Performance Of Control Algorithm Research Articles

Research on the simulation method of a BP neural network PID control for stellar spectrum

This study investigated the multiple correlations among spectral simulation units based on digital micromirror device (DMD) spectral simulation, which leads to the problem that conventional spectral simulation methods such as PID control exhibit a low fitting accuracy or long fitting time in the spectral simulation of various targets. In this paper, a method of stellar spectrum simulation based on back propagation neural network-based PID (BP-PID) control is proposed to achieve high efficiency and high precision simulation of various spectral targets. The topology of the BP neural network was constructed based on the spectral modulation model of a DMD stellar spectrum simulation system, and the algorithm of the BP-PID control was designed. Finally, an experimental platform was built to verify the performance and spectral simulation accuracy of the BP-PID control algorithm. The results show that the overshoot and response time of the BP-PID control algorithm decreased by 79.01% and 30%, respectively compared with those of the PID control algorithm. The maximum spectral simulation accuracies of 2000K, 7000K, and 12000K color temperature increased by a factor of 2.311, 1.871, and 2.254, respectively, and the standard deviations of the spectral simulation error decreased by 56%, 41%, and 54%, respectively. In the range of 2000-12000K color temperature, the spectral simulation error of the BP-PID control algorithm is better than ±3.495%, and the standard deviation of the spectral simulation error is between 1.8255 and 2.2358. The proposed method can improve the spectral simulation accuracy and simulation efficiency of a star simulator, reduce the magnitude and spectrum calibration errors caused by the differential response, improve the star feature recognition accuracy of the orbiting star sensor, and hence, provide a theoretical and technical basis for the development of high-precision star sensors.

Investigating the Impact of Discretization Techniques on Real-Time Digital Control of DC-DC Boost Converters: A Comprehensive Analysis

This paper provides a thorough comparative analysis of discretization techniques commonly utilized in digital control applications for DC-DC boost converters (DBCs). Given the crucial role of DC-DC converters in various industries such as renewable energy systems, electric vehicles, and portable electronic devices, it is imperative to optimize their performance through efficient digital control. This study aims to improve the understanding of the effects of discretization methods on control system behaviour. It investigates their impact on the performance, stability and accuracy of control algorithms in the context of DBC, using dSPACE real-time interface (RTI) and hardware-in-the-loop (HIL) simulation for validation. This approach replicates real-world conditions, allowing performance to be evaluated in a controlled environment. The analysis examines behaviour in the time and frequency domains, providing insight into the strengths and weaknesses of each method. Experimental validation using MATLAB/Simulink/Stateflow on dSPACE RTI 1007 processor, DS2004 high-speed A/D and CP4002 timing and digital I/O boards ensures that the comparative analysis provides practical benefits for DBC digital control applications.

Intelligent control of the Magnus anti-rolling device: A co-simulation approach

In this study, we present an artificial intelligence control method specifically designed for the Magnus anti-rolling device. The core of our approach is the development of a co-simulation framework that integrates an intelligent algorithm with a three-dimensional numerical computational programme within a complex hydrodynamic environment. This integration is achieved through the use of a deep reinforcement learning algorithm, which allows for intelligent adaptation of the anti-rolling device's rotating speed in real time. Our research focuses on evaluating the performance of the intelligent anti-rolling control algorithm under a variety of conditions, including different ship-model roll angles, column geometry models, and varying swinging or slewing speeds. Through numerical studies, we compare the effects of these variables on the effectiveness of the control method. The co-simulation technology provides a platform for testing the intelligent control method. It allows a detailed examination of how the intelligent algorithm interacts with the hydrodynamic responses of the Magnus anti-rolling device, ensuring that the control method can adapt to a wide range of operational scenarios. This adaptability is crucial for maintaining stability and improving the performance of the anti-rolling device in real maritime environments. This study highlights the potential for integrating artificial intelligence with traditional maritime engineering solutions.

Development of a Robotic Platform with Autonomous Navigation System for Agriculture

The development of autonomous agricultural robots using a global navigation satellite system aided by real-time kinematics and an inertial measurement unit for position and orientation determination must address the accuracy, reliability, and cost of these components. This study aims to develop and evaluate a robotic platform with autonomous navigation using low-cost components. A navigation algorithm was developed based on the kinematics of a differential vehicle, combined with a proportional and integral steering controller that followed a point-to-point route until the desired route was completed. Two route mapping methods were tested. The performance of the platform control algorithm was evaluated by following a predefined route and calculating metrics such as the maximum cross-track error, mean absolute error, standard deviation of the error, and root mean squared error. The strategy of planning routes with closer waypoints reduces cross-track errors. The results showed that when adopting waypoints every 3 m, better performance was obtained compared to waypoints only at the vertices, with maximum cross-track error being 44.4% lower, MAE 64.1% lower, SD 39.4% lower, and RMSE 52.5% lower. This study demonstrates the feasibility of developing autonomous agricultural robots with low-cost components and highlights the importance of careful route planning to optimize navigation accuracy.

Adaptive collaborative coverage control for Multi-Stratospheric Airship within unknown non-uniform wind field

This paper investigates the coverage control problem for Multi-Stratospheric Airship (MSA) system within unknown non-uniform wind field. To address the challenge of deploying airships strategically in regions with weak wind, an adaptive collaborative coverage control algorithm is proposed. The algorithm includes a coverage planning approach based on wind field estimation and an airship motion control approach. Firstly, as accurate wind field data in the mission area is unavailable, an adaptive estimation algorithm based on the Gaussian approximation method is proposed to estimate the wind data. This involves processing discrete wind field sampling information with a density function. Secondly, an event-triggered dynamic tracking controller with a neural network to handle model uncertainties is proposed to steer the MSA system approaching a near-optimal configuration. Additionally, convergence of the coverage system is proven through a Lyapunov-like proof. Simulation results illustrate the superior performance of the adaptive collaborative coverage control algorithm.

Modelling of Vehicle Longitudinal Dynamics for Speed Control

Longitudinal dynamics control is one of the essential tasks for an autonomous vehicle, where it deals with speed regulation to ensure smooth and safe operations. To design a good controller, a simple yet reliable mathematical model is needed so that it can be used as a plant and to tune the controller. Although there are many types of mathematical models available in the literature, finding the right one for control application is essential. The model cannot be too complex and can be too simple. Thus, the main objective of this work is to derive a simple yet reliable vehicle longitudinal model so that it can be used as a simulation plant in MATLAB Simulink to test or tune various types of control algorithm's performance. The model consists of three main parts which are the vehicle body dynamics, simplified power train dynamic, and braking dynamic. To validate the reliability of the model, standard urban drive cycles will be used as a reference speed and a hierarchical PID control structure with inverse plant model is used to control the pedal inputs replacing the driver in simulation environment. Results show that the controller managed to track the drive cycle with an acceptable pedal pressing response which is between 40% throttle press and 20% brake press that in line with the normal operation of a vehicle. Although only simulation result is presented, the model can be used as a good starting point for further development and testing of different types of control algorithms for future work.

A GENERATIVE APPROACH TO TESTING THE PERFORMANCE OF PHYSIOLOGICAL CONTROL ALGORITHMS.

Physiological closed-loop control algorithms play an important role in the development of autonomous medical care systems, a promising area of research that has the potential to deliver healthcare therapies meeting each patient's specific needs. Computational approaches can support the evaluation of physiological closed-loop control algorithms considering various sources of patient variability that they may be presented with. In this paper, we present a generative approach to testing the performance of physiological closed-loop control algorithms. This approach exploits a generative physiological model (which consists of stochastic and dynamic components that represent diverse physiological behaviors across a patient population) to generate a select group of virtual subjects. By testing a physiological closed-loop control algorithm against this select group, the approach estimates the distribution of relevant performance metrics in the represented population. We illustrate the promise of this approach by applying it to a practical case study on testing a closed-loop fluid resuscitation control algorithm designed for hemodynamic management. In this context, we show that the proposed approach can test the algorithm against virtual subjects equipped with a wide range of plausible physiological characteristics and behavior, and that the test results can be used to estimate the distribution of relevant performance metrics in the represented population. In sum, the generative testing approach may offer a practical, efficient solution for conducting pre-clinical tests on physiological closed-loop control algorithms.

Investigation of a high-performance control algorithm for a unified chaotic system synchronization control based on parameter adaptive method

With the rapid development of computer technology, parameter adaptive control methods are becoming more and more widely used in nonlinear systems. However, there are still many problems with synchronous controllers with multiple inputs and a single output, uncertainty, and dynamic characteristics. This paper analyzed a synchronization control strategy of uncoupled nonlinear systems based on parameter dynamic factors to adjust the performance of the synchronization controller, and briefly introduced the manifestations of chaotic motion. The characteristics and differences of continuous feedback control methods and transmission and transfer control methods were pointed out. Simple, effective, stable, and feasible synchronous control was analyzed using parameter-adaptive control theory. By analyzing the non-linear relationships between various models at different orders, the fuzzy distribution of the second-order mean and their independent and uncorrelated matrices were obtained, and their corresponding law formulas were established to solve the functional expression between the corresponding state variables and the dynamic characteristics of the system. The error risk test, computational complexity test, synchronization performance score test, and chaos system control effect score test were carried out on the control algorithms of traditional chaos system synchronization methods and chaos system synchronization methods based on parameter adaptive methods. Parameter adaptive methods were found to effectively reduce the error risk of high-performance control algorithms for synchronization of the unified chaos system. The complexity of the calculation process was simplified and the complexity score of the calculation process was reduced by 0.6. The application of parameter adaptive methods could effectively improve the synchronization performance of control algorithms, and the control effectiveness rating of control algorithms was improved. The experimental test results proved the effectiveness of control algorithms, which greatly enriched the field of modern control applications and also drove the vigorous development of nonlinear dynamics research, thus making significant progress in chaos application research.

Intelligent vehicle trajectory tracking control based on physics-informed neural network dynamics model

In order to solve the accuracy problem of trajectory tracking control method based on data-driven model, an intelligent vehicle trajectory tracking control method based on physics-informed neural network (PINN) vehicle dynamics model is proposed. Aiming at the problem of poor interpretability of data-driven model, a vehicle dynamics model based on the PINN is established, and the physics-driven deep learning method is used instead of the data-driven deep learning method to obtain the dynamic characteristics of the intelligent vehicle, to benefit from both the physical-based method and the data-driven method. A sequential training method is also proposed to solve the coupling problem when training multiple PINNs simultaneously. The model takes the nonlinearity of the neural network model and physical interpretability into consideration compared to the standard neural network model. Then, based on the PINN vehicle dynamics model, a trajectory tracking controller based on the iterative linear quadratic regulator (ILQR) control algorithm is developed. The optimal control law is derived by optimizing the ILQR control algorithm to implement the intelligent vehicle’s precise and stable tracking for the desired trajectory. The Levenberg-Marquardt (LM) algorithm and line search technology are used and damping factor adjustment rules are set up to enhance the convergence performance of the ILQR control algorithm. In order to verify the effectiveness of the proposed method, the simulation is conducted under the condition of double lane change. The simulation results demonstrate that the proposed method can track the reference trajectory accurately under the limited conditions. Its control performance is much better than other algorithms.

Disrupting abnormal neuronal oscillations with adaptive delayed feedback control.

Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson's disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.

Automatic control of reactive brain computer interfaces

This article discusses theoretical and practical aspects of real-time brain computer interface control methods based on Bayesian statistics. The theoretical aspects include how the data from the brain computer interface can be translated into a Gaussian mixture model that is used in the Bayesian statistics-based control methods. The practical aspects include how the control methods improve the performance of the brain computer interface. We use a reactive brain computer interface based on a visual oddball paradigm for the investigation and improvement of the performance of automatic control and feedback algorithms used in the system. By using automatic control for selection of the stimuli for the visual oddball experiment, the target stimulus is identified faster than if no automatic control is used. Finally, we introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.

Exploring the feasibility of autonomous forestry operations: Results from the first experimental unmanned machine

AbstractThis article presents a study on the world's first unmanned machine designed for autonomous forestry operations. In response to the challenges associated with traditional forestry operations, we developed a platform equipped with essential hardware components necessary for performing autonomous forwarding tasks. Through the use of computer vision, autonomous navigation, and manipulator control algorithms, the machine is able to pick up logs from the ground and manoeuvre through a range of forest terrains without the need for human intervention. Our initial results demonstrate the potential for safe and efficient autonomous extraction of logs in the cut‐to‐length harvesting process. We achieved a high level of accuracy in our computer vision system, and our autonomous navigation system proved to be highly efficient. This research represents a significant milestone in the field of autonomous outdoor robotics, with far‐reaching implications for the future of forestry operations. By reducing the need for human labor, autonomous machines have the potential to increase productivity and reduce labor costs, while also minimizing the environmental impact of timber harvesting. The success of our study highlights the potential for further development and optimization of autonomous machines in the forestry industry.

Nonlinear Model Predictive Control for a Dynamic Positioning Ship Based on the Laguerre Function

In this paper, we present a novel nonlinear model predictive control (NMPC) algorithm based on the Laguerre function for dynamic positioning ships to solve the problems of input saturation, unknown time-varying disturbances, and heavy computation. The nonlinear model of a dynamic positioning ship is presented as a linear model, transformed from a standard affine nonlinear state-space model by precise feedback linearization. The environmental disturbance is overcome using an integrator. The time cost of the proposed nonlinear control algorithm is decreased by inducing the Laguerre function to describe the feedback-linearization system input increments. The Laguerre function reduces the matrix dimensions of the nonlinear optimization problem. The simulation results for a DP supply vessel showed that the novel algorithm maintained the effective control performance of the original nonlinear model predictive control algorithm and had a reduced computation load to satisfy the requirements of real-time operation.

Evaluating the Impact of Drivetrain Vibrations on MPPT Control Performance in Horizontal Axis Wind Turbines

This study investigates the impact of drivetrain shift vibrations on the control performance of Horizontal Axis Wind Turbines (HAWTs) using a Proportional-Integral-Derivative (PID) controller for Maximum Power Point Tracking (MPPT). Traditionally, PID controllers are tested on simplified rigid models, which do not account for the complex mechanical vibrations encountered in real-world applications. These vibrations, particularly those caused by drivetrain shifts, can significantly affect the stability and efficiency of the control system. Through detailed simulations involving both rigid and flexible drivetrain models, this paper evaluates how drivetrain vibrations influence the performance of the PID-based MPPT control algorithm. The results indicate that the flexible model, which incorporates drivetrain dynamics, experiences pronounced overshoot, oscillations, and significant drops in power coefficient (Cp) compared to the rigid model. These findings highlight the challenges of maintaining control stability and efficiency under varying vibration conditions, with the flexible model showing compromised stability and reduced power conversion efficiency during certain key intervals. This study emphasizes the importance of considering drivetrain dynamics in wind turbine control system design and provides insights into developing more robust and resilient PID control strategies.

Comparative Analysis of Optimal Control Strategies: LQR, PID, and Sliding Mode Control for DC Motor Position Performance

This study applies these control methods to the DC motor system to examine the robustness and performance of four optimal control methods. Optimal controllers aim to control the system to minimize a selected performance index. These control methods offer advantages such as improving energy efficiency, reducing costs, and enhancing system security. The Linear Quadratic Regulator (LQR) based controller is the primary optimal control method. Two well-known traditional control techniques include the Proportional-Integral-Derivative (PID) and Integral Sliding Mode Controller (ISMC). However, they do not usually contain optimal properties. In this study, the optimal control algorithms, defined by obtaining controller parameters through the Riccati equation, are applied to achieve accurate position-tracking control in a DC motor system using Matlab/Simulink. The integral term-based algorithms seem to be robust and eliminate steady-state errors. The optimal PID controller could not provide the minimum performance index, unlike the other controllers in the study. LQR and optimal ISMC algorithms could allow the performance index to be a minimum. An illustrative comparison of the performances of all optimal control algorithms has been presented through graphical representation, along with corresponding interpretations.

Development of a Hardware-in-the-Loop Platform for the Validation of a Small-Scale Wind System Control Strategy

The use of renewable energies contributes to the goal of mitigating climate change by 2030. One of the fastest-growing renewable energy sources in recent years is wind power. Large wind generation systems have drawbacks that can be minimized using small wind systems and DC microgrids (DC-µGs). A wind system requires a control system to function correctly in different regions of its operating range. However, real-time analysis of a physical wind system may not be feasible. An alternative to counteract this disadvantage is using real-time hardware in the loop (HIL) simulation. This article describes the implementation of an HIL platform in an NI myRIO 1900 to evaluate the performance of control algorithms in a small wind system (SWS) that serves as a distributed generator for a DC-µG. In the case of an SWS, its implementation implies nonlinear behaviors and, therefore, nonlinear equations, and this paper shows a way to do it by distributing the computational work, using a high-level description language, and achieving good accuracy and latency with a student-oriented development kit. The platform reproduces, with an integration time of 10 µs, the response of the SWS composed of a 3.5 kW turbine with a fixed blade pitch angle and no gear transmission, a permanent magnet synchronous generator (PMSG), and a three-phase full-bridge AC/DC electronic power converter. The platform accuracy was validated by comparing its results against a software simulation. The compared variables were the PMSG currents in dq directions, the turbine’s angular speed, and the DC bus’s voltage. These comparisons showed mean absolute errors of 0.04 A, 1.9 A, 0.7 rad/s, and 9.5 V, respectively. The platform proved useful for validating the control algorithm, exhibiting the expected results in comparison with a lab-scale prototype using the same well-known control strategy. Using a well-known control strategy provides a solid reference to validate the platform.

Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems.

Research on Multi‐Algorithm Switching Hybrid Control of Permanent Magnet Synchronous Motor for Electric Special Vehicles

AbstractIn order to improve the smoothness of the electric special vehicle (ESV) under each operational condition, a multi‐algorithm switching hybrid control (MASHC) algorithm is proposed for the permanent magnet synchronous motor (PMSM) of ESV. For a steady–state operation state, the transfer function structure of the motor control system is optimized, avoiding its output variables from varying with the integral and differential changes of the input quantity to enhance the stability and response speed of the control system. For transient operation states, it is considered to avoid the torque ripple that can be generated due to the increased computational effort of introducing an intelligent algorithm. The fuzzy proportional (FP) control algorithm that can replicate the transient performance of the fuzzy control algorithm is designed to improve the control accuracy of the control system. In addition, a switching function is designed to blend the two algorithms to improve the overall vehicle operation performance. To verify the effectiveness of the control algorithm, a simulation model and an experimental platform are developed. Both simulation and experimental results show that the proposed control algorithm has the advantages of no overshoot and oscillation in output, which can improve the smoothness of the ESV drive system. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

An improved algorithm for thermal compensation of synchrotron radiation optical mirrors based on Hessian matrix

The heaters-based thermal-compensated adaptive adjustment of a reflection mirror at Shanghai high repetition rate X-ray Free-Electron Laser and extreme light facility (SHINE) is presented here based on finite element analysis. The correction performance of different control algorithms [singular value decomposition and gradient descent (GD)] is analyzed and compared. This study has demonstrated that a significant control algorithm can further improve the surface shape accuracy of the mirror. After optimizing the mirror control algorithm, the calculated slope errors and height errors of the mirror are reduced to nearly less than 50 nrad rms and 0.5 nm rms, respectively. The optimization result indicates that the GD control algorithm based on the Hessian matrix exhibits superior performance and practicality compared to the control algorithm before optimization.

Implementation of dynamics inversion algorithms in active vibration control systems: Practical guidelines

It is common that excessive vibrations problems arise in modern slender, low-damping, lightweight structures such as footbridges or long-span floors subjected to human actions. The integration of inertial vibration controllers has been extensively used to mitigate the problems associated with vibration serviceability of such structures. Among them, the active vibration absorber constitutes the most sophisticated and effective alternative. Nonetheless, the inherent dynamics of the actuators used in the practical implementations of these systems may negatively affect their performance, compromising the overall system stability and performance. In this work, the practical application of dynamics inversion techniques to the force control of electrodynamic proof-mass actuators employed as active vibration absorption systems in lightweight structures subjected to human-induced vibrations is presented. The main idea behind the dynamics inversion approach is to find an approximate inverse of the actuator system which, upon implementation on a real-time controller, approximately cancels out actuator dynamics leading to an improved force tracking, with the subsequent enhancement in the performance of any force-based vibration control algorithm. The dynamics inversion has been applied to the classical direct velocity feedback approach, leading to a novel vibration control algorithm denominated velocity feedback with dynamics inversion. Additionally, the stability of the suggested method has been studied and practical guidelines, including a step-by-step design procedure, have been provided for researchers willing to apply the proposed algorithm. The goodness of the suggested method has been assessed by means of a tests campaign carried over an existing glass fiber reinforced polymer footbridge, which fulfills the static requirements at Serviceability and Ultimate Limit States, but exhibits excessive vibrations when its first resonance frequency is excited. Two types of tests have been performed: a walking load and a vandal bouncing excitation exerted at the midspan of the footbridge. In both cases, the experimental results show that the new procedure outperforms the classical velocity feedback approach thanks to the enhanced force tracking, leading to a remarkable reduction in the vibrations experimented by the structure under study.