IFAC Journal of Systems and Control最新文献_第7页

Adaptation of fractional-order PI controller for a variable input interleaved DC–DC boost converter using particle swarm optimization with parametric variation 基于参数变化粒子群优化的分数阶PI控制器自适应变输入交错DC-DC升压变换器

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-03-18 DOI: 10.1016/j.ifacsc.2025.100301

Dola Sinha , Mou Das Mahapatra , Sucharita Pal , Saibal Majumder , Sovan Bhattacharya , Chandan Bandyopadhyay

The increasing demand for renewable energy integration has led to the development of advanced converter strategies to manage the inherent variability of renewable power sources. This paper presents a high-performance interleaved boost converter regulated by a fractional-order proportional-integral (FoPI) controller to ensure stable output voltage and power delivery under fluctuating input and load conditions. The FoPI controller parameters, including gains and fractional order, are optimized using particle swarm optimization (PSO) with the integral absolute error (IAE) as the objective function. The primary objective is to enhance the system’s robustness against input voltage variations and load disturbances. The proposed PSO-FoPI controller is tested under different operating scenarios: (i) a fixed input of 150 V, (ii) an input variation from 150 V to 350 V, and (iii) a fixed 200 V input with output power demand variations between 8 kW and 12.25 kW. Also sensitivity analysis with changing parameter values of the converter and inclusion of step and ramp input disturbances, the performance of the controller is evaluated. MATLAB/Simulink simulations demonstrate that the PSO-FoPI controller effectively maintains the desired 400 V output and an average power of 10 kW while reducing transient effects and harmonic distortions. Comparative analysis with PI controller, tuned via Ziegler–Nichols and PSO techniques, highlights the superior performance of the proposed approach. The results confirm that the PSO-FoPI-controlled interleaved boost converter enhances stability and efficiency, making it well-suited for real-time applications utilizing renewable power sources.

随着可再生能源集成需求的不断增长，人们开始开发先进的转换器策略，以管理可再生能源固有的可变性。本文介绍了一种由分数阶比例积分（FoPI）控制器调节的高性能交错升压转换器，以确保在波动的输入和负载条件下提供稳定的输出电压和功率。以积分绝对误差（IAE）为目标函数，采用粒子群优化（PSO）对 FoPI 控制器参数（包括增益和分数阶数）进行了优化。主要目的是增强系统对输入电压变化和负载干扰的鲁棒性。所提出的 PSO-FoPI 控制器在不同的运行情况下进行了测试：(i) 输入电压固定为 150 V，(ii) 输入电压从 150 V 变为 350 V，(iii) 输入电压固定为 200 V，输出功率需求变化在 8 kW 和 12.25 kW 之间。此外，还对变流器参数值的变化以及阶跃和斜坡输入干扰的敏感性进行了分析，并对控制器的性能进行了评估。MATLAB/Simulink 仿真表明，PSO-FoPI 控制器能有效保持所需的 400 V 输出电压和 10 kW 的平均功率，同时减少瞬态效应和谐波失真。与通过 Ziegler-Nichols 和 PSO 技术调整的 PI 控制器进行的比较分析表明，所提出的方法性能优越。结果证实，PSO-FoPI 控制交错升压转换器提高了稳定性和效率，非常适合利用可再生能源的实时应用。

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引用次数: 0

Learning optimal safety certificates for unknown nonlinear control systems 学习未知非线性控制系统的最优安全证书

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-03-01 DOI: 10.1016/j.ifacsc.2025.100300

Pouria Tooranjipour, Bahare Kiumarsi

This paper introduces a novel approach for designing safe optimal controllers that avoid destructive conflicts between safety and performance in a large domain of the system’s operation. Designing computationally tractable feedback controllers that respect safety for a given set is impossible in general. The best one can do in this case is to maximize the region contained in the safe set that respects both safety and optimality. To this end, our key contribution lies in constructing a safe optimal domain of attraction (DoA) that ensures optimal convergence of the system’s trajectories to the origin without violating safety. To accomplish this, we leverage the concept of the relaxed Hamilton–Jacobi–Bellman (HJB) equation, which allows us to learn the most permissive control barrier certificates (CBCs) with a maximum-volume conflict-free set by solving a tractable optimization problem. To enhance computational efficiency, we present an innovative sum-of-squares (SOS)-based algorithm, breaking down the optimization problem into smaller SOS programs at each iteration. To alleviate the need for the system model to solve these SOS optimizations, an SOS-based off-policy reinforcement learning (RL) method is presented. This off-policy learning approach enables the evaluation of a target policy distinct from the behavior policy used for data collection, ensuring safe exploration under mild assumptions. In the end, the simulation results are given to show the efficacy of the proposed method.

本文介绍了一种设计安全最优控制器的新方法，该方法在系统运行的大范围内避免了安全与性能之间的破坏性冲突。一般来说，设计出计算上可处理的反馈控制器来保证给定集合的安全性是不可能的。在这种情况下，我们能做的最好的事情就是最大化安全集中包含的区域，同时尊重安全性和最优性。为此，我们的关键贡献在于构建一个安全的最优吸引域（DoA），以确保系统轨迹在不违反安全性的情况下最优收敛到原点。为了实现这一目标，我们利用了松弛Hamilton-Jacobi-Bellman （HJB）方程的概念，该方程允许我们通过解决一个可处理的优化问题来学习具有最大容量无冲突集的最宽松控制屏障证书（CBCs）。为了提高计算效率，我们提出了一种创新的基于平方和（SOS）的算法，在每次迭代中将优化问题分解为更小的SOS程序。为了减轻系统模型解决这些SOS优化问题的需要，提出了一种基于SOS的非策略强化学习（RL）方法。这种非策略学习方法使目标策略的评估不同于用于数据收集的行为策略，确保在温和假设下的安全探索。最后给出了仿真结果，验证了该方法的有效性。

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引用次数: 0

A sparse approach to transfer function estimation via Least Absolute Shrinkage and Selection Operator 基于最小绝对收缩和选择算子的传递函数稀疏估计方法

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-02-18 DOI: 10.1016/j.ifacsc.2025.100299

S.K. Laha

Estimating transfer functions from sampled input–output data is a critical task in system identification. Traditional approaches, such as least square optimization, often result in dense parameter estimates, which can be less interpretable and computationally intensive. This paper introduces a novel method for transfer function estimation by applying the Least Absolute Shrinkage and Selection Operator (LASSO), which promotes sparsity in the identified coefficients. The proposed approach enables sparse identification of both the numerator and denominator coefficients of the transfer function. The efficacy of the method is demonstrated through numerical experiments and application to the estimation of the natural frequencies of a turbine blade from its impulse response. By leveraging LASSO, we achieve a more parsimonious and interpretable model that captures the essential dynamics of the system. The results highlight the advantages of sparse modelling in system identification and its potential for improving the understanding and prediction of complex mechanical systems.

从采样的输入输出数据中估计传递函数是系统辨识中的一项关键任务。传统的方法，如最小二乘优化，通常会导致密集的参数估计，这可能不太可解释性和计算密集。本文提出了一种利用最小绝对收缩和选择算子（LASSO）来估计传递函数的新方法，该方法提高了识别系数的稀疏性。提出的方法可以稀疏识别传递函数的分子和分母系数。数值实验结果表明了该方法的有效性，并将其应用于涡轮叶片脉冲响应的固有频率估计。通过利用LASSO，我们实现了一个更简洁和可解释的模型，该模型捕获了系统的基本动态。结果突出了稀疏建模在系统识别中的优势，以及它在提高对复杂机械系统的理解和预测方面的潜力。

引用次数: 0

Local vs regional neural air pollution forecasting models 局部与区域神经空气污染预测模型

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-02-13 DOI: 10.1016/j.ifacsc.2025.100298

Matteo Sangiorgio, Giorgio Guariso

Selecting a suitable dataset to develop a data-based forecasting model is often problematic. This is particularly important in the case of air pollution, where concentration measures are scattered over large areas. On the one hand, the classical approach creates a single-station (local) forecasting model using only the data collected at the same station. This guarantees a training dataset that considers all the site’s specific characteristics. On the other hand, these data may be limited and not sufficient to develop a robust predictor. Thus, one may use data from other stations to complement the dataset or develop a unique model considering all the data available within a region/domain. While this approach may be prone to filtering high variations, it may consider information on peculiar episodes that have not occurred in the past to a specific station. This paper discusses the topic of air pollution forecasting using the example of several stations in the Padana Plain, Northern Italy. Local forecasting models are developed using LSTM neural networks for nitrogen dioxide and ozone and hourly data from 2010 to 2023 and then compared with regional models. All these models perform extremely well under various regression-based and classification-based performance indicators, except for a few sites with peculiar characteristics that can be considered at the border of the information domain.

选择合适的数据集来开发基于数据的预测模型通常是有问题的。在空气污染的情况下，这一点尤其重要，因为空气污染的浓度措施分散在大片地区。一方面，经典方法仅使用同一站点收集的数据创建单站点（局部）预测模型。这保证了训练数据集考虑了所有站点的特定特征。另一方面，这些数据可能是有限的，不足以开发一个稳健的预测器。因此，可以使用其他站点的数据来补充数据集，或者考虑到一个区域/领域内所有可用的数据，开发一个独特的模型。虽然这种方法可能倾向于过滤高变化，但它可能会考虑特定站点过去未发生的特殊事件的信息。本文以意大利北部帕达纳平原的几个气象站为例，讨论了空气污染的预报问题。利用LSTM神经网络建立了2010 ~ 2023年二氧化氮和臭氧逐时预报模型，并与区域预报模型进行了比较。所有这些模型在各种基于回归和基于分类的性能指标下都表现得非常好，除了一些具有特殊特征的站点，这些站点可以在信息域的边界上考虑。

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引用次数: 0

A top-down approach for climate change mitigation strategies 一种自上而下的减缓气候变化战略方法

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-02-03 DOI: 10.1016/j.ifacsc.2025.100297

Claudio Marchesi, Michele Francesco Arrighini, Laura Zecchi, Marialuisa Volta

This research examined the effects of various GHG reduction policies on climate change via optimization techniques using a top-down approach. The aim was to examine how different aspects of policies to reduce CO₂ and CH₄ emissions would affect changes in temperature compared to pre-industrial levels from 2025 to 2100. The proposed top-down approach allows for the investigation of several factors that may influence the results: (i) the objective function, (ii) the reduction pathway, and (iii) the starting point of the optimization. Two different objective functions were minimized: the overall sum of the temperature between 2025–2100 and the value at 2100. The results were also compared in terms of the reduction trajectories: two different emission trends were assumed: a gradual (gaussian) fall in emissions or a fast (exponential) decline, starting in 2025, in 2030, and in 2035. The mitigation of greenhouse gas (GHG) emissions was limited to a certain range of scenarios outlined by the Intergovernmental Panel on Climate Change (IPCC). These scenarios were determined by analyzing economic, social, and technical developments expected to occur in the next few decades. The analysis also included the interaction in global warming of air pollutant emission variations due to climate policies. The results revealed that exponential trajectories, depending on the initial year, can facilitate the stabilization of global temperatures below 1.5 °C. In contrast, gaussian trajectories were more likely to overtake this threshold if implementation is delayed beyond 2025.

本研究采用自上而下的方法，通过优化技术考察了各种温室气体减排政策对气候变化的影响。其目的是研究减少二氧化碳和甲烷排放的政策的不同方面将如何影响2025年至2100年期间与工业化前水平相比的温度变化。提出的自上而下的方法允许对可能影响结果的几个因素进行调查：(i)目标函数，（ii）减少途径，以及（iii）优化的起点。两个不同的目标函数被最小化：2025-2100年之间的温度总和和2100年的值。结果还在减少轨迹方面进行了比较：假设了两种不同的排放趋势：从2025年、2030年和2035年开始，排放量逐渐（高斯）下降或快速（指数）下降。温室气体（GHG）排放的减缓仅限于政府间气候变化专门委员会（IPCC）概述的一定范围的情景。这些情景是通过分析未来几十年预计发生的经济、社会和技术发展来确定的。该分析还包括了气候政策导致的大气污染物排放变化在全球变暖中的相互作用。结果表明，依赖于初始年份的指数轨迹可以促进全球温度稳定在1.5°C以下。相比之下，如果实施推迟到2025年之后，高斯轨迹更有可能超过这个阈值。

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引用次数: 0

A bilevel optimization approach for Balancing Markets with electric vehicle aggregators and smart charging 电动汽车集成商与智能充电市场平衡的双层优化方法

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-01-30 DOI: 10.1016/j.ifacsc.2025.100296

Daniel Fernández Valderrama, Giulio Ferro, Luca Parodi, Michela Robba

Demand Response (DR) programs can help alleviate the management of the electrical distribution grid by reducing loads in specified areas. They can be enabled within the energy Balancing Market (BM). Aggregators can manage different customers providing flexibility. Recently, Electric Vehicles Aggregators (EVAs) have emerged as significant players in the BM because they can manage fleets of electric vehicles (EVs) in the distribution grid. This paper addresses a multi-objective optimization problem for a distribution power grid that includes EVs and smart charging parks. At the higher level, the Distribution System Operator (DSO) considers the characteristics of each BM actor to minimize costs. Meanwhile, EVAs focus on controlling EV charging at the lower level to maximize their profit. The optimization problems of EVAs and other actors are replaced by KKT (Karush–Kuhn–Tucker) conditions, which are embedded as constraints in the DSO decision problem. Moreover, the resulting bilinear terms (in the optimization problem constraints) are linearized to fasten the finding of an optimal solution. The overall optimization problem is a mixed-integer quadratic programming (MIQP) and has been applied to the IEEE 13-bus test benchmark. The results demonstrate a reduction of about 6% of power loss in the grid achieved by the developed model. Besides, the linearized model can afford a more discretized model due to the reduction of computational effort.

需求响应（DR）计划可以通过减少特定区域的负荷来帮助减轻配电网的管理。它们可以在能量平衡市场（BM）中启用。聚合器可以管理不同的客户，从而提供灵活性。最近，电动汽车聚合器（EVAs）已经成为BM中的重要参与者，因为它们可以管理配电网中的电动汽车（ev）车队。本文研究了包含电动汽车和智能充电公园的配电网的多目标优化问题。在更高的层次上，分配系统操作员（DSO）考虑每个BM参与者的特征以最小化成本。与此同时，电动汽车厂商则致力于将电动汽车充电控制在较低水平，以实现利润最大化。EVAs和其他参与者的优化问题被KKT （Karush-Kuhn-Tucker）条件取代，KKT （Karush-Kuhn-Tucker）条件作为约束嵌入到DSO决策问题中。此外，得到的双线性项（在优化问题约束中）被线性化，以加快寻找最优解。整体优化问题是一个混合整数二次规划（MIQP），并已应用于IEEE 13总线测试基准。结果表明，所开发的模型使电网的功率损耗减少了约6%。此外，由于减少了计算量，线性化模型可以提供更加离散化的模型。

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引用次数: 0

New approach of series-PID controller design based on modern control theory: Simulations and real-time validation 基于现代控制理论的串联pid控制器设计新方法：仿真与实时验证

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-01-25 DOI: 10.1016/j.ifacsc.2025.100295

Vivek Kumar, Yogesh V. Hote

This paper proposed a novel approach to designing the series-proportional–integral derivative (series-PID) controller, which provides desired performance specifications. In this approach, the design of the series-PID controller is carried out by modern control theory, which is based on the Butterworth pattern of pole placement, and classical control theory, which is based on Krishnamurthi’s corollary on the Routh criterion. The uniqueness of the proposed approach in comparison with existing methods is that it comprises both classical and modern control theory for improving performance and robustness trade-off. The validation of the proposed control theory is carried out using numerical examples (Linear & Non-linear models). The results show that the performance is improved compared to the existing results. The main aim of the paper is that the proposed theory should be industrial-friendly. In view of this, the proposed theory is validated on the D.C. servo motor and power system problem of load frequency control. For these practical problems, comparisons are carried out with well-known control approaches, such as the internal model control approach proposed by various authors. Finally, the proposed approach has been implemented and validated on the hardware setup of the DC–DC buck converter (

D D B C_{c}

). In numerical examples and practical problems, the efficacy of the proposed approach has been checked by robustness analysis and fragility analysis. Further, it has also been checked by determining various performance indices such as Integral Square Error (ISE), Integral Absolute Error (IAE), and Total Variations (TV).

本文提出了一种新颖的串联比例积分微分（串联pid）控制器的设计方法，该方法能提供理想的性能指标。在该方法中，串联pid控制器的设计采用了基于Butterworth极点布置模式的现代控制理论和基于Krishnamurthi关于Routh准则的推论的经典控制理论。与现有方法相比，该方法的独特之处在于它结合了经典和现代控制理论，以提高性能和鲁棒性权衡。通过数值算例对所提出的控制理论进行了验证(Linear &；非线性模型)。结果表明，与现有的结果相比，该方法的性能得到了提高。本文的主要目的是提出的理论应该是工业友好的。针对直流伺服电机及电力系统的负载频率控制问题，对所提出的理论进行了验证。针对这些实际问题，与知名的控制方法进行了比较，如不同作者提出的内模控制方法。最后，该方法在DC-DC降压变换器（DDBCc）的硬件设置上进行了实现和验证。通过数值算例和实际问题，通过鲁棒性分析和脆弱性分析验证了该方法的有效性。此外，还通过确定各种性能指标，如积分平方误差（ISE），积分绝对误差（IAE）和总变化（TV）来检查它。

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引用次数: 0

A Control-Equivalent-Turbulence-Input estimation method for unmanned helicopters 无人直升机控制等效湍流输入估计方法

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-01-04 DOI: 10.1016/j.ifacsc.2024.100293

Sergey Nazarov, Per-Olof Gutman

This paper describes a new time-domain estimation method of the Control Equivalent Turbulence Input (CETI). CETI simulates the effects of natural atmospheric turbulence on rotorcraft dynamics by additional control inputs. The identified linear model of helicopter dynamics is changed into a quasi-nonlinear model by adding trim data and nonlinear kinematic equations. An unscented Kalman filter is designed using the quasi-nonlinear helicopter model and scaled unscented transformation to estimate CETI in real-time. The method was first tested in simulations; then, results were obtained for flight. The proposed method is also compared with other methods used in practice.

本文提出了一种新的控制等效湍流输入（CETI）时域估计方法。CETI通过附加控制输入模拟自然大气湍流对旋翼机动力学的影响。通过加入纵倾数据和非线性运动学方程，将识别出的直升机动力学线性模型转化为拟非线性模型。利用拟非线性直升机模型和比例unscented变换设计了无气味卡尔曼滤波器，实时估计CETI。该方法首先在模拟中进行了测试；然后，得到了飞行的结果。并与实际应用中的其他方法进行了比较。

引用次数: 0

CHoKI-based MPC for blood glucose regulation in Artificial Pancreas 基于choki的MPC在人工胰腺血糖调节中的应用

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacsc.2024.100294

Beatrice Sonzogni , José María Manzano , Marco Polver , Fabio Previdi , Antonio Ferramosca

This work presents a Model Predictive Control (MPC) for the artificial pancreas, which is able to autonomously manage basal insulin injections in type 1 diabetic patients. Specifically, the MPC goal is to maintain the patients’ blood glucose level inside the safe range of 70-180 mg/dL, acting on the insulin amount and respecting all the imposed constraints, taking into consideration also the Insulin On Board (IOB), to avoid excess of insulin infusion. MPC uses a model to make predictions of the system behavior. In this work, due to the complexity of the diabetes disease that complicates the identification of a general physiological model, a data-driven learning method is employed instead. The Componentwise Hölder Kinky Inference (CHoKI) method is adopted, to have a customized controller for each patient. For the data collection phase and also to test the proposed controller, the virtual patients of the FDA-accepted UVA/Padova simulator are exploited. The MPC is also tested on simulations with variability of the insulin sensitivity and with physical activity sessions. The final results are satisfying since the proposed controller is conservative and reduces the time in hypoglycemia (which is more dangerous) if compared to the outcomes obtained without the IOB constraints.

这项工作提出了一个模型预测控制（MPC）的人工胰腺，它能够自主管理基础胰岛素注射1型糖尿病患者。具体而言，MPC的目标是将患者的血糖水平维持在70-180 mg/dL的安全范围内，对胰岛素量起作用，尊重所有规定的限制，同时考虑到机上胰岛素（insulin on Board， IOB），避免胰岛素输注过量。MPC使用一个模型来预测系统的行为。在这项工作中，由于糖尿病疾病的复杂性使一般生理模型的识别变得复杂，因此采用了数据驱动的学习方法。采用Componentwise Hölder Kinky Inference （CHoKI）方法，为每个患者定制一个控制器。在数据收集阶段，为了测试所提出的控制器，利用了fda认可的UVA/Padova模拟器的虚拟患者。MPC还在模拟胰岛素敏感性变化和体育锻炼中进行了测试。最终的结果是令人满意的，因为与没有IOB约束的结果相比，所提出的控制器是保守的，并且减少了低血糖（更危险）的时间。

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引用次数: 0

Adaptive chaos control: A novel continuous-time approach for enhanced stability 自适应混沌控制：一种新的增强稳定性的连续时间方法

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2024-12-10 DOI: 10.1016/j.ifacsc.2024.100292

Muhammad Shafiq , Israr Ahmad

Stabilizing chaotic systems with robustness, speed, and smoothness remains a significant challenge due to issues like chattering and slow convergence associated with traditional control methods. This paper proposes a novel continuous-time adaptive robust control (CTARC) scheme to overcome these limitations and enhance the stabilization of uncertain chaotic systems. CTARC employs smooth control functions; specifically hyperbolic secant and inverse hyperbolic sine functions to eliminate chattering and achieve faster, more precise convergence to equilibrium. Unlike conventional controllers that simplify system dynamics by removing nonlinearities, this approach preserves them, thereby improving robustness against time-varying disturbances and model uncertainties. A Lyapunov-based stability analysis rigorously establishes the asymptotic stability of the proposed control strategy. Numerical simulations on the Shimizu–Morioka chaotic system and a memristor-based hyperchaotic system validate CTARC’s superiority in convergence speed, energy efficiency, and stability compared to existing adaptive methods. By reducing transient effects like overshoots and oscillations, the proposed scheme ensures smoother transitions and minimizes energy consumption, addressing critical limitations of traditional methods. These results highlight CTARC’s potential as a robust and energy-efficient solution for chaos stabilization and provide a foundation for future developments in complex system control.

由于传统控制方法存在抖振和缓慢收敛等问题，稳定具有鲁棒性、速度和平滑性的混沌系统仍然是一个重大挑战。本文提出了一种新的连续时间自适应鲁棒控制（CTARC）方案来克服这些限制，提高不确定混沌系统的稳定性。CTARC采用平滑控制功能；特别是双曲正割和反双曲正弦函数，以消除抖振，实现更快，更精确的收敛到平衡。与通过消除非线性来简化系统动力学的传统控制器不同，这种方法保留了它们，从而提高了对时变干扰和模型不确定性的鲁棒性。基于lyapunov的稳定性分析严密地建立了所提控制策略的渐近稳定性。对Shimizu-Morioka混沌系统和基于忆阻器的超混沌系统的数值仿真验证了CTARC与现有自适应方法相比在收敛速度、能量效率和稳定性方面的优势。通过减少过调和振荡等瞬态效应，该方案确保了更平稳的过渡，并最大限度地减少了能耗，解决了传统方法的关键局限性。这些结果突出了CTARC作为一种鲁棒且节能的混沌稳定解决方案的潜力，并为复杂系统控制的未来发展奠定了基础。

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引用次数: 0