Advanced Control for Applications最新文献

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.

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.