Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization
Robotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic enviro...
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MDPI AG
2021-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/1/21 |
| _version_ | 1797492617561243648 |
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| author | Jiaqi Liu Hongbin Fang Jian Xu |
| author_facet | Jiaqi Liu Hongbin Fang Jian Xu |
| author_sort | Jiaqi Liu |
| collection | DOAJ |
| description | Robotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic environments and variable tasks. This research, therefore, aims to advance the state of the art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed, which includes as many uncertain factors as possible for generality, including human–exoskeleton interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in four characteristic scenarios, in which the following factors are considered: exoskeleton parameter perturbations, human effects, walking terrain switches, and walking speed variations. The results indicate that the proposed controller is superior to the standard PSO algorithm and the conventional PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains, adaptively adjusting PID coefficients when internal or external disturbances are encountered, and improving tracking accuracy in both position and velocity. We also demonstrate that the proposed controller could be used to switch the working mode of the exoskeleton for either performance or an energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system, this research proposes a PSO-based online adaptive PID controller that can be easily implemented in applications. Through rich and practical case studies, the excellent anti-interference capability and environment/task adaptivity of the controller are exemplified. |
| first_indexed | 2024-03-10T01:06:16Z |
| format | Article |
| id | doaj.art-364c92913e374a719a9eadd36e16da2c |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T01:06:16Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-364c92913e374a719a9eadd36e16da2c2023-11-23T14:26:11ZengMDPI AGMachines2075-17022021-12-011012110.3390/machines10010021Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm OptimizationJiaqi Liu0Hongbin Fang1Jian Xu2School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, ChinaInstitute of AI and Robotics, Fudan University, Shanghai 200433, ChinaSchool of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, ChinaRobotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic environments and variable tasks. This research, therefore, aims to advance the state of the art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed, which includes as many uncertain factors as possible for generality, including human–exoskeleton interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in four characteristic scenarios, in which the following factors are considered: exoskeleton parameter perturbations, human effects, walking terrain switches, and walking speed variations. The results indicate that the proposed controller is superior to the standard PSO algorithm and the conventional PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains, adaptively adjusting PID coefficients when internal or external disturbances are encountered, and improving tracking accuracy in both position and velocity. We also demonstrate that the proposed controller could be used to switch the working mode of the exoskeleton for either performance or an energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system, this research proposes a PSO-based online adaptive PID controller that can be easily implemented in applications. Through rich and practical case studies, the excellent anti-interference capability and environment/task adaptivity of the controller are exemplified.https://www.mdpi.com/2075-1702/10/1/21exoskeleton robotmodel-free controladaptive controltrajectory tracking controluncertainties and disturbances |
| spellingShingle | Jiaqi Liu Hongbin Fang Jian Xu Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization Machines exoskeleton robot model-free control adaptive control trajectory tracking control uncertainties and disturbances |
| title | Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization |
| title_full | Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization |
| title_fullStr | Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization |
| title_full_unstemmed | Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization |
| title_short | Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization |
| title_sort | online adaptive pid control for a multi joint lower extremity exoskeleton system using improved particle swarm optimization |
| topic | exoskeleton robot model-free control adaptive control trajectory tracking control uncertainties and disturbances |
| url | https://www.mdpi.com/2075-1702/10/1/21 |
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