Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization
This paper presents a tracking control scheme for nonlinear systems with input constraints by combining the continuous-time model predictive control and the feedback linearization. Although there are some similar combinations for nonlinear systems presented in literature, their formulations are comp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/10/5016 |
| _version_ | 1797501778352144384 |
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| author | Yong-Lin Kuo Peeraya Pongpanyaporn |
| author_facet | Yong-Lin Kuo Peeraya Pongpanyaporn |
| author_sort | Yong-Lin Kuo |
| collection | DOAJ |
| description | This paper presents a tracking control scheme for nonlinear systems with input constraints by combining the continuous-time model predictive control and the feedback linearization. Although there are some similar combinations for nonlinear systems presented in literature, their formulations are complex and massive computations are unavoidable. This study aims to simplify the formulations and reduce the computational loads by imposing the Laguerre functions to approximate the control signals. Since the Laguerre functions have the property of orthogonality, the tracking control problem, by applying the combination, leads to a constrained quadratic optimization problem in terms of the coefficients associated with the Laguerre functions, where the input constraints are converted so as to be state-dependent, based on feedback linearization. The Hildreth’s quadratic programming algorithm is used to solve the optimization problem, so as to determine the coefficients. Moreover, this study also summarizes some common linearization schemes and shows their pros and cons. Furthermore, the proposed approach is applied to two illustrative examples, and the control performances are compared with those by linear control strategies combined with those linearization schemes. |
| first_indexed | 2024-03-10T03:24:27Z |
| format | Article |
| id | doaj.art-757d6beacae9425ca23673d24ee0d4d5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:24:27Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-757d6beacae9425ca23673d24ee0d4d52023-11-23T09:56:28ZengMDPI AGApplied Sciences2076-34172022-05-011210501610.3390/app12105016Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback LinearizationYong-Lin Kuo0Peeraya Pongpanyaporn1Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei 106, TaiwanGraduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei 106, TaiwanThis paper presents a tracking control scheme for nonlinear systems with input constraints by combining the continuous-time model predictive control and the feedback linearization. Although there are some similar combinations for nonlinear systems presented in literature, their formulations are complex and massive computations are unavoidable. This study aims to simplify the formulations and reduce the computational loads by imposing the Laguerre functions to approximate the control signals. Since the Laguerre functions have the property of orthogonality, the tracking control problem, by applying the combination, leads to a constrained quadratic optimization problem in terms of the coefficients associated with the Laguerre functions, where the input constraints are converted so as to be state-dependent, based on feedback linearization. The Hildreth’s quadratic programming algorithm is used to solve the optimization problem, so as to determine the coefficients. Moreover, this study also summarizes some common linearization schemes and shows their pros and cons. Furthermore, the proposed approach is applied to two illustrative examples, and the control performances are compared with those by linear control strategies combined with those linearization schemes.https://www.mdpi.com/2076-3417/12/10/5016model predictive controlfeedback linearizationLaguerre functionsinput constraint |
| spellingShingle | Yong-Lin Kuo Peeraya Pongpanyaporn Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization Applied Sciences model predictive control feedback linearization Laguerre functions input constraint |
| title | Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization |
| title_full | Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization |
| title_fullStr | Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization |
| title_full_unstemmed | Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization |
| title_short | Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization |
| title_sort | continuous time nonlinear model predictive tracking control with input constraints using feedback linearization |
| topic | model predictive control feedback linearization Laguerre functions input constraint |
| url | https://www.mdpi.com/2076-3417/12/10/5016 |
| work_keys_str_mv | AT yonglinkuo continuoustimenonlinearmodelpredictivetrackingcontrolwithinputconstraintsusingfeedbacklinearization AT peerayapongpanyaporn continuoustimenonlinearmodelpredictivetrackingcontrolwithinputconstraintsusingfeedbacklinearization |