was used for correction to verify the feasibility of the soft finger bending. We adopted a pneumatic control scheme based on a motor micro-pump and developed a human–computer interface through LabView. A comparative experiment was carried out on the bending performance of the finger, and the experimental data were analyzed to verify the accuracy of the mathematical model and simulation. In this study, the control system was designed, and the human-like finger gesture and grasping experiments were carried out.