Advanced Control for Applications最新文献

Optimizing the tuning parameters for a second-order linear active disturbance rejection controller (SLADRC) presents a challenge due to the complexity of system dynamics. This paper proposes a novel tuning method combining analytical, graphical, and optimization techniques. A graphical approach defines a feasible region based on gain margin and phase crossover frequency using a unified method, while a hybrid method integrating these rules with the Lichtenberg Optimization Algorithm precisely determines optimal parameters. The objective function of the proposed technique is formulated to minimize the deviations in desired settling time, overshoot, and disk margin. The proposed SLADRC tuning approach is evaluated through simulation on two benchmark systems and verified in real time on a DC motor position control system, employing two different loading arrangements. The proposed tuning achieves optimum performance with an average error of less than 0.1% within 30 iterations, and the resulting SLADRC outperforms PID and state feedback controllers under parameter uncertainties and external force disturbances in real time.

Aiming at the bottlenecks of traditional SCARA robot dynamics control method, such as high computational complexity, insufficient parameter identification accuracy, and weak anti-interference ability, a recursive Newton–Euler control framework based on genetic algorithm optimization is proposed. The optimal performance of the Newton–Euler method could achieve 98% accuracy, which was 7%–10% higher than that of the PSO/machine learning model. The NEA recursive computing architecture was designed to reduce the dynamic analysis complexity of the multi-joint system from O(n³) to O(n), and the single-cycle computation time was reduced to 9.0 s (efficiency increased by 14.3%). The practical test results showed that the discrimination rate of the dynamic parameters of the robot based on the model was higher than that of the dynamic control model based on machine learning, which could reach more than 90%. The stronger the stability, the smaller the torque change caused by the collision between the robot and the object, and the variation range was from 90 to −30 nm. In conclusion, the SCARA dynamic control model based on the Newton–Euler algorithm has high control accuracy and stability. The research breaks the contradiction between precision and real time in highly dynamic scenes and provides a new paradigm for the precision control of industrial robots. In the future, reinforcement learning will be integrated to build a hybrid architecture to improve the adaptability to complex working conditions.

Magnetic microrobotic systems include robotic manipulation of objects with characteristic dimensions in the millimeter to micrometer range for various promising applications in biomedical and micro-manufacturing industries. In this work, a bias current-based controller, a PI-based controller, and an active disturbance rejection controller are designed and implemented for efficient control performances. The system has more inputs than outputs, and the inputs are nonlinear. The proposed model-independent controllers are able to linearize the input nonlinearities and decouple the control currents of the 1D, 2D, and 3D magnetic micromanipulators. It is shown that the controllers provide a guaranteed asymptotic stability of the magnetic microrobot, fast and non-overshoot transient responses, and virtually zero steady-state tracking error.

This article proposes an improved sensorless capacitor voltage balancing (CVB) method for modular multilevel converters (MMCs) for high-voltage direct current applications. The suggested method prioritizes achieving precise, straightforward, and computationally efficient control of MMCs, eliminating the necessity for external sensors. Simultaneously, it guarantees effective management of capacitor voltage balance within the converter arms. Combining the proposed sensorless control technique and CVB methods improves converter performance, reduces complexity, and increases overall system reliability. To validate the effectiveness of the proposed strategy, full simulations are performed. The simulation setup includes the MMC structure, the control algorithm, and the sensorless CVB method. The simulation results demonstrate the accurate regulation of energy flow while maintaining balanced capacitor voltages between the arms of the MMC. In addition, experimental verification is carried out using a scaled-down laboratory prototype of the MMC system. The experimental results validate the practical feasibility and reliability of the proposed control strategy.

Nonlinear extension of the integral part of a standard proportional-integral-derivative (PID) feedback control is proposed for perturbed second-order systems. The approach is model-free and requires solely the Lipschitz boundedness of the unknown matched perturbations. For constant disturbances, the global asymptotic stability is shown based on the circle criterion. For Lipschitz perturbations, an ultimately bounded output error is provided based on the steady-state behavior in frequency domain. Also the transient response to the stepwise disturbances is analyzed for the control tuning. Based on the developed analysis, the design recommendations are formulated as a step-by-step procedure. It is also discussed how the proposed control is applicable to second-order systems extended by additional (parasitic) actuator dynamics with low-pass characteristics. The proposed nonlinear control is proven to outperform its linear PID counterpart during the settling phase, that is, at convergence of the residual output error. An experimental case study of the second-order system with an additional actuator dynamics and considerable perturbations is demonstrated to confirm and benchmark the control performance.

The flow control loop in industrial automation employs a low-cost embedded platform to improve system performance and enable real-time monitoring. The challenge is to develop an effective flow control loop for industrial automation using a low-cost embedded platform to improve system evolution and enable real-time monitoring. The goal is to develop a flow control loop for industrial automation that facilitates system evolution and real-time monitoring through an affordable embedded platform. Multi-scale Median Filtering (MSMF) is applied in pre-processing to remove noise and improve signal clarity, optimizing the flow control loop for monitoring and managing industrial automation on a low-cost embedded platform. SDN is applied in implementation strategies to improve flexibility, scalability, and communication efficiency in low-cost embedded platforms for industrial automation. In implementation strategies for low-cost embedded platforms in industrial automation, NFV improves flexibility and scalability by separating system functions from the hardware. Graph Convolutional Networks (GCN) are utilized in implementation strategies for low-cost embedded platforms to process spatial and temporal data, improving decision-making and control within industrial automation systems. The findings of the flow control loop for industrial automation with a low-cost embedded platform highlight enhanced efficiency, affordability, and real-time monitoring, leading to better system performance and reliability. The result shows that the proposed technique outperforms all, with accuracy at 98%, precision at 95%, recall at 89%, and F1-score at 90%, implemented using Python software. The future scope of the flow control loop for industrial automation on a low-cost embedded platform involves enhancing scalability, integrating advanced sensors, and optimizing system performance for a wider range of industrial applications.

Power electronic converters integrating Wide-Bandgap (WBG) semiconductor devices, based on Silicon Carbide (SiC) and Gallium Nitride (GaN), demonstrate superior efficiency compared to conventional silicon-based counterparts. This work investigates the performance of a novel WBG SiC MOSFET switch-based DC-DC boost converter in a solar-fed power system. A fractional-order PID (FOPID) controller, with gain parameters optimized by the particle swarm optimization (PSO) algorithm, is employed for controlling the converters. The transfer characteristics, output characteristics, and transient characteristics of the WBG switch are validated through MATLAB simulation using an available model. The capability of the proposed WBG-based FOPID-controlled DC-DC converter to maintain stability and robustness under varying irradiance as well as load transients is assessed through comprehensive MATLAB simulations. The performance comparison of the proposed DC-DC converter using Proportional Integral (PI), Proportional Integral Derivative (PID), and FOPID controllers, with both WBG and traditional MOSFET switches, was carried out. The results validate the superiority of WBG switches over conventional switches as well as the effectiveness of the fractional parameter effect on the system response. The proposed approach ensures high efficiency performances under medium voltage applications, which are suitable for charging electric vehicles, making it a promising solution for advanced power electronics applications.

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.