Pub Date : 2025-12-26DOI: 10.1109/LCSYS.2025.3648776
Hang Zhang;Xiangru Xu
This letter addresses the safe control synthesis problem for neural network control systems subject to bounded unknown disturbances and known exogenous inputs. A forward reachability analysis method is developed to over-approximate the system’s forward reachable sets using constrained zonotopes, where the control sequence appears linearly in both the zonotope center and the right-hand side of the associated equality constraints. Based on these over-approximations, a quadratically constrained program and its convexification are formulated to synthesize control sequences that guarantee safety. A numerical example demonstrates the effectiveness of the proposed approach.
{"title":"Safe Control Synthesis for Neural Network Control Systems via Constrained Zonotopes","authors":"Hang Zhang;Xiangru Xu","doi":"10.1109/LCSYS.2025.3648776","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3648776","url":null,"abstract":"This letter addresses the safe control synthesis problem for neural network control systems subject to bounded unknown disturbances and known exogenous inputs. A forward reachability analysis method is developed to over-approximate the system’s forward reachable sets using constrained zonotopes, where the control sequence appears linearly in both the zonotope center and the right-hand side of the associated equality constraints. Based on these over-approximations, a quadratically constrained program and its convexification are formulated to synthesize control sequences that guarantee safety. A numerical example demonstrates the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3071-3076"},"PeriodicalIF":2.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1109/LCSYS.2025.3648432
Andreas Köhler;Ping Zhang
This letter proposes a set of novel sufficient conditions that guarantee three behavioral properties in signal interpreted Petri nets (SIPNs), i.e., liveness, reachability, and reversibility. SIPNs provide a modeling formalism for representing the control algorithm of discrete manufacturing systems. The liveness, reachability, and reversibility properties ensure that the desired control actions remain perpetually executable, the system states are reachable, and that the system can always return to its initial state, respectively. The sufficient conditions are derived based on the Petri net state equation and the enabling rules of the transitions in SIPNs. Moreover, it is shown how the reachability and reversibility can be computationally verified based on an integer linear programming problem. The computational complexity for verifying the properties is polynomial with respect to the number of markings in the SIPN when the reachable set is already available.
{"title":"Liveness, Reachability, and Reversibility of Signal Interpreted Petri Nets","authors":"Andreas Köhler;Ping Zhang","doi":"10.1109/LCSYS.2025.3648432","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3648432","url":null,"abstract":"This letter proposes a set of novel sufficient conditions that guarantee three behavioral properties in signal interpreted Petri nets (SIPNs), i.e., liveness, reachability, and reversibility. SIPNs provide a modeling formalism for representing the control algorithm of discrete manufacturing systems. The liveness, reachability, and reversibility properties ensure that the desired control actions remain perpetually executable, the system states are reachable, and that the system can always return to its initial state, respectively. The sufficient conditions are derived based on the Petri net state equation and the enabling rules of the transitions in SIPNs. Moreover, it is shown how the reachability and reversibility can be computationally verified based on an integer linear programming problem. The computational complexity for verifying the properties is polynomial with respect to the number of markings in the SIPN when the reachable set is already available.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3131-3136"},"PeriodicalIF":2.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1109/LCSYS.2025.3649131
Guojie Li;Ping Zhou
In this letter, a prescribed performance function-based data-driven virtual setpoint P-type controller (PPF-DDVSPC) is proposed for single-input single-output (SISO) systems with nonlinear nonaffine dynamics. First, the original model with error constraint is converted into an unconstrained form using the prescribed performance function and error transformation technique. A virtual setpoint updating law, nested within the outer layer of the traditional P-type controller, is developed based on the newly defined unconstrained variable to limit the tracking error. Then, the unconstrained model and virtual setpoint law are converted into the available equivalent linear data models through dynamic linearization technology. The unknown pseudo-partial derivatives in the two models are estimated utilizing the modified projection algorithm. Finally, the P-type controller with prescribed performance is obtained by replacing the actual setpoint signal with the resulting virtual setpoint law. The bounded input and bounded output (BIBO) stability of the system is demonstrated by the contraction mapping principle, which ensures that the constraint conditions are satisfied. The effectiveness and robustness of the PPF-DDVSPC method are validated through a data-driven simulation of the blast furnace ironmaking process.
{"title":"Data-Driven Virtual Setpoint P-Type Control With Prescribed Performance Function","authors":"Guojie Li;Ping Zhou","doi":"10.1109/LCSYS.2025.3649131","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3649131","url":null,"abstract":"In this letter, a prescribed performance function-based data-driven virtual setpoint P-type controller (PPF-DDVSPC) is proposed for single-input single-output (SISO) systems with nonlinear nonaffine dynamics. First, the original model with error constraint is converted into an unconstrained form using the prescribed performance function and error transformation technique. A virtual setpoint updating law, nested within the outer layer of the traditional P-type controller, is developed based on the newly defined unconstrained variable to limit the tracking error. Then, the unconstrained model and virtual setpoint law are converted into the available equivalent linear data models through dynamic linearization technology. The unknown pseudo-partial derivatives in the two models are estimated utilizing the modified projection algorithm. Finally, the P-type controller with prescribed performance is obtained by replacing the actual setpoint signal with the resulting virtual setpoint law. The bounded input and bounded output (BIBO) stability of the system is demonstrated by the contraction mapping principle, which ensures that the constraint conditions are satisfied. The effectiveness and robustness of the PPF-DDVSPC method are validated through a data-driven simulation of the blast furnace ironmaking process.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3089-3094"},"PeriodicalIF":2.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1109/LCSYS.2025.3648777
Ilia Nasiriziba;Matthew F. Singh
Controlling complex, nonlinear systems, such as the brain, presents a fundamental challenge that requires simplified models for practical controller design. Traditional approaches often fail when these systems operate far from steady states under noise and changing inputs. Different control strategies drive systems into distinct behavioral regimes, each requiring a specific approximation. Rather than imposing a single approximation, this letter introduces ergodic quasilinearization (EQL), which automatically identifies the appropriate linear model for each operating scenario. EQL generates adaptive linear models whose parameters adjust based on the system’s long-term statistical behavior under varying inputs and noise levels. These statistics are derived analytically from the steady-state equalities, eliminating the need for repeated computation of the full nonlinear dynamics. The effectiveness of EQL is demonstrated on large-scale brain network models, where traditional methods encounter difficulties due to complex nonlinearities and high dimensionality. Conventional linearization methods perform well under fixed conditions but lose accuracy when control strategies change the operating regime. In contrast, EQL maintains accuracy across diverse operating scenarios, supporting robust controller design for systems that rarely reach simple steady states. We demonstrate the power of EQL in predicting brain-model responses to complex stimulation protocols and in identifying an optimal open-loop control for reproducing target brain-activity patterns.
{"title":"Ergodic Quasilinearization and Control for Brain Dynamics","authors":"Ilia Nasiriziba;Matthew F. Singh","doi":"10.1109/LCSYS.2025.3648777","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3648777","url":null,"abstract":"Controlling complex, nonlinear systems, such as the brain, presents a fundamental challenge that requires simplified models for practical controller design. Traditional approaches often fail when these systems operate far from steady states under noise and changing inputs. Different control strategies drive systems into distinct behavioral regimes, each requiring a specific approximation. Rather than imposing a single approximation, this letter introduces ergodic quasilinearization (EQL), which automatically identifies the appropriate linear model for each operating scenario. EQL generates adaptive linear models whose parameters adjust based on the system’s long-term statistical behavior under varying inputs and noise levels. These statistics are derived analytically from the steady-state equalities, eliminating the need for repeated computation of the full nonlinear dynamics. The effectiveness of EQL is demonstrated on large-scale brain network models, where traditional methods encounter difficulties due to complex nonlinearities and high dimensionality. Conventional linearization methods perform well under fixed conditions but lose accuracy when control strategies change the operating regime. In contrast, EQL maintains accuracy across diverse operating scenarios, supporting robust controller design for systems that rarely reach simple steady states. We demonstrate the power of EQL in predicting brain-model responses to complex stimulation protocols and in identifying an optimal open-loop control for reproducing target brain-activity patterns.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3101-3106"},"PeriodicalIF":2.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-25DOI: 10.1109/LCSYS.2025.3648436
Shima Sadat Mousavi;Xiao Tan;Aaron D. Ames
This letter develops certificates that propagate compatibility of multiple control barrier function (CBF) constraints from sampled vertices to their convex hull. Under mild concavity and affinity assumptions, we present three sufficient feasibility conditions under which feasible inputs over the convex hull can be obtained per coordinate, with a common input, or via convex blending. We also describe the associated computational methods, based on interval intersections or an offline linear program (LP). Beyond certifying compatibility, we give conditions under which the quadratic-program (QP) safety filter is affine in the state. This enables explicit implementations via convex combinations of vertex-feasible inputs. Case studies illustrate the results.
{"title":"From Vertices to Convex Hulls: Certifying Set-Wise Compatibility for CBF Constraints","authors":"Shima Sadat Mousavi;Xiao Tan;Aaron D. Ames","doi":"10.1109/LCSYS.2025.3648436","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3648436","url":null,"abstract":"This letter develops certificates that propagate compatibility of multiple control barrier function (CBF) constraints from sampled vertices to their convex hull. Under mild concavity and affinity assumptions, we present three sufficient feasibility conditions under which feasible inputs over the convex hull can be obtained per coordinate, with a common input, or via convex blending. We also describe the associated computational methods, based on interval intersections or an offline linear program (LP). Beyond certifying compatibility, we give conditions under which the quadratic-program (QP) safety filter is affine in the state. This enables explicit implementations via convex combinations of vertex-feasible inputs. Case studies illustrate the results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3011-3016"},"PeriodicalIF":2.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-25DOI: 10.1109/LCSYS.2025.3648635
Karthik Elamvazhuthi;Sachin Shivakumar
Estimating the reachable set of a dynamical system is a fundamental problem in control theory, particularly when control inputs are bounded. Direct simulation using randomly sampled admissible controls often leads to trajectories that cluster near attractors, resulting in poor coverage of the reachable set. To achieve a more uniform distribution of terminal states, we formulate the problem within an Optimal Transport (OT) framework. In this setting, the goal is to steer the system so that the final state distribution, determined by the chosen controls and initial conditions, matches a desired target distribution. Enforcing this condition exactly is not possible since the reachable set is not known. So we introduce an ${mathrm { L}}_{2}$ -norm based regularization of the terminal distribution that relaxes the constraint while promoting uniform coverage. The resulting formulation can be approximated by a finite-dimensional, particle-based optimal control problem with kernel-coupled terminal cost. We show that this approach converges to the original formulation and demonstrate through a 2D and 6D numerical example that it provides significantly more uniform reachable-set sampling than random control strategies.
{"title":"Uniform Sampling From the Reachable Set Using Optimal Transport","authors":"Karthik Elamvazhuthi;Sachin Shivakumar","doi":"10.1109/LCSYS.2025.3648635","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3648635","url":null,"abstract":"Estimating the reachable set of a dynamical system is a fundamental problem in control theory, particularly when control inputs are bounded. Direct simulation using randomly sampled admissible controls often leads to trajectories that cluster near attractors, resulting in poor coverage of the reachable set. To achieve a more uniform distribution of terminal states, we formulate the problem within an Optimal Transport (OT) framework. In this setting, the goal is to steer the system so that the final state distribution, determined by the chosen controls and initial conditions, matches a desired target distribution. Enforcing this condition exactly is not possible since the reachable set is not known. So we introduce an <inline-formula> <tex-math>${mathrm { L}}_{2}$ </tex-math></inline-formula>-norm based regularization of the terminal distribution that relaxes the constraint while promoting uniform coverage. The resulting formulation can be approximated by a finite-dimensional, particle-based optimal control problem with kernel-coupled terminal cost. We show that this approach converges to the original formulation and demonstrate through a 2D and 6D numerical example that it provides significantly more uniform reachable-set sampling than random control strategies.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3065-3070"},"PeriodicalIF":2.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1109/LCSYS.2025.3647842
Liang Wu;Yunhong Che;Richard D. Braatz;Jan Drgona
Minimizing both the worst-case and average execution times of optimization algorithms is equally critical in real-time optimization-based control applications such as model predictive control (MPC). Most MPC solvers have to trade off between certified worst-case and practical average execution times. For example, our previous work (Wu and Braatz 2025) proposed a full-Newton path-following interior-point method (IPM) with data-independent, simple-calculated, and exact $O(sqrt {n})$ iteration complexity, but not as efficient as the heuristic Mehrotra’s predictor–corrector IPM algorithm (which sacrifices global convergence). This letter proposes a new predictor–corrector IPM algorithm that preserves the same certified $O$ ($sqrt {n}$ ) iteration complexity while achieving a $5times $ speedup over (Wu and Braatz 2025). Numerical experiments and codes that validate these results are provided.
在模型预测控制（MPC）等基于实时优化的控制应用中，最小化优化算法的最坏情况和平均执行时间同样至关重要。大多数MPC求解器必须在认证的最坏情况和实际的平均执行时间之间进行权衡。例如，我们之前的工作（Wu和Braatz 2025）提出了一种全牛顿路径跟踪内点法（IPM），具有数据独立，计算简单，精确的$O(sqrt {n})$迭代复杂度，但不如启发式Mehrotra的预测校正IPM算法（牺牲全局收敛性）高效。这封信提出了一种新的预测校正IPM算法，该算法保留了相同的认证$O$ （$sqrt {n}$）迭代复杂度，同时实现了$5times $的加速（Wu and Braatz 2025）。数值实验和代码验证了这些结果。
{"title":"A Time-Certified Predictor-Corrector IPM Algorithm for Box-QP","authors":"Liang Wu;Yunhong Che;Richard D. Braatz;Jan Drgona","doi":"10.1109/LCSYS.2025.3647842","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3647842","url":null,"abstract":"Minimizing both the worst-case and average execution times of optimization algorithms is equally critical in real-time optimization-based control applications such as model predictive control (MPC). Most MPC solvers have to trade off between certified worst-case and practical average execution times. For example, our previous work (Wu and Braatz 2025) proposed a full-Newton path-following interior-point method (IPM) with data-independent, simple-calculated, and exact <inline-formula> <tex-math>$O(sqrt {n})$ </tex-math></inline-formula> iteration complexity, but not as efficient as the heuristic Mehrotra’s predictor–corrector IPM algorithm (which sacrifices global convergence). This letter proposes a new predictor–corrector IPM algorithm that preserves the same certified <inline-formula> <tex-math>$O$ </tex-math></inline-formula>(<inline-formula> <tex-math>$sqrt {n}$ </tex-math></inline-formula>) iteration complexity while achieving a <inline-formula> <tex-math>$5times $ </tex-math></inline-formula> speedup over (Wu and Braatz 2025). Numerical experiments and codes that validate these results are provided.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3059-3064"},"PeriodicalIF":2.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1109/LCSYS.2025.3648321
Nahid Binandeh Dehaghani;Rafal Wisniewski;A. Pedro Aguiar
We propose a quantum-assisted framework for solving constrained finite-horizon nonlinear optimal control problems using a barrier Sequential Quadratic Programming (SQP) approach. A quantum subroutine is incorporated to efficiently solve the Schur complement step using block-encoding and Quantum Singular Value Transformation techniques. We formally analyze the time complexity and convergence behavior under the cumulative effect of quantum errors, establishing local input-to-state stability and convergence to a neighborhood of the stationary point, with explicit error bounds in terms of the barrier parameter and quantum solver accuracy. The proposed framework enables computational complexity to scale polylogarithmically with the system dimension demonstrating the potential of quantum algorithms to enhance classical optimization routines in nonlinear control applications.
{"title":"Quantum-Assisted Barrier Sequential Quadratic Programming for Nonlinear Optimal Control","authors":"Nahid Binandeh Dehaghani;Rafal Wisniewski;A. Pedro Aguiar","doi":"10.1109/LCSYS.2025.3648321","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3648321","url":null,"abstract":"We propose a quantum-assisted framework for solving constrained finite-horizon nonlinear optimal control problems using a barrier Sequential Quadratic Programming (SQP) approach. A quantum subroutine is incorporated to efficiently solve the Schur complement step using block-encoding and Quantum Singular Value Transformation techniques. We formally analyze the time complexity and convergence behavior under the cumulative effect of quantum errors, establishing local input-to-state stability and convergence to a neighborhood of the stationary point, with explicit error bounds in terms of the barrier parameter and quantum solver accuracy. The proposed framework enables computational complexity to scale polylogarithmically with the system dimension demonstrating the potential of quantum algorithms to enhance classical optimization routines in nonlinear control applications.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3005-3010"},"PeriodicalIF":2.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1109/LCSYS.2025.3647987
Yu Yang;Andreas Oliveira;Louis L. Whitcomb;Felipe Pait;Mario Sznaier;Noah J. Cowan
The weakly electric fish Eigenmannia virescens naturally swims back and forth to stay within a moving refuge, tracking its motion using visual and electrosensory feedback. Previous experiments show that when the refuge oscillates as a low-frequency sinusoid (below about 0.5 Hz), the tracking is nearly perfect, but phase lag increases and gain decreases at higher frequencies. Here, we model this nonlinear behavior as an adaptive internal model principle (IMP) system. Specifically, an adaptive state estimator identifies the a priori unknown frequency, and feeds this parameter estimate into a closed-loop IMP-based system built around a lightly damped harmonic oscillator. We prove that the closed-loop tracking error of the IMP-based system, where the online adaptive frequency estimate is used as a surrogate for the unknown frequency, converges exponentially to that of an ideal control system with perfect information about the stimulus. Simulations further show that our model reproduces the fish refuge tracking Bode plot across a wide frequency range. These results establish the theoretical validity of combining the IMP with an adaptive identification process and provide a basic framework in adaptive sensorimotor control.
{"title":"Modeling Adaptive Tracking of Predictable Stimuli in Electric Fish","authors":"Yu Yang;Andreas Oliveira;Louis L. Whitcomb;Felipe Pait;Mario Sznaier;Noah J. Cowan","doi":"10.1109/LCSYS.2025.3647987","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3647987","url":null,"abstract":"The weakly electric fish Eigenmannia virescens naturally swims back and forth to stay within a moving refuge, tracking its motion using visual and electrosensory feedback. Previous experiments show that when the refuge oscillates as a low-frequency sinusoid (below about 0.5 Hz), the tracking is nearly perfect, but phase lag increases and gain decreases at higher frequencies. Here, we model this nonlinear behavior as an adaptive internal model principle (IMP) system. Specifically, an adaptive state estimator identifies the a priori unknown frequency, and feeds this parameter estimate into a closed-loop IMP-based system built around a lightly damped harmonic oscillator. We prove that the closed-loop tracking error of the IMP-based system, where the online adaptive frequency estimate is used as a surrogate for the unknown frequency, converges exponentially to that of an ideal control system with perfect information about the stimulus. Simulations further show that our model reproduces the fish refuge tracking Bode plot across a wide frequency range. These results establish the theoretical validity of combining the IMP with an adaptive identification process and provide a basic framework in adaptive sensorimotor control.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3077-3082"},"PeriodicalIF":2.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1109/LCSYS.2025.3647981
Margarita A. Guerrero;Braghadeesh Lakshminarayanan;Cristian R. Rojas
Model predictive control is a well established control technology for trajectory tracking. Its use requires the availability of an accurate model of the plant, but obtaining such a model is often time consuming and costly. Data-Enabled Predictive Control (DeePC) addresses this shortcoming in the linear time-invariant setting, by skipping the model building step and instead relying directly on input-output data. Unfortunately, many real systems are nonlinear and exhibit strong operating-point dependence. Building on classical linear parameter-varying control, we introduce DeePC-GS, a gain-scheduled extension of DeePC for unknown, regime-varying systems. The key idea is to allow DeePC to switch between different local Hankel matrices–selected online via a measurable scheduling variable–thereby uniting classical gain scheduling tools with identification-free, data-driven MPC. We test the effectiveness of our DeePC-GS formulation on a nonlinear ship-steering benchmark, demonstrating that it outperforms state-of-the-art data-driven MPC while maintaining tractable computation.
{"title":"Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems","authors":"Margarita A. Guerrero;Braghadeesh Lakshminarayanan;Cristian R. Rojas","doi":"10.1109/LCSYS.2025.3647981","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3647981","url":null,"abstract":"Model predictive control is a well established control technology for trajectory tracking. Its use requires the availability of an accurate model of the plant, but obtaining such a model is often time consuming and costly. Data-Enabled Predictive Control (DeePC) addresses this shortcoming in the linear time-invariant setting, by skipping the model building step and instead relying directly on input-output data. Unfortunately, many real systems are nonlinear and exhibit strong operating-point dependence. Building on classical linear parameter-varying control, we introduce DeePC-GS, a gain-scheduled extension of DeePC for unknown, regime-varying systems. The key idea is to allow DeePC to switch between different local Hankel matrices–selected online via a measurable scheduling variable–thereby uniting classical gain scheduling tools with identification-free, data-driven MPC. We test the effectiveness of our DeePC-GS formulation on a nonlinear ship-steering benchmark, demonstrating that it outperforms state-of-the-art data-driven MPC while maintaining tractable computation.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3041-3046"},"PeriodicalIF":2.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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