Traditional PID Control Strategy Research Articles

Design of two-degree-of-freedom position tracking controller for hypergravity unidirectional airborne shaking table

Hypergravity centrifugal shaking table is widely used in the field of civil engineering and is considered to be the most effective means to study geotechnical earthquake disaster effect in the world today. Here, the structure of hypergravity centrifugal shaking table system is briefly introduced, and then the necessity and difficulty of the precise control of the position ring of the airborne shaking table in the hypergravity centrifugal shaking table are explained. On this premise, the system modeling and analysis of the unidirectional airborne shaking table with hypergravity are carried out in this paper. A new two-degree-of-freedom controller of the unidirectional airborne shaking table with hypergravity is designed by combining the [Formula: see text] control and an improved internal model control (IIMC) strategy, and the designed two-degree-of-freedom controller is experimentally verified. The experimental results show that compared with the traditional PID control strategy and the single [Formula: see text] control strategy, the two-degree-of-freedom controller designed in this paper can make the position loop control of the unidirectional airborne shaking table with hypergravity have higher control accuracy and anti-interference ability.

Research on energy-saving strategies in the architectural design of the Eastern Railway in the era of artificial intelligence

Abstract This paper takes the air conditioning energy consumption in railroad building design as the main object of analysis and uses an orthogonal experimental design method to study the influencing factors and effects of energy consumption in the Middle East railroad construction design. Stochastic working condition simulation is carried out for the typical models of passenger stations in five climate zones to obtain the data set of the passenger station energy consumption model, and the multiple linear regression prediction models of air conditioning energy consumption in different regions are established. Combined with the hour-by-hour average outdoor temperature, hour-by-hour passenger flow and lighting and related equipment data in the waiting hall of a city in July, the air-conditioning cold load of railroad passenger stations is simulated and analyzed. Explore the implementation effect of energy-saving strategies for railroad passenger station air-conditioning system, and for the traditional PID control strategy, put forward the control strategy based on model prediction, analyze the change of centralized air-conditioning system cold load and the change of the actual performance coefficient of the chiller unit under different control strategies. The data show that compared with the traditional PID control, the model prediction control reduces the total energy consumption by 0.2063, which realizes the energy-saving optimization of the air conditioning system for railroad building design.

Design and practical application analysis of thermal management system for power battery in new energy vehicles

Due to the significant advantages of electric vehicles in terms of energy saving and emission reduction, they have been strongly supported and developed by the state and society in recent years. As a key component of electric vehicles, the performance of power battery is significantly affected by temperature, and thermal management is crucial to the charging and discharging performance, reliability and life of the battery. In order to extend the service life of the battery, realize the scientific management of the power battery system, ensure the normal operation of the battery thermal management system, and improve the efficiency of electric vehicles, this paper focuses on constructing the mathematical model of the battery module and the motor module, which are the executive part of the battery thermal management system, introducing the classical PID control theory of the motor, and carrying out the design of the fuzzy controller. Finally, the rotational speed of the motor is simulated by the traditional PID control strategy and fuzzy control strategy, which provides effective data for the parameters of the motor in the experiment, verifies the correctness of the calculation model, and shows that this model can be used for the design of this kind of thermal management system, and provides a certain reference for the application of practical engineering.

Intelligent temperature control framework of lithium-ion battery for electric vehicles

The heat generated during battery charging and discharging induces rapid temperature rise, potentially affecting battery performance and safety. Coolant flow rate control has been used to regulate battery temperature to address this. However, traditional battery temperature control strategies have difficulty balancing temperature control accuracy and system response speed. Thus, an intelligent temperature control framework employing two control strategies: Fuzzy Logic Control (FLC) and Reinforcement Learning Control (RLC), is proposed in this paper. Meanwhile, a single-valve temperature control loop based on FLC and a double-valve temperature control loop based on RLC is designed in the framework. Moreover, an intelligent decision method is proposed to select the appropriate control strategy for each operation stage to achieve intelligent control. The results indicate that, compared with the traditional PID control strategy, the response time decreased from 361 s to 225 s by FLC, and the temperature difference decreased from 5.33 K to 2.36 K by RLC. The performance of the temperature control strategy for liquid cooling has been significantly improved.

Decoupling Control for Module Suspension System of Maglev Train Based on Feedback Linearization and Extended State Observer

The suspension gap of the electromagnetic suspension maglev train is around 8 mm. In practice, it is found that the system gap fluctuations are amplified due to the inner coupling of the suspension module system in the maglev train. In addition, maglev trains are affected by load disturbances and parameter perturbations during operation. These uncertainties reduce the ride comfort. Therefore, it is necessary to propose a novel control strategy to suppress inner coupling while reducing the influence of uncertainties on the system. In this paper, a control strategy based on feedback linearization and extended state observer (ESO) is proposed to address this challenge. Firstly, the suspension module system model is established with parameter uncertainties and external disturbances. Additionally, the inner coupling of the suspension module is represented in this model. Subsequently, the feedback linearization method based on differential geometry theory is applied to reduce the effect of inner coupling. Meanwhile, the system uncertainties are transformed into equivalent disturbances by this method. Afterward, a linear ESO is designed to estimate the equivalent disturbances. Finally, a state feedback controller is used to achieve stable suspension and compensate for the disturbances. Simulation and experimental results show that the proposed decoupled control strategy significantly suppresses the influence of inner coupling and uncertainties on the system compared with the traditional PID control strategy.

Discrete time adaptive neural network control for WME and compression refrigeration systems

In this paper, we study the discrete-time control problem for the refrigeration system with unmodeled dynamics. This paper proposes discrete adaptive neural network controllers for two refrigeration systems, including the water membrane evaporator cooling and compression refrigeration systems. The influence of model uncertainty on system performance can be eliminated effectively by designing the neural network and the corresponding discrete-time adaptive updating strategy. Thus, the model parameters in the refrigeration system are not required in our control algorithms. Simulation results show that the proposed strategy can effectively adjust the refrigeration system temperatures. Compared with the traditional PID control strategy, the system response overshoot under our method can be reduced by 0.3%, and the system settling time is reduced by at least 94.3%.

Optimal Control of VAV Air Conditioning Terminal in the Polar Cruise Public Area

As a key system to ensure residential comfort, the control mode of the polar cruise air conditioning system directly relates to the comfort of the cabin environment and the economy of ship operation. Aiming at the characteristics of uncertainty and regulation time delay in the load change of the VAV air conditioning system in the public area of polar cruise ships, this paper models the mechanism of the VAV terminal system and conducts simulation experiments on the Matlab/Simulink simulation platform. By comparing the difference between the traditional PID control strategy and the fuzzy PID control strategy in the control effect, it provides a reference for the optimal control of the polar cruise air conditioning system.

Research on active disturbance rejection control of the main steam temperature of supercritical thermal power unit based on improved particle swarm optimization

The stability of the boiler’s main steam temperature is very important for the safe operation of the unit. At present, the mainstream PID control scheme is simple to use and convenient to set, but it cannot accomplish a acceptable ascendancy effect in the face of the main steam temperature control structure; with a long time delay, large inertia. In view of this situation, first of all, this paper introduces the linear active disturbance rejection control (LADRC) technology in a steam temperature control structure. The main loop adopts the linear active disturbance rejection controller and the subloop adopts the P-controller. Then, the particle swarm optimization algorithm is improved and used to modify the parameter of the LADRC, so that the LADRC system adjusted according to the theory can better complete the control task. Finally, a simulation to control the temperature is given, and the results show that the LADRC control system can track quickly and stably; in addition, as against with this traditional PID control strategy, it can achieve strong anti-interference performance.

A New Control Strategy for SR Generation System Based on Modified PT Control

The Switched Reluctance generator (SRG) has been widely used as a constant voltage source due to its advantages of simple structure, low cost and high control flexibility. However, the output voltage ripples cannot be ignored due to its unique operating principle with phase commutation. In this paper, the influence of the control parameters on the output voltage ripples are analyzed, and a fly-wheeling pulse train (FW-PT) control strategy is proposed to suppress the voltage ripple. The output voltage can be regulated by the FW-PT control strategy by using two or more sets of preset control pulse combinations, therefore it has the advantages of simple circuit implementation, no Network compensation and fast response speed. The characteristics of steady-state and dynamic behaviors of the switched reluctance power generation system by using different control strategies are simulated and compared, and a platform of 200W 8/6 SRG is built for experimental verification. Simulation and experimental results confirm that compared with the traditional PID control strategy, the FW-PT control strategy can be used to not only suppress the output voltage ripple, but also achieve faster response and dynamic characteristics.

VAV Air-Conditioning System Control Using Neural Network Based MPC

Air conditioning system is known as a multi-variable, large delay, nonlinear and uncertain system, and it is difficult to achieve the ideal performance by using the traditional PID control strategy. In order to improve the system performance and reduce energy consumption at the same time, a neural network based MPC controller is proposed in this paper. After establishing the predictive model by using neural networks based on experiment data, the online optimization programming method is utilized to produce the control sequence of the future. And only the first control law is applied to the system to improve the control effect. The simulation result at last shows that the proposed strategy has strong robustness and adaptive ability, high control precision, better and reliable control effect and other advantages.

The Research of Model-Free Adaptive Control for Large Scale Wind Turbine

Dynamic load is a key consideration in large scale wind turbine design. It is approved that the performance of controller can distinguish impact wind turbine loads. For strong external disturbances and inaccurately modeled of large wind turbines, In this paper, we propose model-free adaptive (Model Free Adapt, MFA) individual pitch control algorithms. The controller was developed in to mitigate the rotor unbalance structural load for variable speed wind turbine. The controller is designed from a nonlinear model of the system which takes into account the blades, shaft and tower flexibilities. Bladed software was used for the control strategy and traditional PID control strategy simulation comparison. The results show that the new control strategy can effectively stabilize wind turbine power output and reduce aerodynamic loads.

The Research of Fuzzy Self-Adaptiving PID Control Technology on Inverter Power

With the development of science and technology, high-powered equipments are increasing demanded in various industrial fields. It is no doubt that higher requirements for power inverter control technology is required. In this paper, on the base of traditional PID control strategy, duo to its paraments can’t self-turning, this disadvantage makes it difficult to achieve the best control effect, proposed a fuzzy self-adaptiving PID control technology. By means of MATLAB/Simulink module to realize the inverter achieves good dynamic and static performance.

Speed Servo Control of Pneumatic Manipulator Based on Sliding Mode Variable Structure Control

For a series of non-linear factors of compression, friction, air pressure and load changing in the pneumatic system, it impact seriously on low-speed precision and stability of the pneumatic servo system. As the sliding mode variable structure has a good anti-interference ability and capacity of the model parameter perturbation, In this paper we use the above control strategy to solve the unmodeled part of the pneumatic system dynamics and bounded disturbances, so it can improve accuracy and stability of the aerodynamic servo system. The simulation results indicate that the system combined with sliding mode variable structure strategy has better anti-jamming capability and response speed than the traditional PID control strategy.

The Application of PID Based on Optimized RBF in Thickness Control of Strip Steel

The strip thickness control system is difficult to establish an accurate mathematical model, and traditional PID control strategy has a poor adaptive ability, so the effect of control is always not satisfying. According to the problems above, a new control strategy of self-tuning PID controller based on RBF neural network whose parameters are optimized by PSO algorithm is proposed in the paper. The control method integrates advantages of RBF neural network as well as PID controller and good global search capability of PSO algorithm. The simulation results indicate that the method not only improves control performance and dynamic quality, but also has strong self-adapting ability and robustness. It achieved a very good control effect when used in strip thickness control system that proved the correctness and effectiveness of the control method.

Adaptive Fuzzy PID Applied in Steam Pressure Control System

Boiler steam pressure control system is important because of affecting the turbine speed directly. In engineering, the steam pressure control system is mostly dominated by traditional PID control. But the traditional PID control strategy can’t obtain satisfied control effects with the changing of the fuel and give-wind flow. The use of intelligent control strategy has been studied in recent years in order to improve steam pressure control. In this paper steam pressure control system with adaptive fuzzy PID control is presented, and the simulation results based on MATLAB show that the proposed algorithm can largely improve the system response performance compared to the traditional PID control.

Research on the Stability Affected by Digital Process of SPWM Inverter

Digital controlled SPWM inverter has become main-stream in development of SPWM inverter control strategy. But the traditional PID control strategy realized in digital control can’t achieve better performance than in analog control. Process of the zero-order hold & one-step-delay control were researched the effect of zero-order hold & one-step-delay control on the stability of digital control SPWM inverter with quantitative analysis in this paper. Finally relevant simulations and experiments were verified the validity of the theoretical analysis.

Electro-Hydraulic Servo System Controlled by Index Reaching Law Sliding Mode

The parameters uncertainty and external disturbance play a negative role to improve electro-hydraulic position servo system performance. The valve controlled cylinder system model is established, using the traditional PID control strategy and reaching law control strategy for simulating the system, respectively, the two methods have similar control effects in the ideal model, but considering the external disturbances, the index approaches sliding mode control law has better response speed and stability. Research shown that sliding mode control algorithm has an important role for improving the performance of hydraulic servo position control system.

Online tuning gain scheduling MIMO neural PID control of the 2-axes pneumatic artificial muscle (PAM) robot arm

This paper presents a detailed study to investigate the possibility of applying the online tuning gain scheduling MIMO neural dynamic DNN-PID control architecture to a nonlinear 2-axes pneumatic artificial muscle (PAM) robot arm so as to improve its joint angle position output performance. The proposed controller was implemented as a subsystem to control the real-time 2-axes PAM robot-arm system so as to control precisely the joint angle position of the 2-axes PAM robot arm when subjected to system internal interactions and load variations. The results of the experiment have demonstrated the feasibility and benefits of the novel proposed control approach in comparison with the traditional PID control strategy. The proposed gain scheduling neural MIMO DNN-PID control scheme forced both joint angle outputs of 2-axes PAM robot arm to track those of the reference simultaneously under changes of the load and system coupled internal interactions. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID. These results can be applied to control other highly nonlinear systems.

Dynamic matrix control of diaphragm-type chlorine/caustic electrolysers

Abstract Dynamic matrix control (DMC) is now widely used in industrial control, and when properly applied results in significant savings due to improved efficiency and reduced off-spec product. The objective of this paper is to implement MIMO DMC on a simulated diaphragm-type chlorine/caustic electrolyser. A comparison between the MIMO DMC and traditional multi-loop SISO PID control strategy shows greatly improved controller performance and cell efficiency using DMC, which will result in reduced operating costs and increase cell diaphragm lifespan.