Advanced Control for Applications最新文献

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.

The proportional-integral-derivative (PID) controller is an important digital control structure that is frequently used by designers in many speed, position, and temperature applications. The main problem of such control structures is that the mathematical calculation burden is high and requires precise parameter determination. In this study, the control parameters were estimated to overcome the two main problems given above for the speed control of a Permanent Magnet Direct Current (PMDC) motor produced in real time. For this process, the Nonlinear Least Squares (NLS) optimization method with the Trust-Region-Reflective (TRR) algorithm, the Pattern Search (PS) optimization method with the Active-Set (AS) algorithm, and the Simplex Search (SS) optimization method with the AS algorithm have been proposed. The proposed techniques are explained in detail, and the Sum of Square Error (SSE) is chosen as the objective function for these techniques. The predicted controller values for a single reference were tested under different speed, load, and armature resistance conditions. The speed control of the PMDC motor, which was created using the parameters estimated over the specified simulation periods, was tested with different load conditions. According to the results obtained, while the NLS-TRR technique stands out in terms of calculation time, the SS-AS technique outperformed in terms of precise parameter estimation.

Electrical motors' effectiveness, optimization, and dependability have led to significant developments in the industrial revolution. Rotor commutation is essential for smooth operation and is done by energizing the proper rotor. The study introduces an innovative methodology for fault-tolerant control of Hall-Effect sensors governing brushless direct current (BLDC) motors. The implemented result validates the substantial saving of energy. The implementation here is unique and effective because the BLDC motor will be operated even if there is a fault in the Hall-Effect sensor. It will run smoothly and efficiently unless the two sensor failures are detected. This method addresses situations where the signal from a Hall Effect sensor stays consistently low or high for short periods. This developed system is mature enough to handle the subsystem when the data from the sensor detects faults, identifies faulty signals, and creates a replacement signal. The proposed research work uses MATLAB Simulink and the TI-DSP (TMS320F2812) KIT to show the high-speed simulation. The simulation results were validated through different motor speeds from 3500 to 2000 rpm. Drawbacks like cost and reliability have been overcome in the implemented research work. In the implemented research work, the simulation outcome shows approximately 10% better results in generating more torque than the existing methods using sinusoidal windings.