Advanced Control for Applications最新文献

In the context of the development of high-speed railways, the management of rail transit operation and maintenance is an important task for the daily operation of trains. At present, train scheduling is the most common fault manifestation in rail transit systems. The detection and identification of fault sources during scheduling are uncertain and subject to interference from subjective and objective factors. The present study employs statistical analysis to examine the occurrence of fault events in the train scheduling structure and operation process. The study integrates Bayesian network structure and related algorithms to calculate the occurrence and diagnosis of faults in the operation and maintenance process. A comparative analysis of the probability analysis and identification methods of fault occurrence revealed that the posterior probability of fault events at network nodes was the highest at 90.34%, which was 32.3% higher than the prior knowledge state. In comparing fault recognition methods, the recognition accuracy of the support vector machine algorithm was 91.17%, while the proposed Bayesian knowledge recognition algorithm was as high as 95.89%, with a specificity of 97.02%. Therefore, the superiority of its method in rail transit operation and maintenance has been proven.

Permanent Magnet Synchronous Motors (PMSMs) are highly efficient and versatile, widely used in electric vehicles, robotics, and industrial systems due to their high torque density, precision, and low maintenance. Research focuses on enhancing control performance, addressing dynamic response, overshoot, torque ripples, and disturbances to meet modern application demands. This study offers a reliable control approach for creating a three-phase Permanent Magnet Synchronous Motor (PMSM) speed control system. A state-feedback control rule based on the Robust Parametric Quadratic (RPQ) approach is developed using the coupled model in the (d,q) reference frame because the motor's dynamic model is nonlinear. To guarantee system stability and good dynamic performance, the model is reformed into an Affine/Polytopic state-space representation, and the control law is constructed using Linear Matrix Inequalities (LMI) approaches. The results of the simulation show that the suggested RPQ controller is better than the traditional LQ and PI controllers. The RPQ controller achieves a faster response, minimal overshoot, higher efficiency in overcoming load torque variations, reduced electromagnetic torque ripples, and improved quality of electrical signals. These findings underscore the effectiveness of the proposed controller in addressing challenges arising from parameter variations and nonlinearities in the motor model.

In order to reduce the possibility of collisions during the driving process of intelligent vehicles in the same direction, this paper studies the collision avoidance control of intelligent vehicles in the same direction and designs an active collision avoidance controller. The longitudinal safe distance model, lateral lane change path planning model, and adaptive multi-point preview model of preview distance are established. The longitudinal speed control is carried out by the expert PID control method based on mode switching, the lateral path tracking control is carried out by the sliding mode control method with exponential convergence law, and the active collision avoidance controller is designed in combination with the multi-point preview module that is adaptive to the preview distance. The active collision avoidance controller was jointly simulated using Carsim, Prescan, and Simulink software for emergency lane change scenarios and slow vehicle driving in front. In the emergency lane change scenario, the minimum distance between the two vehicles is 1.9 m, and the path tracking deviation is 0.17 m. In the front vehicle slow driving scenario, the minimum distance between the two vehicles is 2.2 m, and the path tracking deviation is 0.13 m. The controller can realize collision avoidance in two scenarios of 80 and 108 km/h respectively, which shows that the controller is robust and considers the tracking accuracy and steering stability at the same time, which is of reference significance for improving the safety of intelligent vehicles driving in the same direction.

The aim of this study is to improve industrial boiler efficiency with QFT-based controllers. The operational parameters of industrial boilers are frequently highly uncertain, which can have an impact on their performance and stability. PID controllers and other conventional control techniques have not been able to effectively manage these uncertainties. The proposed methodology involves identifying and modeling the dynamics of the industrial boiler system, accounting for parametric uncertainties like fuel flow, air flow, and pressure changes. A QFT controller is designed using frequency-domain techniques, incorporating phase and magnitude information to ensure robust stability and performance under uncertain conditions. The designed controller is validated through simulations and real-time testing to demonstrate its effectiveness in improving boiler efficiency and reducing fuel consumption. According to the simulation results, the QFT-based controller outperform conventional controllers in terms of disturbance rejection, settling times, and smoother responses. The controller is then synthesized to satisfy these bounds, ensuring that the system remains stable and performs satisfactorily under all specified uncertainties. The system increases overall system stability, decreases fuel consumption, and improves fuel efficiency by the use of QFT. The controller successfully mitigates the impact of uncertainties, ensuring that key performance indicators such as response time, overshoot, and disturbance rejection, remain within acceptable limits. The results shows that implementing QFT-based controllers significantly improves industrial boiler efficiency. By addressing the challenges of high parametric uncertainty, the QFT controller achieves robust stability, enhanced performance, and better handling of dynamic variables such as fuel flow and boiler pressure. This leads to smoother system responses, reduced overshoot, faster settling time, and ultimately contributes to reduced fuel consumption and increased overall operational efficiency.

Due to the high nonlinearity of the quadrotor dynamics and the generated structural faults, a Fault-Observer-Based Fault-Tolerant Fuzzy Terminal Sliding Mode Controller (FT2SMC) is utilized. This strategic utilization remains effective even when the system encounters input saturation. A fault observer estimates the fault vector since it is applied to the governing equations of motion as external disturbances. Additionally, a Fuzzy Inference System (FIS) has been employed to estimate the discontinuous term of the terminal sliding mode control and to alleviate the chattering phenomenon. The finite-time convergence of the tracking errors of the attitude and the altitude toward the origin has been guaranteed, and the entire closed-loop stability of the faulty quadrotor has been proved using Lyapunov theory, ensuring that it continues following the desired trajectory even in the presence of faults. The simulation results indicate the prodigious effectiveness of the controller, which navigates the quadrotor to a desired altitude. Also, the superiority of this control algorithm is demonstrated by comparing the results of the control inputs with those obtained with a regular terminal sliding mode controller.

In this paper, a modification in the Kharitonov theorem is proposed to fix the robustness criteria of the discrete time systems. This proposed method is noble and quite simpler than the existing method of the Kharitonov theorem for discrete time plants. In this method, to ensure robust stability, there is no need to check the stability of all four Kharitonov interval polynomials, whether it can be calculated through the much simpler conditions by only knowing the polynomials' maximum and lowest limits up to fourth order polynomials. The proposed method is derived and verified for the different orders of the plants. Further, the proposed method is implemented as a physical example of a four tank MIMO system. A PID controller is designed in this work on this proposed method to find the robust gains, which are further fine-tuned by the TLBO algorithm. Tests of the multi-channel input output gain change, delay variation, and output disturbance to the plant are used to assess the robustness of the developed controller.

The burgeoning digitalization of industrial automation systems (IASs) has amplified their vulnerability to sophisticated cyberattacks, necessitating robust and adaptable detection mechanisms. This review provides insights into the current landscape of cyberattack detection in IASs and underscores the potential of model predictive control (MPC)-based AI techniques to bolster security and resilience in critical infrastructure settings. By harnessing the combined strengths of physically grounded MPC models and data-driven AI algorithms, this framework offers significant advantages over traditional methods. The review navigates through existing literature, scrutinizing diverse approaches for cyberthreat detection. An emphasis is placed on the proactive nature of MPC, which enables the modeling and optimization of complex system dynamics, coupled with the adaptability and learning capabilities of AI algorithms. These features collectively empower the system to identify anomalies indicative of cyberattacks in real-time, thus fortifying IAS against potential disruptions. Key findings from reviewed studies demonstrate the previous technologies in detecting and mitigating cyberthreats while maintaining the stability and functionality of industrial processes. The review further highlights a problem formulation based on MPC method to detect cyberattacks, including computational efficiency and real-time responsiveness. This review concludes by affirming the immense potential of MPC-based AI to revolutionize cyberattack detection in IAS. Its robust and adaptable nature offers a compelling alternative to existing methods, paving the way for securing critical infrastructure in the face of ever-evolving cyberthreats.

Controlling nonlinear systems remains a complex challenge, even when their dynamic models are known, due to inherent uncertainties and unpredictable behaviors that affect system performance and stability. This complexity has led to the growing adoption of multi-controller strategies supervised by advanced controllers, offering substantial advancements over the years. These strategies have evolved from simple approaches to sophisticated techniques that integrate artificial intelligence and machine learning, significantly improving the robustness, performance, and adaptability of control systems across various industries. This paper describes a novel supervisory control approach for a nonlinear cement ball mill grinding system. The proposed approach combines two controllers under the guidance of a fuzzy supervisor: A Proportional-Integral-Derivative (PID) controller, fine-tuned through the Grey Wolf Optimization (GWO) algorithm to achieve a rapid and precise dynamic response, and a Fuzzy Logic Controller (FLC), which delivers robust performance during steady-state operation while dealing with the uncertainties associated with the process. The supervisory system employs advanced fuzzy aggregation operators, specifically the 2-additive fuzzy Choquet integral, and the fuzzy arithmetic mean, to evaluate tracking error and its variation. These evaluations dynamically determine the contributions of the PID and FLC controllers, ensuring smooth transitions while augmenting the benefits of each controller. Comparative analyzes with recent control methods highlight the superiority of the proposed approach in achieving a more stable and efficient cement grinding process. This innovative approach ensures flexible and robust management of the studied system, enhancing its overall performance while being easy to implement. It also provides better adaptation to system variations and increased robustness against uncertainties and disturbances.