The steam generator (SG) is a critical component of the steam supply system in the nuclear power plant (NPP). Hence, it is necessary to control the SG level well to ensure the stable operation of the NPPs. However, it...
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The steam generator (SG) is a critical component of the steam supply system in the nuclear power plant (NPP). Hence, it is necessary to control the SG level well to ensure the stable operation of the NPPs. However, its dynamic level response process has significant nonlinearity (such as the 'swell and shrinks' effect) and time-varying properties. As most of the SG level control systems (SGLCS) are constructed based on the Proportional-Integral-Derivative (PID) controllers with fixed parameters, the controller parameters should be optimized to improve the performance of the SGLCS. However, traditional parameters tuning methods are generally experience-based, cumbersome, and time-consuming, and it is difficult to obtain the optimal parameters. To address the challenge, this study adopts a knowledge-informed simultaneous perturbation stochastic approximation (IK-SPSA) based on adjacent iteration points information to improve the performance of the SGLCS. Rather than the traditional controller parameter tuning method, the IK-SPSA method optimizes the control system directly by using measurements of control performance. The method's efficiency lies in the following aspects. Firstly, with the help of historical information during the optimization process, the IK-SPSA can dynamically sense the current status of the optimization process. Secondly, it can accomplish the iteration step size tuning adaptively according to the optimization process's current status, reducing the optimization cost. Thirdly, it has the stochastic characteristic of simultaneous perturbation, which gives it high optimization efficiency to optimize high dimensional controller parameters. Fourthly, it incorporates an intelligent termination control mechanism to accomplish optimization progress control. This mechanism could terminate the optimization process intelligently through historical iterative process information, avoiding unnecessary iterations. The optimization method can improve the stability
Chip junction temperature is a key factor affecting the normal operation of the chip. The development of integrated circuit technology brings about high integration and low cost, but it also puts forward higher requir...
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Chip junction temperature is a key factor affecting the normal operation of the chip. The development of integrated circuit technology brings about high integration and low cost, but it also puts forward higher requirements for the cooling system. This paper focuses on the air cooling of the chip, builds a hardware test platform based on MCS-52, the general name of the intel series microcontroller unit, and sets up a mathematical model of the air cooling process of the chip on the matlab platform based on the principle of energy conservation, heat transfer theory and finite element method. By proposing the equivalent convective heat transfer coefficient, the thermal resistance of the system can be well estimated. This model can easily realize the joint simulation of chip, heat radiator, and control strategy, which overcomes the disadvantage that traditional finite element simulation software are difficult to combine with control strategy. In addition, based on the model, the proportional integral differential (PID) controlparameters are automatically optimized, achieving excellent temperature control effect, and proving the feasibility of optimizing the controlparameters through the model.
The steering system is an important link between driver and vehicle, and it has a significant impact on energy consumption and driving experience. In order to improve the system's overall performance, the electro-...
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The steering system is an important link between driver and vehicle, and it has a significant impact on energy consumption and driving experience. In order to improve the system's overall performance, the electro-hydraulic active steering (EHAS) system is taken as the design object, which involves steering energy loss, steering road feel, steering sensitivity and steering stability. According to the energy flow analysis of the steering system, the optimization of the parameters of assist motor and rotary valve is the key to improve steering economy. Based on the optimization of structure parameters of EHAS system, controlparameters are innovatively introduced into the optimization of steering system performance. The influence of optimizationparameters on these evaluation indexes is further explored. Then, the multi-objective optimization model of EHAS system is then established and optimized by a multi objective genetic algorithm. The optimization results show that the energy loss of EHAS system with optimized structure and controlparameters is 9.44% lower than before optimization, and the driving experience is further improved. (c) 2020 Elsevier Ltd. All rights reserved.
The plug-in hybrid electric vehicle (PHEV) has been progressively penetrated in the urban public transport system for its special advantages of energy saving and emission reduction potential. In order to improve the f...
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The plug-in hybrid electric vehicle (PHEV) has been progressively penetrated in the urban public transport system for its special advantages of energy saving and emission reduction potential. In order to improve the fuel economy of PHEV engine, an optimization study on the engine controlparameters is carried out and applied on a PHEV with a rule-based energy management strategy (EMS) in this paper. Firstly, the mathematical model of diesel engine is established on GT-Power platform to accurately reflect the fuel consumption characteristics of engine. Secondly, a control parameter optimization method based on engine equivalent operating point is proposed and four key engine controlparameters are optimized by genetic algorithm (GA). Thirdly, the optimization and calibration of the PHEV engine is performed under ChinaCity driving cycle. The results show that the maximum reduction of engine brake specific fuel consumption (BSFC) on the optimal operation line is 3.56%, and the equivalent fuel consumption of PHEV under ChinaCity, WVUCITY and WVUSUB driving cycles is reduced by 2.62%, 2.24% and 2.05%, respectively.
The paper develops a method for automatic tuning of the PID process controlparameters, which is called “auto-tuning" shortly. The procedure of applying the method consists of (1) sampling a process response to ...
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The paper develops a method for automatic tuning of the PID process controlparameters, which is called “auto-tuning" shortly. The procedure of applying the method consists of (1) sampling a process response to an automatically applied input signal, (2) processing the sampled data for estimating characteristic values of the process, and (3) calculating the optimal values of the PID controlparameters. For the optimization, a new type of performance index, i.e., a weighted integral of squared error is introduced. The procedure is implemented on a small size digital controller and applied to some real processes, yielding satisfactory results.
The controlparameters optimization of the servosystem in parallel robots, which is the basis of the control system design of the robot, is very important. In this paper, the parameters optimization of the servosystem...
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The controlparameters optimization of the servosystem in parallel robots, which is the basis of the control system design of the robot, is very important. In this paper, the parameters optimization of the servosystem based on the genetic algorithm method is introduced. In the servosystem of parallel robots, a three-closed-loop PID control method is usually used. So the controlparameters of the speed control loop and position control loop of the servosystem have great influence on the performance of the motor. To meet the requirement of the control system, a three-closed-loop PID control structure of the servosystem is designed according to the hardware system of the AC servo. With the time-domain performance and dynamic characteristic as the optimization targets, the controlparameters of the speed control loop and position control loop are optimized. Comparing with the tuning result optimized by the traditional Ziegler-Nichols tuning laws, the results obtained by the GA method show better performance in the computer simulation of a parallel robot. Therefore, the GA method which is used in the optimization of the controlparameters of the servosystem, has the advantages of simple calculation, fast tuning speed, good optimization result, and so on.
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