Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation
The closed-loop optimal control systems using the receding horizon control (RHC) structure make predictions based on a process model (PM) to calculate the current control output. In many applications, the optimal prediction over the current prediction horizon is calculated using a metaheuristic algo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Automation |
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| Online Access: | https://www.mdpi.com/2673-4052/3/1/5 |
| _version_ | 1827649887677710336 |
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| author | Viorel Mînzu Iulian Arama |
| author_facet | Viorel Mînzu Iulian Arama |
| author_sort | Viorel Mînzu |
| collection | DOAJ |
| description | The closed-loop optimal control systems using the receding horizon control (RHC) structure make predictions based on a process model (PM) to calculate the current control output. In many applications, the optimal prediction over the current prediction horizon is calculated using a metaheuristic algorithm, such as an evolutionary algorithm (EA). The EAs, as other population-based metaheuristics, have large computational complexity. When integrated into the controller, the EA is carried out at each sampling moment and subjected to a time constraint: the execution time should be smaller than the sampling period. This paper proposes a software module integrated into the controller, called at each sampling moment. The module estimates using the PM integration the future process states, over a short time horizon, for different control input values covering the given technological interval. Only a narrower interval is selected for a ‘good’ evolution of the process, based on the so-called ‘state quality criterion’. The controller will consider only a shrunk control output range for the current sampling period. EA will search for its best prediction inside a smaller domain that does not cause the convergence to be affected. Simulations prove that the computational complexity of the controller will decrease. |
| first_indexed | 2024-03-09T20:06:49Z |
| format | Article |
| id | doaj.art-791249e113dd4fd6ac8d607683ea59e2 |
| institution | Directory Open Access Journal |
| issn | 2673-4052 |
| language | English |
| last_indexed | 2024-03-09T20:06:49Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Automation |
| spelling | doaj.art-791249e113dd4fd6ac8d607683ea59e22023-11-24T00:28:28ZengMDPI AGAutomation2673-40522022-01-01319511510.3390/automation3010005Optimal Control Systems Using Evolutionary Algorithm-Control Input Range EstimationViorel Mînzu0Iulian Arama1Control and Electrical Engineering Department, “Dunarea de Jos” University, 800008 Galati, RomaniaInformatics Department, “Danubius” University, 800654 Galati, RomaniaThe closed-loop optimal control systems using the receding horizon control (RHC) structure make predictions based on a process model (PM) to calculate the current control output. In many applications, the optimal prediction over the current prediction horizon is calculated using a metaheuristic algorithm, such as an evolutionary algorithm (EA). The EAs, as other population-based metaheuristics, have large computational complexity. When integrated into the controller, the EA is carried out at each sampling moment and subjected to a time constraint: the execution time should be smaller than the sampling period. This paper proposes a software module integrated into the controller, called at each sampling moment. The module estimates using the PM integration the future process states, over a short time horizon, for different control input values covering the given technological interval. Only a narrower interval is selected for a ‘good’ evolution of the process, based on the so-called ‘state quality criterion’. The controller will consider only a shrunk control output range for the current sampling period. EA will search for its best prediction inside a smaller domain that does not cause the convergence to be affected. Simulations prove that the computational complexity of the controller will decrease.https://www.mdpi.com/2673-4052/3/1/5optimal controlmetaheuristic algorithmsevolutionary algorithmssimulation |
| spellingShingle | Viorel Mînzu Iulian Arama Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation Automation optimal control metaheuristic algorithms evolutionary algorithms simulation |
| title | Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation |
| title_full | Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation |
| title_fullStr | Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation |
| title_full_unstemmed | Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation |
| title_short | Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation |
| title_sort | optimal control systems using evolutionary algorithm control input range estimation |
| topic | optimal control metaheuristic algorithms evolutionary algorithms simulation |
| url | https://www.mdpi.com/2673-4052/3/1/5 |
| work_keys_str_mv | AT viorelminzu optimalcontrolsystemsusingevolutionaryalgorithmcontrolinputrangeestimation AT iulianarama optimalcontrolsystemsusingevolutionaryalgorithmcontrolinputrangeestimation |