Asian Journal of Control最新文献

This study investigates a novel class of variational programming problems characterized by fractional interval values, formulated under the Caputo–Fabrizio fractional derivative with an exponential kernel. Invex and generalized invex functions are used to discuss the Mond–Weir-type dual problem for the considered variational problem. The pertinent duality theorems are developed in this study to connect the primal and its dual problems, namely the weak, strong, and converse duality theorems for a Mond–Weir-type dual problem. Several numerical examples are provided to demonstrate the applicability of the theoretical results. This work contributes to the expanding application of fractional calculus and interval optimization in addressing complex, real-world problems in science and engineering.

This study focuses on the development of a new fractional-order $��{�L�}_{�1�}�$ adaptive feedback control strategy of dynamical systems. Specifically, the original $��{�L�}_{�1�}�$ adaptive controller is revisited based on the consideration of fractional-order filters using the Oustaloup recursive approximation. To show the effectiveness and superiority of the proposed fractional-order filter, a comparative study is conducted against classical integer-order filters based on both time analysis using performance metrics such as the integral of absolute error (IAE) and energy consumption, and frequency analysis, including Nyquist criterion and stability margins. The proposed control scheme is validated, on an electro-hydraulic system (EHS), through several numerical simulation scenarios to show enhanced performance and robustness. Results indicate up to 86% improvement in tracking performance compared to the original controller.

To address the problem of multi-axis synchronous motion control, this paper establishes a dynamic model of a four-axis ball screw press platform using the LuGre friction model, which effectively provides simulation verification for the controller. Based on this model, a composite controller combining fractional-order operators and sliding mode control is proposed using multi-agent cooperative control theory to solve the nonlinearity and synchronization issues of the system. This design improves stability and robustness while optimizing dynamic performance indicators such as response speed, overshoot, and settling time. The stability of the controller is proven using graph theory and the Lyapunov stability theorem. Simulation results demonstrate that the proposed method ensures multi-axis synchronization consistency, exhibits strong robustness against disturbances, and is effective in tracking accuracy and dynamic response.

This research explores the approximate controllability of a neutral fractional differential equations incorporating delay effects via almost sectorial operators. A set of sufficient conditions is established to achieve the desired results using semigroup theory associated with almost sectorial operators, Schauder's fixed-point theorem, and fractional calculus. Then, we provide some novel conditions for the system and assume that the corresponding linear system is approximately controllable. Finally, an example is provided to illustrate the main results.

This study presents a non-iterative tuning technique for a linear fractional-order (FO) controller, based on the integral of the time-weighted absolute error (ITAE) criterion. Minimizing the ITAE is a traditional approach for tuning FO controllers. This technique reduces the over/undershoot and suppresses the steady-state error. In contrast to conventional approaches of ITAE-based controller tuning, the proposed approach does not require multiple closed-loop experiments or model-based simulations to evaluate the ITAE. The one-shot input/output data is collected from the controlled plant. A fictitious reference signal is defined on the basis of the collected input and output signal, which enables us to evaluate the closed-loop response provided by the arbitrary controller parameters. To avoid repeated experiments that are necessary in the conventional approach, we reformulate the ITAE minimization problem using the fictitious reference signal. The desired FO controller parameters minimizing the ITAE are obtained by solving the optimization problem that is based on the fictitious reference signal. The validity of the proposed approach is demonstrated by a numerical study. The avoidance of repeated experiments significantly reduces the development cost of linear FO controllers, thereby facilitating their practical application.

This study presents a new hybrid fractional two-degree-of-freedom (2DOF) proportional–integral–derivative (PID) controller designed for nonlinear multiple-input multiple-output (MIMO) systems. This innovative control strategy merges fractional calculus with a 2DOF PID controller, resulting in a hybrid approach that improves transient response, tracking accuracy, and disturbance rejection, while keeping low control effort. A notable feature of this approach is the use of complex basis functions, offering a robust and adaptable framework for optimizing controller performance across multiple interacting subsystems. Furthermore, the suitability of the proposed controller for nonlinear MIMO processes increases its applicability in real-world industrial settings. The findings of this research demonstrate the effectiveness of the proposed method in tackling challenges related to coupled nonlinear MIMO systems.

Rehabilitation after neurological or musculoskeletal impairments requires accurate detection and prediction of gait phases to support motor recovery and ensure safe, adaptive assistance. However, traditional assessment techniques often depend on subjective clinical evaluations, lacking the precision and responsiveness needed for real-time robotic interventions. To overcome these limitations, we propose a hybrid intelligent rehabilitation system that combines machine learning (ML) models with fractional order proportional-integral-derivative (FOPID) controllers for enhanced gait phase classification and trajectory correction. The proposed methodology integrates electromyography (EMG) signals, joint angle kinematics (hip and knee), and foot pressure data of 1000 samples to train and evaluate three machine learning models: random forest (RF), support vector machine (SVM), and long short-term memory (LSTM). Feature extraction techniques are applied to capture time-domain and frequency-domain characteristics of the EMG and biomechanical signals. The initial ML-based classification identifies eight distinct gait phases. Subsequently, a FOPID controller is employed to correct misclassifications and refine the trajectory of lower-limb articulations in real time. Experimental evaluations were conducted using a dataset collected from healthy subjects performing continuous walking trials. Results show that the RF-FOPID hybrid model achieves the best classification performance, with an accuracy of 82% across all gait phases. In terms of trajectory tracking, the integration of the FOPID controller leads to significant reductions in mean absolute error (MAE), with improvements of 39.12% at the hip joint and 40.21% at the knee. In an other hand, the LSTM-FOPID model showed the highest improvement in error correction, with 50% to 60% reduction in tracking deviations compared with the uncorrected baseline. These findings highlight the effectiveness of combining fractional-order control with machine learning to improve the precision, robustness, and adaptability of rehabilitation robots. This hybrid approach offers promising applications in personalized gait rehabilitation, allowing for real-time correction and adaptive assistance tailored to the patient's evolving motor capabilities.

Addressing financial risk models is essential to maintaining the sustainability and stability of economic setups in a world economy. The advantage of using fractional discrete-time difference equations to model financial risk management and economic processes is their capacity to detect random fluctuations in dynamic behaviors. This novel research introduces and analyzes the complex dynamics of a discrete-time financial risk model with fractional orders. The fundamental dynamics of this proposed model are examined, including the bifurcation analysis, chaotic attractor, and Lyapunov exponents. Furthermore, sequence complexity variations when fractional order and parameter of bifurcation risk $��\mu �$ varies are observed using the approximate entropy (ApEn) and 0-1 test. The findings highlight the sensitivity of the discrete-time financial risk model to fractional derivative orders, leading to the emergence of various rich, dynamic patterns such as chaos. Additionally, the suggested fractional-order discrete-time model is successfully stabilized and synchronized through the design of an active controller. Finally, MATLAB R2024b simulations was used to validate the research findings.

This paper addresses the design of an unknown input observer for fractional-order systems characterized by one-sided Lipschitz nonlinearities. The proposed observer ensures robust state estimation in the presence of unknown disturbances and time-varying inputs. By leveraging tempered fractional calculus and Lyapunov stability theory, sufficient conditions for $��\varsigma �$-Mittag-Leffler stability are derived and formulated as linear matrix inequalities, solvable via standard numerical tools. A simulation case study on a 3D fractional-order system demonstrates the observer's effectiveness in reconstructing unmeasured states and rejecting unknown disturbances. The work generalizes existing observer design methodologies to fractional-order dynamics, offering enhanced applicability to systems with nonlocal and memory-dependent properties.

In this paper, we study a fractional-order predator–prey system with time delay, where the dynamics are logistic with prey population commensurate to the carrying capacity. Mainly, by linearizing the system around the equilibrium point, we first analyze the stability and then prove the existence of Hopf bifurcation. Moreover, by defining the Lyapunov function for this system, the global stability of the solution is proven. The results of this study demonstrate that the stability and Hopf bifurcation of the ecological model are remarkably affected by both the time delay and the order of fractional derivatives. Finally, to support our new theoretical results, two different numerical examples are illustrated by taking two different fractional orders, $��q�$.