Pub Date : 2025-12-18DOI: 10.1016/j.automatica.2025.112738
Corrado Possieri, Mario Sassano
The linear quadratic regulator problem is solved here within the policy space alone. To this end, a quadratic algebraic equation is envisioned to comprise only the entries of the optimal feedback matrix as unknown variables. As a consequence, the resulting equation contains, in general, much fewer quadratic equations than the corresponding Algebraic Riccati Equation and it permits the characterization of all the symmetric solutions of the latter. Furthermore, it is shown that the derived policy equation is amenable for an iterative solution via Newton’s method, which yields updated values identical to those obtained via the celebrated Kleinman’s algorithm.
{"title":"Solving the linear quadratic regulator problem in the policy space: The Policy Algebraic Riccati Equation","authors":"Corrado Possieri, Mario Sassano","doi":"10.1016/j.automatica.2025.112738","DOIUrl":"10.1016/j.automatica.2025.112738","url":null,"abstract":"<div><div>The linear quadratic regulator problem is solved here within the policy space alone. To this end, a quadratic algebraic equation is envisioned to comprise only the entries of the optimal feedback matrix as unknown variables. As a consequence, the resulting equation contains, in general, much fewer quadratic equations than the corresponding Algebraic Riccati Equation and it permits the characterization of all the symmetric solutions of the latter. Furthermore, it is shown that the derived policy equation is amenable for an iterative solution via Newton’s method, which yields updated values identical to those obtained via the celebrated Kleinman’s algorithm.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112738"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.automatica.2025.112764
Wenxue Zhang , Ali Hajbabaie , Massinissa Amrouche , Yichuan Li , Dusan M. Stipanovic
This paper presents a novel closed-form feedback control design with mathematically proven collision-free maneuvers for four-wheel car-like vehicles. New avoidance and switching functions are constructed to avoid various types of obstacles as well as obey road motion constraints. The key distinction lies in the activation of avoidance control, which is triggered based on the derivative of the collision risk assessment function, rather than relying on the assessment function itself. This strategy mitigates overly conservative avoidance maneuvers while maintaining system safety with no compromise. Auxiliary trajectory with avoidance information feedback is established to balance tracking and avoidance. Furthermore, the inclusion of relative motion information in the switching functions not only makes the avoidance response smoother but also improves the overall dynamic performance. In technical terms and contributions, this paper offers theoretical guarantees for system stability and safety through Lyapunov analysis. The effectiveness of the proposed algorithm is further demonstrated through two examples: one showcasing cooperative avoidance in a dense obstacle environment, and the other highlighting safe overtaking and passing on a curved road.
{"title":"A closed-form avoidance control for safe maneuvering of multiple car-like vehicles","authors":"Wenxue Zhang , Ali Hajbabaie , Massinissa Amrouche , Yichuan Li , Dusan M. Stipanovic","doi":"10.1016/j.automatica.2025.112764","DOIUrl":"10.1016/j.automatica.2025.112764","url":null,"abstract":"<div><div>This paper presents a novel closed-form feedback control design with mathematically proven collision-free maneuvers for four-wheel car-like vehicles. New avoidance and switching functions are constructed to avoid various types of obstacles as well as obey road motion constraints. The key distinction lies in the activation of avoidance control, which is triggered based on the derivative of the collision risk assessment function, rather than relying on the assessment function itself. This strategy mitigates overly conservative avoidance maneuvers while maintaining system safety with no compromise. Auxiliary trajectory with avoidance information feedback is established to balance tracking and avoidance. Furthermore, the inclusion of relative motion information in the switching functions not only makes the avoidance response smoother but also improves the overall dynamic performance. In technical terms and contributions, this paper offers theoretical guarantees for system stability and safety through Lyapunov analysis. The effectiveness of the proposed algorithm is further demonstrated through two examples: one showcasing cooperative avoidance in a dense obstacle environment, and the other highlighting safe overtaking and passing on a curved road.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112764"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.automatica.2025.112786
José C. Geromel , Eduardo V.L. Nunes , Liu Hsu
This paper presents a novel procedure for robust control design of linear time-invariant systems using a Multivariable Generalized Super-Twisting Algorithm (MGSTA). The proposed approach addresses robust stability and performance conditions, considering convex bounded parameter uncertainty in all matrices of the plant state-space realization and Lipschitz exogenous disturbances. The primary characteristic of the closed-loop system, finite-time convergence, is thoroughly examined and evaluated. The design conditions, obtained through the proposal of a novel max-type non-differentiable Lyapunov function, are formulated as Linear Matrix Inequalities (LMIs), which can be efficiently solved using existing computational tools. A fault-tolerant MGSTA control is designed for a mechanical system, illustrating the efficacy of the proposed LMI-based approach.
{"title":"Multivariable Generalized Super-Twisting Algorithm robust control of linear time-invariant systems","authors":"José C. Geromel , Eduardo V.L. Nunes , Liu Hsu","doi":"10.1016/j.automatica.2025.112786","DOIUrl":"10.1016/j.automatica.2025.112786","url":null,"abstract":"<div><div>This paper presents a novel procedure for robust control design of linear time-invariant systems using a Multivariable Generalized Super-Twisting Algorithm (MGSTA). The proposed approach addresses robust stability and performance conditions, considering convex bounded parameter uncertainty in all matrices of the plant state-space realization and Lipschitz exogenous disturbances. The primary characteristic of the closed-loop system, finite-time convergence, is thoroughly examined and evaluated. The design conditions, obtained through the proposal of a novel max-type non-differentiable Lyapunov function, are formulated as Linear Matrix Inequalities (LMIs), which can be efficiently solved using existing computational tools. A fault-tolerant MGSTA control is designed for a mechanical system, illustrating the efficacy of the proposed LMI-based approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112786"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.automatica.2025.112763
Nikolaus Vertovec , Kostas Margellos , Maria Prandini
We consider a moving target that we seek to learn from samples. Our results extend randomized techniques developed in control and optimization for a constant target to the case where the target is changing. We derive a novel bound on the number of samples that are required to construct a probably approximately correct (PAC) estimate of the target. Furthermore, when the moving target is a convex polytope, we provide a constructive method of generating the PAC estimate using a mixed integer linear program (MILP). The proposed method is demonstrated on an application to autonomous emergency braking.
{"title":"Finite sample learning of moving targets","authors":"Nikolaus Vertovec , Kostas Margellos , Maria Prandini","doi":"10.1016/j.automatica.2025.112763","DOIUrl":"10.1016/j.automatica.2025.112763","url":null,"abstract":"<div><div>We consider a moving target that we seek to learn from samples. Our results extend randomized techniques developed in control and optimization for a constant target to the case where the target is changing. We derive a novel bound on the number of samples that are required to construct a probably approximately correct (PAC) estimate of the target. Furthermore, when the moving target is a convex polytope, we provide a constructive method of generating the PAC estimate using a mixed integer linear program (MILP). The proposed method is demonstrated on an application to autonomous emergency braking.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112763"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.automatica.2025.112755
Zheng Ning , Shuangshuang Yu , Chao Ge , Ge Chen
Two-component chemical self-assembly has been a decisive method of creating various structures and is the basis of the superlattice in material science; however because the process of self-assembly is tangled and hard to describe, there is little theoretical research, and mathematical theory is particularly lacking currently. Based on some assumptions of the hard sphere model and Coulombic assembly experiment, this paper presents a novel multi-agent system for two-component chemical self-assembly using a group of stochastic differential equations. The existence and uniqueness theorem of the solution is also presented. A structure control problem is proposed by taking the bond-orientational order as the characteristic function of the assembled product structure, and the temperature as the control input. Our simulations show that the bond-orientational order of self-assembled product is heavily related to the final temperature, but has little relevance to the control path from the initial temperature to the final temperature.
{"title":"Two-component nanoparticle self-assembly: Modeling, analysis, and structure control","authors":"Zheng Ning , Shuangshuang Yu , Chao Ge , Ge Chen","doi":"10.1016/j.automatica.2025.112755","DOIUrl":"10.1016/j.automatica.2025.112755","url":null,"abstract":"<div><div>Two-component chemical self-assembly has been a decisive method of creating various structures and is the basis of the superlattice in material science; however because the process of self-assembly is tangled and hard to describe, there is little theoretical research, and mathematical theory is particularly lacking currently. Based on some assumptions of the hard sphere model and Coulombic assembly experiment, this paper presents a novel multi-agent system for two-component chemical self-assembly using a group of stochastic differential equations. The existence and uniqueness theorem of the solution is also presented. A structure control problem is proposed by taking the bond-orientational order as the characteristic function of the assembled product structure, and the temperature as the control input. Our simulations show that the bond-orientational order of self-assembled product is heavily related to the final temperature, but has little relevance to the control path from the initial temperature to the final temperature.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112755"},"PeriodicalIF":5.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.automatica.2025.112747
Pedro Henrique Silva Coutinho , Iury Bessa , Victor Hugo Pereira Rodrigues , Tiago Roux Oliveira
This paper addresses sliding mode control for uncertain multivariable systems. In this sense, we present systematic procedures to design variable structure controllers (VSCs) and unit-vector controllers (UVCs) for stabilization and tracking in finite time. Although these controller classes are known to be effective, the literature lacks systematic design methods. By using appropriate representations of the closed-loop system, we derive sufficient conditions that enable the systematic design of robust sliding mode controllers based on semidefinite programming with linear matrix inequalities (LMIs) constraints. The proposed approach ensures that the origin of the closed-loop system is finite-time stable. Additionally, since the reaching time depends on initial conditions and the decay rate, the design approach uses convex optimization problems to minimize the reaching time for a given set of initial conditions. Numerical examples demonstrate the effectiveness of the proposed techniques.
{"title":"Systematic LMI approaches to design multivariable sliding mode controllers for uncertain systems","authors":"Pedro Henrique Silva Coutinho , Iury Bessa , Victor Hugo Pereira Rodrigues , Tiago Roux Oliveira","doi":"10.1016/j.automatica.2025.112747","DOIUrl":"10.1016/j.automatica.2025.112747","url":null,"abstract":"<div><div>This paper addresses sliding mode control for uncertain multivariable systems. In this sense, we present systematic procedures to design variable structure controllers (VSCs) and unit-vector controllers (UVCs) for stabilization and tracking in finite time. Although these controller classes are known to be effective, the literature lacks systematic design methods. By using appropriate representations of the closed-loop system, we derive sufficient conditions that enable the systematic design of robust sliding mode controllers based on semidefinite programming with linear matrix inequalities (LMIs) constraints. The proposed approach ensures that the origin of the closed-loop system is finite-time stable. Additionally, since the reaching time depends on initial conditions and the decay rate, the design approach uses convex optimization problems to minimize the reaching time for a given set of initial conditions. Numerical examples demonstrate the effectiveness of the proposed techniques.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112747"},"PeriodicalIF":5.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.automatica.2025.112732
Robin Strässer , Manuel Schaller , Karl Worthmann , Julian Berberich , Frank Allgöwer
The Koopman operator serves as the theoretical backbone for machine learning of dynamical control systems, where the operator is heuristically approximated by extended dynamic mode decomposition (EDMD). In this paper, we propose SafEDMD, a novel stability- and feedback-oriented EDMD-based controller design framework. Our approach leverages a reliable surrogate model generated in a data-driven fashion in order to provide closed-loop guarantees. In particular, we establish a controller design based on semi-definite programming with guaranteed stabilization of the underlying nonlinear system. As central ingredient, we derive proportional error bounds that vanish at the origin and are tailored to control tasks. We illustrate the developed method by means of several benchmark examples and highlight the advantages over state-of-the-art methods.
{"title":"SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems","authors":"Robin Strässer , Manuel Schaller , Karl Worthmann , Julian Berberich , Frank Allgöwer","doi":"10.1016/j.automatica.2025.112732","DOIUrl":"10.1016/j.automatica.2025.112732","url":null,"abstract":"<div><div>The Koopman operator serves as the theoretical backbone for machine learning of dynamical control systems, where the operator is heuristically approximated by extended dynamic mode decomposition (EDMD). In this paper, we propose SafEDMD, a novel stability- and feedback-oriented EDMD-based controller design framework. Our approach leverages a reliable surrogate model generated in a data-driven fashion in order to provide closed-loop guarantees. In particular, we establish a controller design based on semi-definite programming with guaranteed stabilization of the underlying nonlinear system. As central ingredient, we derive proportional error bounds that vanish at the origin and are tailored to control tasks. We illustrate the developed method by means of several benchmark examples and highlight the advantages over state-of-the-art methods.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112732"},"PeriodicalIF":5.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.automatica.2025.112777
Arnold Tshimanga Banza , Iven Mareels
This manuscript presents a new adaptive sliding mode control (SMC) algorithm for singularly perturbed nonlinear systems subject to matched and unmatched uncertainties. The circumstance of fast sensor dynamics in series with the plant is explored as a case study. It is shown that by establishing a clear time scale separation between the dynamics of the plant, sensors, and adaptive SMC structure and by adequate selection of the proposed control parameters, the overall closed-loop system can converge to a sufficiently small neighbourhood of the origin. Some simulation results are presented to show the effectiveness of the proposed design.
{"title":"Adaptive sliding mode control design for singularly perturbed nonlinear systems under matched and unmatched uncertainties","authors":"Arnold Tshimanga Banza , Iven Mareels","doi":"10.1016/j.automatica.2025.112777","DOIUrl":"10.1016/j.automatica.2025.112777","url":null,"abstract":"<div><div>This manuscript presents a new adaptive sliding mode control (SMC) algorithm for singularly perturbed nonlinear systems subject to matched and unmatched uncertainties. The circumstance of fast sensor dynamics in series with the plant is explored as a case study. It is shown that by establishing a clear time scale separation between the dynamics of the plant, sensors, and adaptive SMC structure and by adequate selection of the proposed control parameters, the overall closed-loop system can converge to a sufficiently small neighbourhood of the origin. Some simulation results are presented to show the effectiveness of the proposed design.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112777"},"PeriodicalIF":5.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.automatica.2025.112759
Bin Zhu, Jiale Tang
The area of spectral analysis has a traditional dichotomy between continuous spectra (spectral densities) which correspond to purely nondeterministic processes, and line spectra (Dirac impulses) which represent sinusoids. While the former case is important in the identification of discrete-time linear stochastic systems, the latter case is essential for the analysis and modeling of time series with notable applications in radar systems. In this paper, we develop a novel approach for line spectral estimation which combines ideas of Georgiou’s filter banks (G-filters) and atomic norm minimization (ANM), a mainstream method for line spectral analysis in the last decade following the theory of compressed sensing. Such a combination is only possible because a Carathéodory–Fejér-type decomposition is available for the covariance matrix of the filter output. The ANM problem can be characterized via semidefinite programming which can be solved efficiently. As a consequence, our optimization scheme can be seen as a substantial generalization of the standard ANM for line spectral estimation. Moreover, our ANM approach with a G-filter has significant advantages over subspace methods because it can work with just one output vector and without a priori knowledge about the number of sinusoids in the input. Simulation results show that our approach performs favorably against the standard ANM, the frequency-selective ANM, and standard subspace methods MUSIC and ESPRIT under a variety of parameter configurations when the G-filter is suitably designed.
{"title":"Line spectral analysis using the G-filter: An atomic norm minimization approach","authors":"Bin Zhu, Jiale Tang","doi":"10.1016/j.automatica.2025.112759","DOIUrl":"10.1016/j.automatica.2025.112759","url":null,"abstract":"<div><div>The area of spectral analysis has a traditional dichotomy between continuous spectra (spectral densities) which correspond to purely nondeterministic processes, and line spectra (Dirac impulses) which represent sinusoids. While the former case is important in the identification of discrete-time linear stochastic systems, the latter case is essential for the analysis and modeling of time series with notable applications in radar systems. In this paper, we develop a novel approach for line spectral estimation which combines ideas of Georgiou’s filter banks (G-filters) and atomic norm minimization (ANM), a mainstream method for line spectral analysis in the last decade following the theory of compressed sensing. Such a combination is only possible because a Carathéodory–Fejér-type decomposition is available for the covariance matrix of the filter output. The ANM problem can be characterized via semidefinite programming which can be solved efficiently. As a consequence, our optimization scheme can be seen as a substantial generalization of the standard ANM for line spectral estimation. Moreover, our ANM approach with a G-filter has significant advantages over subspace methods because it can work with just one output vector and without <em>a priori</em> knowledge about the number of sinusoids in the input. Simulation results show that our approach performs favorably against the standard ANM, the frequency-selective ANM, and standard subspace methods MUSIC and ESPRIT under a variety of parameter configurations when the G-filter is suitably designed.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112759"},"PeriodicalIF":5.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.automatica.2025.112744
Pan Yu , Qiang Wu , Kang-Zhi Liu
A hierarchical optimization-based online learning control scheme is presented to exploit the learning control performance of neural networks (NNs) for nonlinear systems with unknown disturbances. First, leveraging the equivalent-input-disturbance (EID) concept and using only the system output, an NN-assisted control structure is devised. Then, a two-stage learning control framework is developed. Specifically, the first rough stage is for fast dynamic disturbance-rejection control with exponential convergence performance and then the second refined stage is to further refine the performance by online optimization. Further, the closed-loop performance with integrated disturbance-rejection performance is analyzed and a performance-oriented design algorithm is presented. Finally, superiorities of the developed scheme are shown by comparisons of rotary inverted pendulum experiments with other methods.
{"title":"Hierarchical optimization-based online learning control for nonlinear systems with unknown disturbances","authors":"Pan Yu , Qiang Wu , Kang-Zhi Liu","doi":"10.1016/j.automatica.2025.112744","DOIUrl":"10.1016/j.automatica.2025.112744","url":null,"abstract":"<div><div>A hierarchical optimization-based online learning control scheme is presented to exploit the learning control performance of neural networks (NNs) for nonlinear systems with unknown disturbances. First, leveraging the equivalent-input-disturbance (EID) concept and using only the system output, an NN-assisted control structure is devised. Then, a two-stage learning control framework is developed. Specifically, the first rough stage is for fast dynamic disturbance-rejection control with exponential convergence performance and then the second refined stage is to further refine the performance by online optimization. Further, the closed-loop performance with integrated disturbance-rejection performance is analyzed and a performance-oriented design algorithm is presented. Finally, superiorities of the developed scheme are shown by comparisons of rotary inverted pendulum experiments with other methods.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112744"},"PeriodicalIF":5.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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