Classical Control Methods Research Articles

An efficient control scheme for operational performance enhancement of vehicular fuel cell integrated power system

Recent advancements in fuel cell (FC) technology have positioned it as a promising alternative energy source across various stationary, mobile, and transportation applications. As fuel cell vehicles (FCVs) become increasingly prominent in the transportation sector, they also offer the potential to function as supplementary stationary energy providers when parked, thereby contributing to the grid. In this study, a control method to enhance the energy transfer capability of an FCV-integrated grid system is proposed. To manage energy transfer between grid and FCVs, classical strategies using fixed parameter controllers suffer from performance degradation due to the nonlinear and external parameter-dependent nature of the FC stacks. The proposed adaptive fractional-order proportional-integral strategy bears the advantage of self-tuning parameters feature for designing the control parameters of power conditioning unit. Using fractional-order control endows the system with memory and heredity, enhancing its ability to handle nonlinearity and uncertainty. Through case studies, it is demonstrated that the proposed strategy reduces vehicular FC output power variations by over 60 % while enhancing transient response by 50 % compared to classical control methods. Thus, the limitations such as low control flexibility, slow transient response, and high undershoot/overshoot rates addressed by the classical controllers are countered thanks to the developed strategy.

Analysis of Optimization Algorithms Used in Permanent Magnet Synchronous Motor Control According to Different Performance Indices

Permanent magnet synchronous motors (PMSM) can be produced at lower costs with new developments in magnet technology and are widely used in many industrial areas due to their low energy consumption. The widespread use of PMSM brings with it the requirement for high accuracy control performance. In order to achieve high performance accuracy, vector control technique is generally preferred. However, the parameter values of the controllers used in this technique are very important for motor performance. Tuning these parameters by optimization techniques instead of classical methods has become a popular topic. Nowadays, modern control methods show more effective control behavior than classical control methods, which has made the use of modern control methods widespread and studies on modern control methods have intensified. In this study, the analysis of different optimization algorithms used in the control of an PMSM according to their performance indices is investigated in Matlab-Simulink environment. As a result of simulation with different optimization algorithms under the same conditions, the analysis of optimization algorithms using different performance indices such as integral of the absolute value of the error (IAE), integral of the square of the error (ISE), Integral of the Square of the Error Multiplied by Time (ITSE), and Integral of the Absolute Error Multiplied by Time (ITAE) is carried out.

Data‐driven cooperative adaptive cruise control for unknown nonlinear vehicle platoons

AbstractThis article studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature. A unified data‐driven CACC design is proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and human‐driven vehicles (HVs). The CACC leverages online‐collected sufficient data samples of vehicle accelerations, spacing, and relative velocities. The data‐driven control design is formulated as a semidefinite program that can be solved efficiently using off‐the‐shelf solvers. Efficacy of the proposed CACC are demonstrated on a platoon of pure AVs and mixed platoons with different penetration rates of HVs using a representative aggressive driving profile. Advantage of the proposed design is also shown through a comparison with the classic adaptive cruise control (ACC) method.

Research on Active Disturbance Rejection Control of Small Modular Reactor Once-Through Steam Generator Based on Differential Evolution Optimization Algorithm

The pressure control of once-through steam generator (OTSG) is critical for the operation and safety of small modular reactors. However, the dynamics of the OTSG are quite nonlinear, time varying, and uncertain. Classical control methods face significant challenges in keeping the pressure within acceptable limits in an environment with frequent load variations and multiple sources of external disturbances. In this paper, a robust control strategy based on active disturbance rejection control (ADRC) optimized by the differential evolution (DE) algorithm is proposed to satisfy the requirements of steam pressure control in an optimum and efficient way. First, a lumped parameter model for the OTSG is developed based on the notion of moving boundaries and linearized to introduce a transfer function model for control design purposes. Then a feedforward cascade control system based on an ADRC controller and a proportional integral differential (PID) controller is designed, which mainly consists of a pressure ADRC controller, a feedwater PID controller, and a feedforward compensator. To improve the pressure control performance and parameter tuning efficiency of the OTSG control system, a DE algorithm is applied to optimize the ADRC parameters, and the frequency domain and time domain characteristics are compared with particle swarm optimization and the genetic algorithm. Transient simulation experiments were used to evaluate the control performance at 100%, 50%, and 25% power levels, respectively. Moreover, a performance robustness criterion is proposed to demonstrate the robust stability of the ADRC, and the robustness metric is compared with that of the PID control schemes. The simulation results show that DE-ADRC control strategy has better set point tracking, interference rejection, and robust stability than DE-PID control strategy.

Controlling a Single Tank Liquid Level System with Classical Control Methods and Reinforcement Learning Methods

In this study, the control of the single tank liquid level system used in control systems has been carried out. The control of the single tank liquid level system has been performed with the classic PI, modified PI, state feedback with integrator action, and Q learning algorithm and SARSA algorithms, one of the artificial intelligence methods. The tank system to be modelled was carried out using classical physics, namely Newton's laws. Then, the mathematical model obtained of the system that are continuous model in time is acquired. The originality of the study; the non-linear liquid tank system is controlled by classical controllers and reinforcement methods. For this purpose, the system was firstly designed to model the system, then the system has been linearized at a specific point in order to design classic PI, modified PI, and state feedback with integral. After that, agents of the Q Learning algorithm and SARSA algorithms were trained for the system. Then the agents have controlled the single-level tank system. The results of the classic controllers and supervised controllers are contrasted with regard to performance criteria such as rising time, settling time, overshoot and integral square error. Consequently, Q learning method has produced 0.0804-sec rising time, 0.943 sec settling time and 0.574 integral square errors. So, Q learning algorithm has produced and exhibited more thriving and successful results for controlling single liquid tank system than PI, Modified PI, state feedback controllers and SARSA.

Suppression of wake-induced galloping of tandem cylinders by helical strakes

Helical strakes as a classic passive control method for vortex-induced vibration (VIV), have been extensively studied, but wake-induced galloping (WIG) control of three tandem cylinders has received no previous attention. The current wind tunnel experimental study tries to fill this gap in the context of mechanical science. WIG of three tandem cylinders considering different structures of helical strakes is modeled and investigated. Helical strakes with varying heights G, pitches P, and cross-sectional shapes (square “S”, involute “I”, and D-shaped “D”) are first designed. Wind tunnel tests are conducted to experimentally study the dynamics responses of tandem cylinders (m*ξ = 0.09) at different spacings, and three vibration modes are obtained. This study delves into the characterization and quantification of the impact of various parameters of helical strakes on WIG of three tandem cylinders. These aspects, scarcely addressed in existing research, are thoroughly explored through statistical analysis, focusing on vibration amplitude, vibration frequency, and wavelet analysis. It is found that helical strakes can effectively suppress the multi-frequency vibration instability of bare cylinders. For wind speeds below 6 m/s, the "S" helical strakes achieves a vibration suppression efficiency of up to 665 %; Three-dimensional computational fluid dynamics (3D-CFD) demonstrate the coupling effect in the flow field, revealing that the "S" helical strakes has a greater impact on boundary layer formation than the "D" helical strakes; Reducing the pitch of the helical strakes can enhance the disturbance ability of the spanwise position, thereby increasing its suppression effect. This work improves vibration reduction and sheds light on how the helical strakes' cross-section controls vibrations in the tandem cylinders.

Safe Reinforcement Learning-Based Balance Control for Multi-Cylinder Hydraulic Press

Abstract Considering the load uncertainty and unmodeled dynamics in multicylinder hydraulic systems, this paper proposes a balance control algorithm based on safe reinforcement learning to release the restrictions of classical model-based control methods that depend on fixed gain. In this paper, the hydraulic press is controlled by a trained agent that directly maps the system states to control commands in an end-to-end manner. By introducing an action modifier into the algorithm, the system states are kept within security constraints from the beginning of training, making safe exploration possible. Furthermore, a normalized exponential reward function has been proposed. Compared with a quadratic reward function, the precision is greatly improved under the same training steps. The experiment shows that our algorithm can achieve high precision and fast balance for multicylinder hydraulic presses while being highly robust. To the best of our knowledge, this research is the first to attempt the application of a reinforcement learning algorithm to multi-execution units of hydraulic systems.

A zero-net-mass-flux wake stabilization method for blunt bodies via global linear instability

A rectangular cylinder, with an aspect ratio of 5, is a widely used bluff body in engineering practice. It undergoes intricate dynamical behavior in response to minute alterations in the flow angle of attack (α). These modifications invariably precipitate the failure of wake control for classical flow control methods with various α values. In this study, global linear instability, adjoint method, and sensitivity analysis are employed to identify the optimal position for flow control. It is found that the sensitive region gradually transitions from the leeward side to the downwind side of the model as α and Reynolds number (Re) increase. So, we set up airflow orifices for flow control in both positions. Jet flow control on the leeward side effectively inhibits vortex shedding (α ≤ 2°). High-order dynamic mode decomposition is employed to reveal the inherent mechanism of control. Suction control on the downside effectively mitigates the shear layer separation phenomenon induced by the altered spatial structure associated with higher α. A novel zero-net-mass-flux wake control, bionics-based breathe-valve control (BVC), is proposed to optimize the control effect. BVC is applicable for various α and Re, with optimal effectiveness achievable through jet velocity adjustments. The prediction-control approach in this investigation provides a targeted method to mitigate flow-induced vibration.

Study of Q-learning and deep Q-network learning control for a rotary inverted pendulum system

The rotary inverted pendulum system (RIPS) is an underactuated mechanical system with highly nonlinear dynamics and it is difficult to control a RIPS using the classic control models. In the last few years, reinforcement learning (RL) has become a popular nonlinear control method. RL has a powerful potential to control systems with high non-linearity and complex dynamics, such as RIPS. Nevertheless, RL control for RIPS has not been well studied and there is limited research on the development and evaluation of this control method. In this paper, RL control algorithms are developed for the swing-up and stabilization control of a single-link rotary inverted pendulum (SLRIP) and compared with classic control methods such as PID and LQR. A physical model of the SLRIP system is created using the MATLAB/Simscape Toolbox, the model is used as a dynamic simulation in MATLAB/Simulink to train the RL agents. An agent trainer system with Q-learning (QL) and deep Q-network learning (DQNL) is proposed for the data training. Furthermore, agent actions are actuating the horizontal arm of the system and states are the angles and velocities of the pendulum and the horizontal arm. The reward is computed according to the angles of the pendulum and horizontal arm. The reward is zero when the pendulum attends the upright position. The RL algorithms are used without a deep understanding of the classical controllers and are used to implement the agent. Finally, the outcome indicates the effectiveness of the QL and DQNL algorithms compared to the conventional PID and LQR controllers.

Fuzzy system of automatic tuning of electromagnetic excitation system of vibration-frequency liquid density meter

A fuzzy system for setting the location of electromagnetic exciter of vibration-frequency density meter is presented, taking into account the possible nonlinearities of the object. This paper proposes a system and algorithm for tuning the location of electromagnetic exciter of vibration-frequency density meter using fuzzy set theory. The use of fuzzy control methods allowed us to obtain a system for tuning the location of the electromagnetic exciter of the vibration-frequency density meter without the use of cumbersome computational procedures characteristic of the classical control method.

Research on PID Control of Quadrotor Drones Based on MATLAB

Four-rotor UAV is a very practical and widely used UAV. In this paper, the development status and future trend of Fourrotor UAV at home and abroad are introduced, and then the transformation matrix from ground coordinates to airframe coordinates is deduced by using Euler equation. In the process of dynamic modeling, the plane position reference coordinate system and rotation angle reference coordinate system are selected to analyze the external forces and moments on the body, and the linear motion equation and the angular motion equation are written in parallel. On the basis of UAV dynamics model, the classical PID control method is used to control the attitude of the inner loop and the position of outer loop . According to simulation results, the flight controller can keep the UAV flying stably.

Neural Network Model-Based Reinforcement Learning Control for AUV 3-D Path Following

Autonomous underwater vehicles (AUVs) have become important tools in the ocean exploration and have drawn considerable attention. Precise control for AUVs is the prerequisite to effectively execute underwater tasks. However, the classical control methods such as model predictive control (MPC) rely heavily on the dynamics model of the controlled system which is difficult to obtain for AUVs. To address this issue, a new reinforcement learning (RL) framework for AUV path-following control is proposed in this paper. Specifically, we propose a novel actor-model-critic (AMC) architecture integrating a neural network model with the traditional actor-critic architecture. The neural network model is designed to learn the state transition function to explore the spatio-temporal change patterns of the AUV as well as the surrounding environment. Based on the AMC architecture, a RL-based controller agent named ModelPPO is constructed to control the AUV. With the required sailing speed achieved by a traditional proportional-integral (PI) controller, ModelPPO can control the rudder and elevator fins so that the AUV follows the desired path. Finally, a simulation platform is built to evaluate the performance of the proposed method that is compared with MPC and other RL-based methods. The obtained results demonstrate that the proposed method can achieve better performance than other methods, which demonstrate the great potential of the advanced artificial intelligence methods in solving the traditional motion control problems for intelligent vehicles.

Landing control of a returned spacecraft under conditions of uncertainty

The nonlinear model of a spacecraft requires high robustness, and the classical control method, as one of the earliest developed control methods, has simple algorithms and high robustness. A control system structure that ensures retention attack angle and overload by combining fuzzy control with classical control for adaptive optimization and adjustment of control parameters and tracking of the attack angle step signals is proposed. The simulation results show that the performance of the improved system is better than that of the original system. Improved algorithms are used to simulate the attack angle and overload retention sections, also simulated aerodynamic uncertainties is carried out. The results show that the developed control law meets the requirements of a certain level of robustness. Keywords aerospace vehicles, position control, adaptive fuzzy control, PID adjusting, uncertainty

Combatting Malaysia's Dengue Outbreaks with Auto-Dissemination Mosquito Traps: A Hybrid Stochastic-Deterministic SIR Model

Classical mosquito control methods (e.g. chemical fogging) struggle to sustain long-term reductions in mosquito populations to combat vector-borne diseases like dengue. The Mosquito Home System (MHS) is an auto-dissemination mosquito trap, that kills mosquito larvae before they hatch into adult mosquitoes. A novel hybrid stochastic-deterministic model is presented, that successfully predicts the effect of deploying MHSs within high-rise flats in Selangor, Malaysia. Stochastic SIR (Susceptible-Infected-Recovered) equations (flats) are paired with an existing deterministic SIR model (wider Kuala Lumpur population). Model predictions provide excellent agreement with data from a 44 week MHS trial within the flats. The stochastic model is validated as a powerful tool for predicting short- and long-term impacts of deploying this style of trap within similar environments. Significant, sustainable reductions in mosquito populations are predicted when the MHS is active: with a mean of 9 (95% Uncertainty Range (UR): 1; 30) during the 44 week trial period, compared to 35 (95% UR: 1; 234) dengue cases with no MHSs. Long-term predictions for endemic equilibrium show MHSs significantly narrow the mosquito population distribution and reduce dengue prevalence: from a mean of 5 (95% UR: 0; 52) (no MHS), to 1 (95% UR: 0; 8) dengue cases annually (with MHS).

Nonlinear flap for passive flutter control of bidimensional wing

A solution for passive aeroelastic control is presented and tested experimentally on a bidimensional wing setup. The solution consists of a flap integrated in the wing which acts as a secondary mass damper and absorbs energy when aeroelastic instability is encountered. This device is passive, which makes it safe in emergency cases and adds little mass to the system in the fundamental case presented in this paper. If installed on actual aircraft wings, the flap-NES would not substitute but coexist with classic active control methods, adding virtually zero mass to the existing system. Using a flap placed in the airflow enables the control system to benefit from aerodynamic damping with a behavior dependent on wind speed. The device can either have a linear or nonlinear stiffness. It is shown that both options absorb energy from the main system, that is, the wing. The flap acts as a TMD (tuned mass damper) in the linear case and as an NES (nonlinear energy sink) in the nonlinear case. The nonlinear solution not only absorbs more energy at given wind speed but it also is a more suited solution as the damper follows the wind speed dependent frequencies due to its nonlinear feature. In this work, the flap-TMD and the flap-NES are tested experimentally on a bidimensional wing which presents classic flutter.

Research on roll temperature compensation of variable domain fuzzy controller based on improved cat swarm optimization

In the hot rolling process, the hot crown of the roll is determined by the roll temperature and the temperature distribution. Furthermore, the hot crown is an important factor, which causes variation in the strip gage along the plane perpendicular to the rolling direction, affecting the strip quality. However, the roll temperature response has the characteristics of nonlinearity, hysteresis and time-varying, which makes it difficult to control accurately by classical control theory and method. In order to accurately control the rolling temperature, a variable universe fuzzy controller based on improved cat swarm optimization (ICSO-VUFC) was established. Firstly, a dynamic mixture ratio and an improved tracking mode were used to improve the optimization capability of the cat swarm. In comparison with the conventional cat swarm optimization (CSO) controller, the proposed process showed better optimization performance. Secondly, a simulation analysis based on MATLAB was employed to compare the ICSO-VUFC with the conventional fuzzy controller (C-FC) and the fuzzy controller based on the improved cat swarm optimization (ICSO-FC). The results reveal that the ICSO-VUFC exhibits the best dynamic and steady performance. Finally, the temperature control accuracy of the rolled regions during different rolling passes under the three fuzzy controllers was examined and compared. The results show that the ICSO-VUFC exhibits the highest control accuracy and stability with a temperature error range of ±4 °C. Through the analysis of the strip crown, it can be seen that the control accuracy of the strip crown can be effectively improved by using ICSO-VUFC to control the roll temperature distribution.

Mean Field Multi-Agent Reinforcement Learning Method for Area Traffic Signal Control

Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural networks are further enhanced, thereby avoiding the limitations of traditional signal control methods. However, when faced with the sequential decision tasks of regional signal control, it encounters issues such as the curse of dimensionality and environmental non-stationarity. To address the limitations of traditional reinforcement learning algorithms applied to multiple intersections, the mean field theory is applied. This models the traffic signal control problem at multiple intersections within a region as interactions between individual intersections and the average effects of neighboring intersections. By decomposing the Q-function through bilateral estimation between the agent and its neighbors, this method reduces the complexity of interactions between agents while preserving global interactions between the agents. A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network Area Traffic Signal Control (MFQ-ATSC) and Mean Field Actor-Critic Network Area Traffic Signal Control (MFAC-ATSC). The model was validated using the SUMO simulation platform. The experimental results indicate that across different metrics, such as average speed, the mean field reinforcement learning method outperforms classical signal control methods and several existing approaches.

Vibration suppression of an armature assembly in hydraulic servo valve based on distributed parameters mathematical models

As a commonly used hydraulic control component, the nozzle-flapper servo valve has the advantages of small structure and sensitive dynamic response. Due to the small size and complex structure of the servo valve, the classical control methods based on various feedback information cannot effectively improve its performance. In order to effectively improve the performance of the servo valve, this paper implemented the feedforward control method to suppress the dynamic performance of the armature assembly of the servo valve. The distributed parameters mathematical models of the armature assembly are established and verified which can accurately predict the vibration of the armature assembly. With the mathematical model, the feedforward control signal is calculated based on the real-time velocity, acceleration, and deflection of the armature assembly, respectively. The applicability of different signals is analyzed and the signals calculated by velocity and deflection are verified which can effectively suppress the vibration amplitude of armature assembly. The numerical simulation and experimental methods are implemented to verify the performance of different control signals and the optimal feedforward control signal under different external loads is obtained.

Evaluating the establishment of a new water hyacinth biological control agent in South Africa

Megamelus scutellaris Berg (Hemiptera: Delphacidae) is the most recent of nine biological control agents developed to manage invasive water hyacinth, Pontederia (=Eichhornia) crassipes Mart. (Pontederiaceae), in South Africa. More than a million M. scutellaris have been mass-reared and released since the first introduction of the agent into South Africa in 2013, successfully establishing overwintering populations at 32 sites in seven of the nine provinces. Establishment has also been recorded at seven of these sites through natural dispersal from sites where they had established. Inundative releases, where large numbers of M. scutellaris are released regularly, have resulted in excellent establishment, and caused a significant reduction in water hyacinth cover in areas where, historically, biological control seemed unlikely due to excessive eutrophication. Although M. scutellaris has established well throughout South Africa through classical biological control methods, this study also showed that inundative releases of biological control agents over multiple seasons results in the most effective control of the weed, especially at cool temperate and eutrophic sites.

Cooperative Reference Velocity Regulation Control for Virtual Coupling Systems of Heavy-Haul Trains

Virtual coupling systems are important for improving the safety and efficiency of heavy-haul trains by coordinating the velocities of heavy-haul trains. However, the set point change of reference velocity leads to unsafe train operation. To address this issue, in this paper, a cooperative reference velocity regulation control method is proposed for virtual coupling systems of heavy-haul trains, which can smoothly adjust the train velocity during the operation process. Firstly, the heavy-haul trains are characterized by a cyber-physical model, where the physical layer models the longitudinal dynamics of the trains, while the cyber layer represents the communication topology of the trains using graph theory. Secondly, a cooperative control law is designed to achieve velocity consensus and maintain a safe distance between adjacent trains. The set point modulation method is utilized to smoothly adjust the reference velocity of heavy-haul trains, suppressing velocity fluctuations of heavy-haul trains. The proposed approach is validated through extensive simulations under acceleration and deceleration conditions. Simulation results verify that the proposed method can significantly reduce the velocity fluctuations during the operation of virtual coupling systems when compared with the classical cooperative control method.