Performance Of Control System Research Articles

Performance Assessment Of Two Active Absorption Systems In A Large Wave Flume

The quality of most coastal or ocean laboratory experiment depends on the accuracy, uniformity and stability of the generated waves. Generation of waves in the laboratory seems simple, but it should consider the spatio-temporal variability of the wave profile. As they propagate, waves interact with the boundaries, i.e. changes in water depth, lateral walls, test specimens, instrument supporting structures, passive absorbers, etc., as well as with varying hydrodynamic conditions (wave-induced currents, wave-wave interactions, or nonlinear energy transfer, among others). Reflected, scattered and radiated waves may propagate back to the wave generator and reflect back. Re-reflections from the wave generator may be a non-realistic representation of the intended wave field, to the extent that the total energy content in the testing facility may increase significantly and uncontrollably. Theoretically, for weakly nonlinear unidirectional waves propagating over a relatively small number of wavelengths, and as long as the still water depth and facility width does not change, the wave properties should remain constant in space and time, which can be identified as the uniformity and stability of the waves. Under these conditions, nonuniform or unsteady wave properties can be associated to an inadequate selection of the wave theory (Airy, Stokes n-order, Cnoidal, Stream Function, etc., e.g. Fenton 1999), the approximation of the wave generation technique (first or second order, nonlinear - see e.g. Mohtat et al., 2020), formation of wave instabilities (e.g. Benjamin-Feir, 1967), wave reflection, scattering or radiation, and the performance of the active wave absorption system. It can be said that the performance of the active wave absorption control system is one of the responsible factors that can affect directly the quality of the generated waves. Many laboratories have implemented different passive and active wave absorption systems, but the performance on the uniformity and stability of the generated waves, as well as the performance of the active wave absorption system used, have not been assessed in a systematic way, and related published data and information is quite scarce, with few interesting exceptions, e.g. (Lykke-Andersen, 2016; Spinneken 2010; Schäffer and Skourup 1996). However, a direct comparison among the different available active wave absorption control systems is still missing. This paper presents a thorough experimental study on the performance assessment of two different active wave absorption control systems (MTS and Awasys7) as a means to establish the quality of the experiment in terms of the uniformity (space) and stability (time) of generated wave parameters (descriptors) in the laboratory.

Adaptive reinforcement learning-based control using proximal policy optimization and slime mould algorithm with experimental tower crane system validation

This paper presents a novel optimal reference tracking control approach resulted from the combination of a popular policy gradient Reinforcement Learning (RL) algorithm, namely Proximal Policy Optimization (PPO), and a metaheuristic Slime Mould Algorithm (SMA). One of the most important parameters in the PPO-based RL process is the learning rate, which has a big impact on how the parameters of the actor neural network (NN) are iteratively updated. In every episode of the RL process, the weights and the biases of the actor NN are multiplied with the learning rate, determining how much the learning agent will step into a certain direction computed based on previous experiences. The classical PPO algorithm usually relies on fixed values for the learning rates which rarely change, or not at all, during the learning process. However, its main drawback is that the learning agent cannot take advantage of positive momentum in the learning process by accelerating towards good learning experiences or slow down and quickly change the direction in the case of consecutive negative learning experiences. The main objective of the combination proposed in this paper is to create an adaptive SMA-based PPO approach applied to control systems, which instead of using fixed learning rate values, it uses the SMA to compute optimal values of the learning rates in each time step of the learning process based on the progress of the learning agent. This paper investigates if the adaptive SMA-based PPO control approach can be considered as an alternative to the classical PPO version, which employs fixed values of the learning rate. A comparison is carried out using control system performance indices gathered while performing an optimal reference tracking control task on tower crane system laboratory equipment.

Gas rudder 2DOF FOPID position control based on CSFLA optimisation

Aiming at the problems of parameter variety, rigid-flexible coupling and external unknown factors interference in the process of spacecraft propulsion, a two-degree-of-freedom fractional-order PID (2DOF FOPID) controller based on a coevolutionary shuffled frog leaping algorithm (CSFLA) is designed for the high-precision control of the gas rudder servo control system. The anti-interference and target tracking capabilities of 2DOF PID is combined with the advantage of FOPID, which is insensitive to the parameters of the controlled object, to improve the performance of the control system as a whole. CSFLA is formed by integrating the self-learning evolution of elite populations and the interactive learning strategy among populations into the SFLA to enhance the global searching ability and searching accuracy of SFLA, and CSFLA is used to optimize the parameters of the 2DOF FOPID controller. The simulation results show that the designed CSFLA-2DOF FOPID controller has the advantages of fast response speed, small overshoot, strong tracking performance and strong anti-disturbance ability, which improves the dynamic and static performance and anti-disturbance ability of the gas rudder servo control system.

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Performance-optimisation-based repetitive trajectory tracking control for autonomous farming vehicle

The high precision trajectory tracking for the autonomous farming vehicle (AFV) is closely related to work quality and crop yield. Farmland operations are usually repetitive tillages, and the farmland soil is relatively soft, slippery, and uneven, which can easily lead to uncertain problems such as slippage, parameter perturbation, and external disturbance. This paper proposes a finite-time repetitive trajectory tracking control strategy integrated with particle swarm optimisation (PSO) and equivalent input disturbance (EID) for the AFV to achieve performance optimisation. The repetitive control framework with finite-time convergence technique can effectively realise the iterative convergence performance of the repetitive trajectory tracking. The PSO algorithm is integrated into determining the control gains to optimise the dynamic and steady-state performances of the trajectory tracking control system. The EID method enhances the tracking precision and robustness to internal and external disturbances. Under MATLAB/Simulink and CarSim co-simulation environment, the effectiveness and advantages of the designed control strategy are illustrated by comparing it with the traditional repetitive control, the EID-based PI control, the active-disturbance-rejection-control-based nonsingular terminal sliding mode control, as well as the sliding-mode-observer-based integral sliding mode control strategies for a farming vehicle.

A Walking Trajectory Tracking Control Based on Uncertainties Estimation for a Drilling Robot for Rockburst Prevention

A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode chattering in the designed sliding mode controller. Additionally, a walking trajectory tracking control strategy is proposed to improve the walking trajectory tracking performance in terms of response time, tracking precision, and robustness, including walking longitudinal and lateral trajectory tracking controllers. Finally, simulation and experimental results are employed to verify the trajectory tracking performance and observability of the model uncertainties. The results testify that the proposed approach is better than other comparative methods, and the longitudinal and lateral trajectory tracking average absolute errors are controlled in 10.23 mm and 22.34 mm, respectively, thereby improving the walking trajectory tracking performance of the walking electrohydraulic control system for the coal mine drilling robot for rockburst prevention.

Enhanced Whale Optimization Algorithm for Fuzzy Proportional–Integral–Derivative Control Optimization in Unmanned Aerial Vehicles

The traditional PID controller in quadrotor UAVs has poor performance, a large overshoot, and a long adjustment time, which limit its stability and accuracy in practical applications. In order to solve this problem, an improved whale optimization fuzzy PID control strategy based on CRICLE chaos map initialization is proposed, and a detailed simulation analysis was carried out using MATLAB software (MATLAB R2022B). Firstly, to more realistically reflect quadrotor UAVs’ flight behavior, a dynamic simulation model was established, and the dynamics and kinematic characteristics of the aircraft were considered. Then, CRICLE chaotic mapping initialization was introduced to improve the global search ability of the whale optimization algorithm and to effectively initialize the parameters of the fuzzy PID controller. This improved initialization method helped to speed up the convergence process and improve the stability of the control system. In the simulation experiments, we compared the performance indicators of the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID controller to the traditional PID and fuzzy PID controllers, including overshoot, adjustment time, etc. The results show that the proposed control strategy has better performance than the traditional PID and fuzzy PID controllers, significantly reduces overshoot, and achieves a significant improvement in adjustment time. Therefore, the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID control strategy proposed in this study provides an effective solution for improving the performance of the quadrotor control system and has practical application potential.

Research on UAV flight attitude control system based on simulink and genetic algorithm

With the evolution of networking and information technology, unmanned aerial vehicles (UAVs) have emerged as integral components in numerous fields, executing diverse and vital tasks. The accuracy and efficiency of UAV operations are greatly influenced by their attitude control systems, which dictate flight stability, speed, and functionality. However, traditional control methods for UAV flight stability and accuracy have become outdated, unable to meet the demands of modern applications. To address these challenges, this study introduces a novel attitude control approach based on genetic algorithms. This method utilizes the principles of natural selection and genetic traits to optimize the parameters of the attitude control system. The objective is to enhance the current control effectiveness, improve the UAV's maneuverability, and enable its versatile applications across multiple fields. Simulink tool simulations are conducted to evaluate the stability of the optimized results. This simulation tool allows for the replication of real-world flight conditions and the assessment of the attitude control system's performance under various scenarios. The results of these simulations provide valuable insights into the effectiveness of the genetic algorithm-based attitude control method. This study contributes to the advancement of UAV technology by improving the accuracy and stability of attitude control systems. It paves the way for more efficient and reliable UAV operations in various fields, from environmental monitoring to search-and-rescue missions, thus enhancing their operational capabilities and expanding their potential applications.

Neural Network-Based Distributed Consensus Tracking Control for Nonlinear Multi-Agent Systems with Mismatched and Matched Disturbances

In practice, disturbances, including model uncertainties and unknown external disturbances, are always widely present and have a significant impact on the cooperative control performance of a networked multi-agent system. In this work, the distributed consensus tracking control problem for a class of multi-agent systems subject to matched and mismatched uncertainties is addressed. In particular, the dynamics of the leader agent are modeled with uncertain terms, i.e., the leader’s higher-order information, such as velocity and acceleration, is unknown to all followers. To solve this problem, a robust consensus tracking control scheme that combines a neural network-based distributed observer, a barrier function-based disturbance observer, and a tracking controller based on the back-stepping method was developed in this study. Firstly, a neural network-based distributed observer is designed, which is able to achieve effective estimation of leader information by all followers. Secondly, a tracking controller was designed utilizing the back-stepping technique, and the boundedness of the closed-loop error system was proved using the Lyapunov-like theorem, which enables the followers to effectively track the leader’s trajectory. Meanwhile, a barrier function-based disturbance observer is proposed, which achieves the effective estimation of matched and mismatched uncertainties of followers. Finally, the effectiveness of the robust consensus tracking control method designed in this study was verified through numerical simulations.

Variable Universe Fuzzy-Proportional-Integral-Differential-Based Braking Force Control of Electro-Mechanical Brakes for Mine Underground Electric Trackless Rubber-Tired Vehicles.

Currently, the main solution for braking systems for underground electric trackless rubber-tired vehicles (UETRVs) is traditional hydraulic braking systems, which have the disadvantages of hydraulic pressure crawling, the risk of oil leakage and a high maintenance cost. An electro-mechanical-braking (EMB) system, as a type of novel brake-by-wire (BBW) system, can eliminate the above shortcomings and play a significant role in enhancing the intelligence level of the braking system in order to meet the motion control requirements of unmanned UETRVs. Among these requirements, the accurate control of clamping force is a key technology in controlling performance and the practical implementation of EMB systems. In order to achieve an adaptive clamping force control performance of an EMB system, an optimized fuzzy proportional-integral-differential (PID) controller is proposed, where the improved fuzzy algorithm is utilized to adaptively adjust the gain parameters of classic PID. In order to compensate for the deficiency of single-close-loop control and adjusting the brake gap automatically, a cascaded three-closed-loop control architecture with force/position switch technology is established, where a contact point detection method utilizing motor rotor angle displacement is proposed via experiments. The results of the simulation and experiments indicate that the clamping force response of the proposed multi-close-loop Variable Universe Fuzzy-PID (VUF-PID) controller is faster than the multi-closed-loop Fuzzy-PID and cascaded three-close-loop PID controllers. In addition, the chattering of braking force can be suppressed by 17%. This EMB system may rapidly and automatically finish the operation of the overall braking process, including gap elimination, clamping force tracking and gap recovery, which can obviously enhance the precision of the longitudinal motion control of UETRVs. It can thus serve as a BBW actuator of mine autonomous driving electric vehicles, especially in the stage of braking control.

Experimental and modeling investigation on dynamic response of sticky control valves

The dynamic response of control valves directly affects the safe and efficient operation of industrial control loops. Valve stiction is a common and persistent issue that causes oscillations in control loops. The stiction behavior of valves has received widespread attention from researchers worldwide in the past two decades. The developed valve stiction models have been widely applied in the detection, quantification, and compensation research of sticky control valves. However, how to more accurately characterize stiction behavior still requires efforts. Most data-driven models do not consider the effects of dynamic response on the stiction behavior. In this paper, the inconsistency of the representative stiction models is discussed during the unidirectional motion of the valve stem, and potential improvements are revealed. An experimental device for valve stiction has been designed. This device can replicate valve stiction caused by tight packing, and measure valve position and friction through smart positioner and force sensor. The second-order dynamic system for the sticky valve is constructed by the physical model, and the response time of the valve is calculated and verified combined with the experiment. The effects of sampling interval on stiction behavior are discussed. On this basis, an improved valve stiction model considering the dynamic response of the valve is proposed, which supplements the situation of stiction during the unidirectional motion of the valve stem. The proposed model can be applied in control systems with fixed sampling intervals that involve sticky valves. It can also be extended to control systems with variable sampling intervals. This work contributes to evaluating the performance of control systems with sticky valves and providing reference value for the detection, quantification, and compensation of valve stiction.

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Inertia Energy-Based Required Capacity Calculation of BESS for Achieving Carbon Neutrality in Korean Power System

Frequency response performance in power systems is becoming vulnerable due to the transition toward the higher penetration of renewable energy such as achieving carbon neutrality. In particular, reducing power system inertia energy as the asynchronous generation increases could result in violating the frequency stability constraint when a disturbance occurs in the power systems. In order to control the rapidly fluctuating frequency of the power system with low inertia, it is necessary to introduce fast frequency response resources such as a Battery Energy Storage System (BESS). This paper proposes a method to calculate the required capacity of BESS for compensating the frequency control performance of the power system using inertia energy. For calculating the required capacity of BESS, the inertia energy in the critical power system, where frequency control performance marginally satisfies frequency stability constraint, should be calculated. Also, the inertia energy in the evaluated power system having deficit inertia energy should be calculated. By comparing power systems that respond with different dynamics when the same disturbance occurs, the proposed calculation corresponds to the ratio of inertia energy deficiency based on critical power system inertia energy within the power imbalance. Through various case studies employing Korean power systems, the effectiveness of the inertia energy-based calculation method for the required BESS is verified by the fact that the BESS integrated power system marginally satisfies the frequency stability constraint. In these study cases, it is found that the instant response of BESS is very effective for compensating the frequency control performance of the low inertia power system. By applying the proposed method, it is also found that about 840 MW of BESS can achieve carbon neutrality in the Korean power system.

Propelling precision of longitudinal vibration mitigation in ship propeller shafts through advanced nonlinear intelligent semi-active control leveraging adaptive neuro-fuzzy inference system with linear quadratic regulator

This study presents a pioneering method to address longitudinal vibrations in ship propeller shafts by integrating an intelligent semi-active control system. By leveraging Adaptive Neuro-Fuzzy Inference System (ANFIS) and Linear Quadratic Regulator (LQR) techniques, the proposed system offers a novel solution to a longstanding challenge in maritime engineering. The efficacy of the semi-active control system is assessed through two complementary analyses: frequency domain analyses and Percentage Reduction Index (PRI) values. The investigation reveals substantial PRI values, ranging from 24.68% to 46.87%, indicating notable enhancements in vibration control with the adoption of semi-active technology. Furthermore, frequency domain analyses demonstrate significant advantages, with a peak reduction of 68.21% observed at 750 r/min. To rigorously evaluate the statistical significance of these improvements, paired t-tests were conducted between the semi-active control system and passive strategies. The results confirm a statistically significant difference in vibration reduction ( p < .05) between the two systems. Specifically, for Percentage Reduction Index Values, the t test yielded a p-value of .023, while for Frequency Domain Analyses, the t-statistic significantly exceeded the critical value (t ≈ 8.09, tcritical ≈ 2.571). Both data sets provide compelling evidence to reject the null hypothesis, indicating a significant difference in vibration reduction between the semi-active control system and passive strategies. The PRI values consistently demonstrate superior performance for the semi-active control system, highlighting its effectiveness in mitigating vibrations. These findings validate the effective ness of the system. These results show the practical implications of implementing semi-active control systems in maritime engineering, offering enhanced safety, fuel efficiency, and operational reliability in ship propeller shaft applications.

Hybrid Nonlinear Passivity-Based Control Approach to Magnetic-Impulsive Spacecraft Attitude Regulation

This paper proposes two novel passivity-based control design frameworks for hybrid nonlinear time-varying dynamical systems involving an interacting amalgam of continuous-time and discrete-time dynamics whose dynamical properties evolve periodically over time. In this regard, the hybrid Kalman–Yakubovich–Popov conditions are employed in tandem with the passivity theorem to construct a three-step control design algorithm to render closed-loop dynamics stable. The proposed control architectures are subsequently exploited to regulate the attitude motion of spacecraft with magnetic and impulsive modes of operation. Practical considerations involved in implementing the proposed hybrid algorithms are then discussed in detail. Simulation results show significant improvement in the performance of the attitude control system in terms of system response, robustness, and the required magnetic and impulsive control usage as compared to a hybrid linear approach.

Reinforcement learning-based adaptive tracking control for mismatched non-affine nonlinear systems with coupled uncertainties

This paper studies a reinforcement learning-based adaptive non-affine tracking control method for a class of uncertain mismatched non-affine nonlinear systems. The considered system is not only affected by external mismatched disturbances and internal uncertainties, but also influenced by the non-affine control structures. Firstly, an auxiliary integral system is developed for the purpose of isolating the non-affine control input. Secondly, by designing the actor-critic networks to evaluate the system control performance and generate the reinforcement signal, the unknown internal uncertainties can be handled. Thirdly, based on the output of reinforcement learning network, several disturbance compensation laws are constructed to address the adverse impact of matched and mismatched disturbances. As a result, a novel intelligent adaptive non-affine controller is proposed by integrating actor-critic reinforcement learning framework, disturbance compensation and adaptive laws. It has been proved that closed-loop system are stable and the tracking errors are bounded. The numerical simulation results show the effectiveness and superiority of the proposed method.

Design and Experiment of Plate Taking Control System of Edible Sunflower (Edulis helianthus catino L.) Harvester

This study aims at the problems of high labor intensity, high cost and high loss rate of mechanical picking of seeds, low mechanization level and difficulty to guarantee the quality of picking plates in the process of picking edible sunflower. Based on the principle of manual plate taking, a plate taking control system for a sunflower harvesting table was designed. The principle of taking the plate of the edible sunflower harvesting table was analyzed. According to the actual operation requirements, the overall scheme of the sunflower plate control system is determined, and the control strategy of imitating artificial low-loss harvesting is designed. To reduce the grain loss in the process of taking the plate and improve the control accuracy of the system on the movement speed and displacement of the key components in the process of taking the plate, a trapezoidal acceleration and deceleration control algorithm is designed as the control algorithm of taking the plate. The working performance of the plate control system was verified with the absolute error, relative error and total loss rate of the harvest as objectives. Bench and field verification experiments were both carried out. The bench experiment showed that the speed error of the plate parts was not more than 0.028 m/s. In the bench experiments of the device, the maximum positioning error was 1.25 mm, the average relative error was only 0.94% and the grain loss rate was not more than 2.26%. Its result showed that the system algorithm was reliable, the positioning accuracy was high and the plate taking operation can be completed well. The field verification experiment showed that the forward speed of the unit was in the range of 0.4~0.8 m/s, and the total loss rate of harvest was less than 5%. When the forward speed is 0.6 m/s, the minimum harvest loss rate is 2.32%, which indicated the control system meets the requirements of sunflower harvesting operation.

Fuzzy-based adaptive control method for automatic mooring systems

An automatic mooring system is not only safer and more economical than a traditional mooring system with ropes but also significantly reduces the mooring time. In this paper, we propose an automatic mooring system designed to securely berth vessels entering a port, along with an efficient control method aimed at constraining vessel movement within a specific range. To assess the operational range and control performance of the proposed system in a marine environment, a dynamic analysis program was employed to create a simulator. The simulator incorporated a parallel structured Stewart platform to replicate the 6-DOF motion of a marine vessel. The proposed automatic mooring system controller regulates the position using a time-delay controller (TDC). TDC has proven to be excellent for effectively predicting system uncertainty and handling changes in system dynamics. However, given the unpredictable nature of extreme disturbances in marine environments, it is essential to adaptively modify the control parameters to ensure optimal performance. The method presented in this paper employs a fuzzy logic controller (FLC) to dynamically adjust the control parameters of the TDC, enabling effective adaptation to rapidly changing marine conditions, including waves and wind.

Experimental study of smoke characteristics in a double-deck tunnel with multiple point extraction using a branch pipe

A reduced scale 1:8 double-deck tunnel model was built to study smoke control performance of a semi-transverse ventilation system using a branch pipe to connect the lower deck tunnel to the upper smoke duct. 41 experiments were conducted to analyze the effects of different activation modes of smoke vents, heat release rate (HRR), and exhaust smoke rates on smoke spread distance. An optimum smoke control mode was found by activating two vents near the fire source when the heat release rate was 44.19 kW (8 MW at full scale) and the fan's extraction rate was 0.66 m3/s (120 m3/s at full scale) using a branch pipe. Results showed that as HRRs rise, the smoke spread distance first increases and then remains constant. The ratio of the smoke spreading distance on the upstream side of the fire to the total smoke spreading distance was 0.4 in this experimental setup. Furthermore, fire smoke spread characteristics were quantified for a typical ventilation design.

Environmental management control system, blockchain adoption, cleaner production, and product efficiency on environmental reputation and performance: Empirical evidence from Indonesia

This research investigates the intricate relationships between Environmental Management Control Systems (EMCS), Blockchain Adoption (BCHA), Cleaner Production (CLPR), Product Efficiency (PROD), Environmental Reputation (ENRE), and Environmental Performance (ENPE) within organizational contexts in Indonesia. The study aims to shed light on the role of technology adoption, sustainability practices, and reputation management in shaping environmental outcomes. Methodologically, the research employs a quantitative approach, utilizing survey data from diverse organizations. Structural equation modeling (SEM) analyzes the data and tests the hypothesized relationships. The findings reveal significant positive relationships between EMCS and BCHA, EMCS and CLPR, EMCS and PROD, BCHA and CLPR, BCHA and PROD, and BCHA and ENRE. Cleaner Production demonstrates a substantial positive impact on both ENRE and ENPE. Product Efficiency influences ENRE positively. However, the direct influence of PROD on ENPE is found to be inconclusive. The study contributes to understanding sustainability dynamics by highlighting the pivotal roles of EMCS, BCHA, CLPR, and PROD in driving environmental reputation and performance within organizations. Furthermore, it underscores the significance of aligning perceived reputation with tangible environmental commitment. Limitations include potential data constraints and the challenge of establishing causality due to the study's correlational nature. Future research is encouraged to explore diverse contexts, conduct in-depth case studies, and investigate moderating variables. This research offers novel insights into the complex interplay between technology adoption, sustainability practices, reputation management, and environmental outcomes, providing valuable guidance for organizations striving to navigate the sustainability landscape in an era of heightened environmental awareness.