Advanced Control for Applications最新文献

The existing spatial motion control methods for industrial production robotic arms suffer from sluggish convergence efficiency and inadequate control accuracy. With the aim of resolving this matter, the research proposes an optimization method for spatial motion control of robotic arms based on modeling analysis and the fast extended random tree algorithm. The study first models and analyzes the spatial motion of the robotic arm, and combines the Monte Carlo method for random sampling to create a significant amount of feasible paths. The difference between the feasible path and the planned path is that the feasible path is optimized through multiple iterations, gradually approaching the optimal solution, while the planned path is a predetermined fixed path. Afterwards, the improved fast expansion random tree algorithm is used to optimize the path and achieve efficient planning of the robotic arm path. Finally, the proportional integral derivative control method and the recursive least squares method are employed to flexibly control the spatial motion end of the robotic arm, improving the motion accuracy and stability of the robotic arm. The experimental outcomes reveal that the node utilization rate and planned path length of the proposed control method in simulation analysis are 20.71% and 111.61 cm, respectively, and the fitting degree between its control trajectory and the ideal trajectory exceeds 0.98. In practical applications, the average time required for the robotic arm using the proposed method is only 12.5 s, and the quality pass rate remains above 98%. In addition, the completion rate of its winding task is 99.5%. The proposed control optimization approach can effectively improve the control accuracy and production quality of industrial production robotic arms.

This study presents a robust control strategy for unstable cascade processes with time delays, integrating Particle Swarm Optimization (PSO) and the Internal Model Control (IMC) framework. The proposed methodology employs a dual-controller architecture, where the secondary controller is systematically tuned using the IMC approach, while the primary controller is optimized via PSO to enhance closed-loop stability and performance. Extensive simulation studies were conducted across various unstable cascade processes to evaluate the effectiveness of the proposed approach in both servo and regulatory tasks. Comparative analysis with state-of-the-art control methodologies demonstrates that the proposed strategy achieves superior closed-loop performance, particularly in handling system uncertainties. A comprehensive numerical assessment using multiple performance indices indicates a substantial reduction in total error by 65% and control effort by 43%. The evaluation metrics include error minimization, total variation, rise time, and overshoot percentage, affirming the efficacy and robustness of the proposed control scheme.

This article presents the adaptive sliding mode method for the gun elevation system of the tank operating under model uncertainties and external disturbances. The proposed controller combines optimal control design, the adaptive compensation mechanism using the radial basis function (RBF) neural network, while integrating the sliding mode control (SMC) law to enhance robustness and trajectory tracking accuracy. The RBF network is used to estimate and compensate for unknown nonlinear components and dynamic uncertainties in real time, and the SMC law is incorporated to ensure robustness and force the system output to accurately follow the desired trajectory. The control strategy is synthesized to meet strict performance requirements under complex real-world operating conditions. Simulation studies conducted in Matlab evaluate the controller's effectiveness. The results demonstrate that the proposed method achieves accurate trajectory tracking, strong disturbance rejection, and improved robustness, confirming its potential for practical military applications.

Inverted pendulum is an example of a classical problem in control theory that has been widely used for investigating control algorithms like state feedback, artificial neural networks, fuzzy control, and robust control. The piecewise Affine-fuzzy (PWA-Fuzzy) approximation of an inverted pendulum on a cart has been investigated in previous literature. The main drawbacks of PWA approximation, i.e., discontinuity in control signal and chattering between different regions, are addressed by the PWA-Fuzzy model. In this paper, with the aim of stabilizing the system in the open-loop unstable equilibrium point, a patched-fuzzy state feedback (PFSF) controller is designed as an improved form of the conventional state feedback controller for the PWA-Fuzzy model of an inverted pendulum on a cart. Because of the intention to implement the mentioned controller for a real plant, the identification of PWA-Fuzzy model parameters by the linear least squares method based on the numerical method is presented. Furthermore, the implementation of the mentioned controller using two different techniques, including analog and digital circuits, is presented. Finally, in order to evaluate the proposed method, the simulation and experimental results are compared.

Oscillatory behavior in process control loops is a persistent challenge in industrial plants, often resulting in diminished control performance, increased energy consumption, and economic losses. If left unaddressed, such oscillations can propagate throughout the plant, causing variability in downstream processes and negatively impacting throughput and product quality. Accurate detection and classification of oscillations, along with identification of their root causes, are therefore critical for enabling timely corrective actions that enhance control performance and overall process efficiency. Common causes of oscillations in process control include control valve stiction, suboptimal PID tuning, measurement noise, and external disturbances, each imparting distinct dynamic patterns on the process variable (PV) and controller output (OP). Manual detection and classification of these oscillations through visual analysis is time-consuming and impractical due to the large number of control loops in modern plants. In this paper, we present an automated deep learning framework for the detection and classification of oscillations in process control loops. The proposed method employs a one-dimensional convolutional neural network (1D-CNN) to analyze time-series data from PV and OP signals, enabling the model to learn and distinguish between different oscillation patterns associated with various root causes. The framework is trained and validated using both simulated datasets and real industrial plant data, ensuring robustness across a wide range of oscillation scenarios. Case studies are provided to illustrate the practical application of the method, and results demonstrate that the proposed approach achieves high accuracy in both detecting the presence of oscillations and correctly identifying their underlying causes. This automated solution offers a scalable and efficient tool for plant operators and engineers, supporting proactive maintenance, improved control loop reliability, and enhanced overall plant performance.

This paper aims to develop a nonlinear control algorithm to optimize the operation of a single input single output (SISO) steam boiler system. First, a dynamic model based on mean density and mean specific internal energy is developed and validated against published data to accurately capture the system's behavior. Building on this model, two advanced nonlinear control strategies—Generic Model Control (GMC) and Globally Linearizing Control (GLC)—are systematically designed to improve system performance. To address the challenge of unmeasurable internal states, a Lyapunov-based state observer is formulated and integrated with the nonlinear controllers. For comparison, a conventional Proportional-Integral (PI) controller is also implemented. Simulation results demonstrate that the observer-enhanced GMC approach significantly outperforms both GLC and PI controllers in regulating boiler steam temperature through heat input manipulation. The work offers a novel integration of nonlinear control, state estimation, and performance benchmarking, contributing a robust and realistic solution to the control of nonlinear energy systems.

Switched reluctance motors (SRMs) are less stable, simple, and reliable, even in harsh environments. Despite its advantages, SRM remained out of date until advancements in power electronic devices made it possible to implement SRM drives. The efficiency of SRMs is limited by issues such as high torque ripple, low power factor (PF), and control complexity. Developments in power electronics have stimulated concepts for further enhancing the performance of SRMs, making them even better candidates for modern applications. Hence, issues related to acoustic noise and nonlinear characteristics remain. Addressing these constraints ensures reliable operation and greater efficiency. In this paper, an enhanced Z-Source converter-based controller is developed for voltage management and PF correction of SRMs, an innovative front-end converter that simultaneously performs voltage regulation and PF correction, tailored for SRM performance enhancement. The proposed converter, acting as a front-end device, performs power-factor correction and voltage regulation by adjusting the magnetization voltage according to the operating mode and drive structure requirements. To achieve these objectives, a central control technique (CCT) is developed that reduces the third-harmonic distortion (THD) and improves the PF. Moreover, angle control is employed to reduce torque ripple and maintain voltage regulation in the front-end converter. It uses a fractional order integral derivative (FOPID) system that is optimized using the modified coronavirus mask protection algorithm (MCMPA). This optimization was improved by MCMPA, which is an addition of the coronavirus mask protection algorithm (CMPA) combined with Levy flight distribution (LFD). Efficient operation of the converter ensures improved voltage management and PF correction. To validate the performance of the proposed controller, the SRM motor was tested under electric vehicle (EV) load conditions. To validate the proposed methodology, it was designed in MATLAB; the performance was evaluated using different measures such as SRM motor current, voltage, speed, and torque. The proposed methodology was compared with conventional approaches such as ant colony optimization (ACO), whale optimization algorithm (WOA), and enhanced fire hawk optimization (EFHO).

This research outlines the design and implementation of a constrained adaptive sliding-mode controller specifically developed for a nonlinear system involving a plate and a ball. A particular platform with rotary actuators has been chosen, and the comprehensive construction methodology is detailed to support the positioning of the ball on the plate. To attain high sampling rates for positional data, a resistive touchscreen panel is employed as a sensor, considering the field of view. The accuracy of the model was validated using real system data, confirming that it accurately represents the actual dynamics. The adaptive sliding-mode controller features error integration, which improves robustness against uncertainties in parameters, such as changes in the weight of the ball. A constrained control framework that utilizes the Barrier Lyapunov function is applied to keep the system state variables within allowable limits, addressing the limitations of the sensors' field of view. Both simulation and experimental findings indicate that the tracking controller, which is integrated with a constrained adaptive sliding-mode structure, successfully stabilizes the nonlinear plate and ball system.

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.