Indirect adaptive control using the novel online hypervolume-based differential evolution for the four-bar mechanism
•An indirect adaptive control based on multi-objective optimization is proposed for the four-bar mechanism.•A Novel Online Hypervolume-Based Differential Evolution is proposed to handle the conflicting control requirements.•Conflicting control requirements demand multi-objective meta-heuristic optim...
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| Published in | Mechatronics (Oxford) Vol. 69; p. 102384 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.08.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4158 1873-4006 |
| DOI | 10.1016/j.mechatronics.2020.102384 |
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| Abstract | •An indirect adaptive control based on multi-objective optimization is proposed for the four-bar mechanism.•A Novel Online Hypervolume-Based Differential Evolution is proposed to handle the conflicting control requirements.•Conflicting control requirements demand multi-objective meta-heuristic optimization for tuning.•Experimental tests show the reliability of the proposal with the 99% Confidence Interval test.•The effectiveness of the proposal is also compared with state-of-the-art controllers.
Four-bar mechanisms have increased their use in current applications from industrial to rehabilitation systems. These applications become more demanding over time, and the control systems are required to provide them higher accuracy, lower energy consumption, and an extended lifetime, among other conflicting features. In addition to the previously mentioned demands, four-bar mechanisms have highly nonlinear dynamics and are often subject to external loads that make them difficult to control. In this paper, an indirect adaptive control based on online multi-objective optimization is proposed to regulate the speed of the four-bar mechanism and increase its lifetime by smoothing the control action under the effects of uncertainties. This consists of a multi-objective optimization process for the online identification of the model parameters that fulfill the performance demands of the mechanism. In this process, a multi-objective optimization problem is stated and then solved by the novel Online Hypervolume-based Differential Evolution (O-HV-MODE) in such a way that several promising model parameter configurations are found in real-time, with different trade-offs among the performance demands. O-HV-MODE takes advantage of the past problem knowledge to accelerate the search for new solutions and uses the Hypervolume metric to increase their convergence and diversity. Then, a single model parameter configuration is selected based on the application necessities and is further used in the nonlinear compensator of the computed-torque controller, while a fixed-gain PD control loop is used for stabilization. The proposed control is validated through experimental tests and the reliability of the results with the 99% Confidence Interval test. Also, the proposal is compared with state-of-the-art linear and non-linear control approaches. |
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| AbstractList | •An indirect adaptive control based on multi-objective optimization is proposed for the four-bar mechanism.•A Novel Online Hypervolume-Based Differential Evolution is proposed to handle the conflicting control requirements.•Conflicting control requirements demand multi-objective meta-heuristic optimization for tuning.•Experimental tests show the reliability of the proposal with the 99% Confidence Interval test.•The effectiveness of the proposal is also compared with state-of-the-art controllers.
Four-bar mechanisms have increased their use in current applications from industrial to rehabilitation systems. These applications become more demanding over time, and the control systems are required to provide them higher accuracy, lower energy consumption, and an extended lifetime, among other conflicting features. In addition to the previously mentioned demands, four-bar mechanisms have highly nonlinear dynamics and are often subject to external loads that make them difficult to control. In this paper, an indirect adaptive control based on online multi-objective optimization is proposed to regulate the speed of the four-bar mechanism and increase its lifetime by smoothing the control action under the effects of uncertainties. This consists of a multi-objective optimization process for the online identification of the model parameters that fulfill the performance demands of the mechanism. In this process, a multi-objective optimization problem is stated and then solved by the novel Online Hypervolume-based Differential Evolution (O-HV-MODE) in such a way that several promising model parameter configurations are found in real-time, with different trade-offs among the performance demands. O-HV-MODE takes advantage of the past problem knowledge to accelerate the search for new solutions and uses the Hypervolume metric to increase their convergence and diversity. Then, a single model parameter configuration is selected based on the application necessities and is further used in the nonlinear compensator of the computed-torque controller, while a fixed-gain PD control loop is used for stabilization. The proposed control is validated through experimental tests and the reliability of the results with the 99% Confidence Interval test. Also, the proposal is compared with state-of-the-art linear and non-linear control approaches. |
| ArticleNumber | 102384 |
| Author | Aldape-Pérez, Mario Villarreal-Cervantes, Miguel G. Rodríguez-Molina, Alejandro |
| Author_xml | – sequence: 1 givenname: Alejandro surname: Rodríguez-Molina fullname: Rodríguez-Molina, Alejandro email: alejandro.rm@tlalnepantla.tecnm.mx organization: Tecnológico Nacional de México/IT de Tlalnepantla, Research and Postgraduate Division, Estado de México, 54070, México – sequence: 2 givenname: Miguel G. orcidid: 0000-0002-7565-8128 surname: Villarreal-Cervantes fullname: Villarreal-Cervantes, Miguel G. email: mvillarrealc@ipn.mx organization: Postgraduate Department, Instituto Politécnico Nacional, CIDETEC, Mexico City, 07700, Mexico – sequence: 3 givenname: Mario surname: Aldape-Pérez fullname: Aldape-Pérez, Mario email: maldape@ipn.mx organization: Postgraduate Department, Instituto Politécnico Nacional, CIDETEC, Mexico City, 07700, Mexico |
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| Keywords | Adaptive tuning Multi-objective optimization Meta-heuristics Intelligent control Four-bar mechanism |
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| SubjectTerms | Adaptive tuning Four-bar mechanism Intelligent control Meta-heuristics Multi-objective optimization |
| Title | Indirect adaptive control using the novel online hypervolume-based differential evolution for the four-bar mechanism |
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