Advanced Control for Applications最新文献

This paper investigates the detection and control of chaotic behavior in field-oriented control (FOC) systems for Permanent Magnet Synchronous Motors (PMSMs). Chaos, characterized by unpredictable and highly sensitive dynamics, can significantly impact the stability and performance of electrical machines. We begin by employing conventional methods, such as computing the Largest Lyapunov Exponent (LLE) and analyzing attraction basins, to distinguish between chaotic and periodic behaviors. Building on this foundation, we introduce a three-stage neural network (NN)-based control design for chaos synchronization, leveraging unsupervised learning (UL) to exploit the hidden properties of chaotic systems without explicit supervision. By integrating clustering, dimensionality reduction, and unsupervised modeling techniques, we demonstrate the potential to efficiently synchronize chaotic behavior in PMSMs. This approach not only enhances the understanding of chaotic dynamics but also enables the design of a robust NN-based control strategy. The proposed methodology highlights the synergy between artificial intelligence (AI) and chaos theory, offering powerful tools for analyzing and controlling chaotic systems. Our findings pave the way for robust applications in complex industrial environments, where chaos synchronization can improve the reliability and efficiency of electrical machines. This study underscores the transformative potential of AI-driven techniques and the three-stage neural control framework in advancing the control of chaotic systems and their practical implementation in real-world scenarios.

Position control of direct current motors remains one of the most important control problems in various application domains like robotics, automation in industries, and aviation. Traditionally, Proportional–Integral–Derivative based controllers are most popular for such scenarios, however due to their inability to handle constraints and are not being optimal and robust by design, they are not preferred in precision position tracking applications like antenna positioning, pitch angle control for wind turbine blades, solar tracking in photovoltaic panels etc. This calls for the need to employ some robust and high-precision controllers like model predictive control. The main objective of the work carried out is to present a better alternative for the position control problem for a DC servo motor plant using model predictive control. The optimization problem is formulated to minimize the cost function that penalizes position errors and input changes, along with the necessary constraints on output and inputs. The implementation of the proposed scheme is carried out both in simulations and with experimentation. In simulation, the scheme is verified using MATLAB/Simulink, and in experimentation on the real plant of Quanser's DC servo motor setup through Simulink real-time interface blocks. The obtained simulation and experimental results efficiently validate the proposed theoretical findings by gracefully achieving the required position trajectory tracking. Achieved results are also compared with standard PID, which confirms the superiority of model predictive control over PID control, especially in handling constraints and yielding better tracking performance without any overshoots and with the overall lesser control energy requirement.

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.