CN112947627B - Temperature control method based on DDPG-fuzzy PID - Google Patents

Temperature control method based on DDPG-fuzzy PID Download PDF

Info

Publication number
CN112947627B
CN112947627B CN202110206340.XA CN202110206340A CN112947627B CN 112947627 B CN112947627 B CN 112947627B CN 202110206340 A CN202110206340 A CN 202110206340A CN 112947627 B CN112947627 B CN 112947627B
Authority
CN
China
Prior art keywords
temperature
fuzzy
value
control
ddpg
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110206340.XA
Other languages
Chinese (zh)
Other versions
CN112947627A (en
Inventor
吴敏
王晓璐
姜玉东
钟磊
莫飞杨
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanxi Ruihai Energy Saving Network Control Electric Central Heating Co ltd
Shenzhen Luchen Information Technology Service Co ltd
Original Assignee
Jinling Institute of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jinling Institute of Technology filed Critical Jinling Institute of Technology
Priority to CN202110206340.XA priority Critical patent/CN112947627B/en
Publication of CN112947627A publication Critical patent/CN112947627A/en
Application granted granted Critical
Publication of CN112947627B publication Critical patent/CN112947627B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D23/00—Control of temperature
    • G05D23/19—Control of temperature characterised by the use of electric means
    • G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)
  • Control Of Temperature (AREA)

Abstract

A temperature control method based on DDPG-fuzzy PID comprises the following steps: step 1, acquiring experimental data of a temperature control system; step 2, designing a fuzzy PID main controller of the temperature system; step 3, optimizing fuzzy PID parameters by a genetic algorithm; step 4, designing a DDPG auxiliary controller of the temperature system; and 5, performing linkage control on the main controller and the auxiliary controller to obtain the temperature regulation and control system with robustness. Aiming at the problems of long temperature control time and large error of the traditional temperature control system, the invention firstly solves the optimization problem that the membership function and the fuzzy rule of the fuzzy PID are difficult to determine through a genetic algorithm, and improves the control performance and the precision of the fuzzy PID; secondly, a control method using fuzzy PID as a main controller and DDPG as an auxiliary controller is provided, the controller can reduce noise interference, enhance the adaptability and robustness of the system and is beneficial to improving the control precision of the system.

Description

Temperature control method based on DDPG-fuzzy PID
Technical Field
The invention relates to the field of temperature control, in particular to a temperature control method based on DDPG-fuzzy PID.
Background
With the development of science and technology, temperature control is widely applied to various technical fields, and the implementation of central heating, reasonable resource utilization, energy utilization efficiency improvement and environmental pollution reduction is an effective measure and an important way for accelerating urban modernization. Central heating has become the main form of winter heating in northern china and is increasingly widely used, and it uses a heat supply network to connect a large number of heat consumers and provides necessary heat through a uniform heat source. In a heating system, a temperature regulation system is a complex system with large lag, time variation and nonlinearity, and the traditional PID control cannot meet the control requirement.
In order to better solve the control problem of the system, the fuzzy PID combines fuzzy control with a classical PID regulator, and meanwhile, the fuzzy PID has flexibility and adaptability of the fuzzy control and improves the control precision of the system. However, fuzzy PID systems have difficulty accurately controlling temperature when there are severe and frequent disturbances and large hysteresis in the control system. Aiming at the problem, the invention designs a temperature control method based on DDPG-fuzzy PID, which introduces an auxiliary controller based on DDPG to a fuzzy PID control system, utilizes DDPG algorithm to carry out online weight learning capability, carries out auxiliary control on a system actuator, realizes automatic compensation of uncertain interference of a temperature control system, and obtains a temperature regulation and control system with robustness through linkage control of a main controller and an auxiliary controller.
Disclosure of Invention
In order to solve the problems, the invention provides a temperature control method based on DDPG-fuzzy PID. The control precision of the temperature system is improved through a fuzzy PID control algorithm, the optimization problem that the membership function and the fuzzy rule of the fuzzy PID are difficult to determine is further solved through a genetic algorithm, and finally, a control method which takes the fuzzy PID as a main controller and takes the DDPG as an auxiliary controller is provided. In order to achieve the purpose, the invention provides a temperature control method based on DDPG-fuzzy PID, which comprises the following steps:
step 1, acquiring experimental data of a temperature control system: detecting the temperature of the system through a temperature sensor, and recording the output and command signals of an actuator;
step 2, designing a fuzzy PID main controller of the temperature system: the fuzzy PID controller is used as a main controller to control the action of an actuator, and the tracking of the set value of the system temperature is completed;
step 3, optimizing fuzzy PID parameters by a genetic algorithm: the membership function and the fuzzy rule of the fuzzy PID are optimized by adopting a genetic algorithm, so that the control performance of the temperature system is improved;
step 4, designing a DDPG auxiliary controller of the temperature system: the DDPG auxiliary controller realizes auxiliary control on the temperature system by detecting the temperature value of the temperature control system, the reward value for executing the action and the system temperature value after executing the action;
and 5, performing linkage control on the main controller and the auxiliary controller to obtain a temperature regulation and control system with robustness, and embedding the temperature regulation and control system into an upper computer for practical application.
Further, the process of designing the fuzzy PID main controller of the temperature system in the step 2 is represented as follows:
the temperature control system respectively sets the temperature set value and the temperature recovery value as two-dimensional input including the deviation e (t) of the system temperature set value and the measurement feedback value, the deviation signal change rate ec (t) and the three-dimensional output including the proportionality coefficient kpIntegral coefficient kiDifferential coefficient kdThe system modifies the parameters of the fuzzy PID controller in real time through monitoring and the value of ec (t), thereby optimizing the control performance of the fuzzy PID controller; in the temperature control system, a deviation signal e (t) is obtained by the operation of a sensor for recovering the current temperature value and a target set value, the deviation signal e (t) and the deviation signal change rate ec (t) are fuzzified to obtain a corresponding fuzzy quantity, then the fuzzy control quantity is obtained by reasoning according to a fuzzy rule, and finally the fuzzy control quantity is subjected to sharpening treatment to accurately control a controlled object to obtain kp、kiAnd kd。
Further, the process of optimizing the fuzzy PID parameter by the genetic algorithm in step 3 can be expressed as follows:
the genetic algorithm is an iterative self-adaptive optimal solving algorithm based on a natural selection principle and a genetic mechanism, can improve the control performance of a temperature system, and comprises the following steps of:
step 3.1, fuzzy domain coding; the actual range of temperature deviation e (t) may reach-100 deg.C to 100 deg.C, and thus, its domain of discourse is [ -100,100 [ ]]Since the temperature changes slowly, the fundamental range of the rate of change of the temperature deviation is approximately [ -2,2 [ ]]The output k is analyzed through parameter settingp、ki、kdThe actual universe of discourse is set to [ -1,1 []、[-0.02，0.02]、[-0.3，0.3](ii) a The input and output fuzzy domain is set as [ -6, -5, -4, -3,2, -1,0,1,2,3,4,5,6](ii) a The system sets negative large NB and negative medium NThe 7 language variable values of M, negative NS, zero ZO, positive small PS, positive middle PM and positive large PB are coded and expressed by 0,1,2,3,4,5 and 6;
step 3.2, selecting an initial population; setting an initial population parameter range according to a set value, setting the optimal solution parameter range of the initial population to [0, 6], and randomly generating an initial population with the scale of 100 in the set range;
step 3.3, selecting a fitness function; the fitness function judges the fitness of the individual by judging the individual characteristics, the fitness of the individual is used as a quality standard for evaluating PID parameters, and the dynamic deviation, overshoot and adjustment time performance indexes of the system are used as target functions, so that the fitness function is described as follows:
Figure BDA0002950884130000021
where u (t) is the controller output, tu is the system response time, ω1、ω2、ω3Is a weighting constant;
step 3.4, selecting a genetic operator; the roulette selection method determines the probability of selection by using the ratio of the individual fitness value to the overall fitness value in the overall population, and the formula is as follows:
Figure BDA0002950884130000022
wherein, PiIs the probability that the individual i is genetically selected, fjIs the global fitness value of the superposition of all individuals, fiIs the fitness value of the individual i;
step 3.5, crossover and mutation operations; the genetic algorithm mutually exchanges partial genes of two paired individuals according to a certain mode through cross operation, the cross probability is set to be 0.86, and a two-point cross algorithm is adopted, so that two new individuals are formed; meanwhile, in order to improve the local search capability, the mutation probability is set to be 0.04, and a new individual is generated by changing certain gene values of the individual through mutation operation.
Further, in step 4, the process of designing the temperature system DDPG auxiliary controller can be expressed as follows:
the DDPG algorithm obtains sample data(s) through a temperature control systemt,at,rt+1,st+1) Wherein s istIs the temperature value of the temperature control system at time t, atIs that the system is in stAction performed in the state rt+1Is the system is in state stLower execution action atIs given a prize value of st+1The temperature control system is performing action atAnd (3) measuring the temperature value of the system, putting the sample data into an experience pool, randomly sampling the minimum batch data from the experience pool to learn and update, and finally executing the following actions by the DDPG auxiliary controller:
at＝μ(st|θμ)+Nt (3)
in the formula, NtIs random noise, the function μ () is the optimal behavior strategy, θμRepresenting policy network parameters; the DDPG auxiliary controller is mainly used for compensating the temperature tracking error of a temperature control system and improving the control performance of the fuzzy PID main controller.
The invention relates to a temperature control method based on DDPG-fuzzy PID, which has the following beneficial effects: the invention has the technical effects that:
1. on the basis of a fuzzy PID controller, a temperature regulation system taking the fuzzy PID as a main controller is established, and the optimal membership function and fuzzy rule of the fuzzy PID are screened through a genetic algorithm, so that the system precision is improved;
2. in order to increase the self-adaption and anti-interference capability of a temperature regulation system, the invention designs a new auxiliary controller based on DDPG, which utilizes DDPG algorithm to carry out on-line weight learning capability, carries out auxiliary control on a system actuator and can automatically compensate the uncertainty and interference of a temperature control system;
3. the robust temperature regulation and control system is obtained through linkage control of the main controller and the auxiliary controller, the regulation robustness of the temperature system is far superior to that of a traditional temperature control system, and the regulation time, the delay time and the like are obviously shortened.
Drawings
FIG. 1 is a control block diagram of the present invention;
FIG. 2 is a membership function optimized by the genetic algorithm of the present invention;
FIG. 3 is a graph of the dynamic response of three model algorithms for a target temperature of 30 ℃ in accordance with the present invention;
FIG. 4 shows three model algorithm dynamic response curves at a target temperature of 30 ℃ under white noise interference according to the invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a temperature control method based on DDPG-fuzzy PID, aiming at obtaining a robust temperature regulation and control system and reducing the regulation time and delay time of the system. Fig. 1 is a control structure diagram of the present invention. The steps of the present invention will be described in detail below with reference to the control structure diagram.
Step 1, acquiring experimental data of a temperature control system: detecting the temperature of the system through a temperature sensor, and recording the output and command signals of an actuator;
step 2, designing a fuzzy PID main controller of the temperature system: the fuzzy PID controller is used as a main controller to control the action of an actuator, and the tracking of the set value of the system temperature is completed;
the process of designing the fuzzy PID main controller of the temperature system in the step 2 can be expressed as follows:
the temperature control system respectively sets the temperature set value and the temperature recovery value as two-dimensional input (the deviation e (t) of the system temperature set value and the measurement feedback value), the deviation signal change rate ec (t) and three-dimensional output (a proportionality coefficient k)pIntegral coefficient kiDifferential coefficient kd) The system modifies the parameters of the fuzzy PID controller in real time through monitoring and the value of ec (t), thereby optimizing the control performance of the fuzzy PID controller; in the temperature control system, the deviation signal e (t) is obtained by the operation of the sensor for recovering the current temperature value and the target set value, and then the deviation signal e (t) and the deviation signal change rate ec (t) are fuzzified to obtain the corresponding deviation signalFuzzy control quantity is obtained through reasoning according to fuzzy rules, and finally, in order to accurately control the controlled object, the fuzzy control quantity is subjected to sharpening processing to obtain kp、kiAnd kd。
Step 3, optimizing fuzzy PID parameters by a genetic algorithm: the membership function and the fuzzy rule of the fuzzy PID are optimized by adopting a genetic algorithm, so that the control performance of the temperature system is improved;
the process of genetic algorithm optimization of fuzzy PID parameters in step 3 can be represented as follows:
the genetic algorithm is an iterative self-adaptive optimal solving algorithm based on a natural selection principle and a genetic mechanism, can improve the control performance of a temperature system, and comprises the following steps of:
step 3.1, fuzzy domain coding; the actual range of temperature deviation e (t) may reach-100 deg.C to 100 deg.C, and thus, its domain of discourse is [ -100,100 [ ]]Since the temperature changes slowly, the fundamental range of the rate of change of the temperature deviation is approximately [ -2,2 [ ]]The output k is analyzed through parameter settingp、ki、kdThe actual universe of discourse is set to [ -1,1 []、[-0.02，0.02]、[-0.3，0.3](ii) a The input and output fuzzy domain is set as [ -6, -5, -4, -3,2, -1,0,1,2,3,4,5,6](ii) a The system sets 7 language variable values of negative large NB, negative middle NM, negative NS, zero ZO, positive small PS, positive middle PM and positive large PB, and carries out coding representation by 0,1,2,3,4,5 and 6;
step 3.2, selecting an initial population; setting an initial population parameter range according to a set value, setting the optimal solution parameter range of the initial population to [0, 6], and randomly generating an initial population with the scale of 100 in the set range;
step 3.3, selecting a fitness function; the fitness function judges the fitness of the individual by judging the individual characteristics, the fitness of the individual is used as a quality standard for evaluating PID parameters, and the dynamic deviation, overshoot and adjustment time performance indexes of the system are used as target functions, so that the fitness function is described as follows:
Figure BDA0002950884130000051
where u (t) is the controller output, tu is the system response time, ω1、ω2、ω3Is a weighting constant;
step 3.4, selecting a genetic operator; the roulette selection method determines the probability of selection by using the ratio of the individual fitness value to the overall fitness value in the overall population, and the formula is as follows:
Figure BDA0002950884130000052
wherein, PiIs the probability that the individual i is genetically selected, fjIs the global fitness value of the superposition of all individuals, fiIs the fitness value of the individual i;
step 3.5, crossover and mutation operations; the genetic algorithm mutually exchanges partial genes of two paired individuals according to a certain mode through cross operation, the cross probability is set to be 0.86, and a two-point cross algorithm is adopted, so that two new individuals are formed; meanwhile, in order to improve the local search capability, the mutation probability is set to be 0.04, and a new individual is generated by changing certain gene values of the individual through mutation operation.
The invention sets the iteration times of the genetic algorithm as 100 times, and k can be obtained after 100 times of iterative computationp、ki、kdThe fuzzy control rules and their membership functions are shown in tables 1 to 3 and fig. 2, respectively.
TABLE 1 genetic Algorithm optimized kpRule base
Figure BDA0002950884130000053
TABLE 2 genetic Algorithm optimized kiRule base
Figure BDA0002950884130000054
TABLE 3 genetic Algorithm optimized kdRule base
Figure BDA0002950884130000061
Step 4, designing a DDPG auxiliary controller of the temperature system: the DDPG auxiliary controller realizes auxiliary control on the temperature system by detecting the temperature value of the temperature control system, the reward value for executing the action and the system temperature value after executing the action;
in step 4, the process of designing the temperature system DDPG auxiliary controller can be represented as follows:
the DDPG algorithm obtains sample data(s) through a temperature control systemt,at,rt+1,st+1) Wherein s istIs the temperature value of the temperature control system at time t, atIs that the system is in stAction performed in the state rt+1Is the system is in state stLower execution action atIs given a prize value of st+1The temperature control system is performing action atAnd (3) measuring the temperature value of the system, putting the sample data into an experience pool, randomly sampling the minimum batch data from the experience pool to learn and update, and finally executing the following actions by the DDPG auxiliary controller:
at＝μ(st|θμ)+Nt (3)
in the formula, NtIs random noise, the function μ () is the optimal behavior strategy, θμRepresenting policy network parameters; the DDPG auxiliary controller is mainly used for compensating the temperature tracking error of a temperature control system and improving the control performance of the fuzzy PID main controller, and the parameters of a DDPG network are shown in a table 4.
TABLE 4 parameter settings for DDPG auxiliary controller
Figure BDA0002950884130000062
And 5, performing linkage control on the main controller and the auxiliary controller to obtain a temperature regulation and control system with robustness, and embedding the temperature regulation and control system into an upper computer for practical application.
The response curves of the traditional PID control system, the fuzzy PID control system and the DDPG fuzzy self-tuning control system based on the method are compared at the same time under the same condition, and the results are shown in figures 3 and 4
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (1)

1. A temperature control method based on DDPG-fuzzy PID comprises the following steps:
step 1, acquiring experimental data of a temperature control system: detecting the temperature of the system through a temperature sensor, and recording the output and command signals of an actuator;
step 2, designing a fuzzy PID main controller of the temperature system: the fuzzy PID controller is used as a main controller to control the action of an actuator, and the tracking of the set value of the system temperature is completed;
the process of designing the fuzzy PID main controller of the temperature system in the step 2 is represented as follows:
the temperature control system respectively sets the temperature set value and the temperature recovery value as two-dimensional input including the deviation e (t) of the system temperature set value and the measurement feedback value, the deviation signal change rate ec (t) and the three-dimensional output including the proportionality coefficient kpIntegral coefficient kiDifferential coefficient kdThe system modifies the parameters of the fuzzy PID controller in real time through monitoring and the value of ec (t), thereby optimizing the control performance of the fuzzy PID controller; in the temperature control system, a deviation signal e (t) is obtained by the operation of a sensor for recovering the current temperature value and a target set value, the deviation signal e (t) and the deviation signal change rate ec (t) are fuzzified to obtain a corresponding fuzzy quantity, then the fuzzy control quantity is obtained by reasoning according to a fuzzy rule, and finally the fuzzy control quantity is subjected to sharpening treatment to accurately control a controlled object to obtain kp、kiAnd kd；
Step 3, optimizing fuzzy PID parameters by a genetic algorithm: the membership function and the fuzzy rule of the fuzzy PID are optimized by adopting a genetic algorithm, so that the control performance of the temperature system is improved;
the process of optimizing the fuzzy PID parameters by the genetic algorithm in the step 3 is represented as follows:
the genetic algorithm is an iterative self-adaptive optimal solving algorithm based on a natural selection principle and a genetic mechanism, can improve the control performance of a temperature system, and comprises the following steps of:
step 3.1, fuzzy domain coding; the actual range of temperature deviation e (t) reaches-100 deg.C to 100 deg.C, so its domain of discourse is defined as [ -100,100 [)]Since the temperature changes slowly, the basic universe of the rate of change of the temperature deviation is [ -2,2]The output k is analyzed through parameter settingp、ki、kdThe actual universe of discourse is set to [ -1,1 []、[-0.02，0.02]、[-0.3，0.3](ii) a The input and output fuzzy domain is set as [ -6, -5, -4, -3,2, -1,0,1,2,3,4,5,6](ii) a The system sets 7 language variable values of negative large NB, negative middle NM, negative NS, zero ZO, positive small PS, positive middle PM and positive large PB, and carries out coding representation by 0,1,2,3,4,5 and 6;
step 3.2, selecting an initial population; setting an initial population parameter range according to a set value, setting the optimal solution parameter range of the initial population to [0, 6], and randomly generating an initial population with the scale of 100 in the set range;
step 3.3, selecting a fitness function; the fitness function judges the fitness of the individual by judging the individual characteristics, the fitness of the individual is used as a quality standard for evaluating PID parameters, and the dynamic deviation, overshoot and adjustment time performance indexes of the system are used as target functions, so that the fitness function is described as follows:
Figure FDA0003292372600000021
where u (t) is the controller output, tu is the system response time, ω1、ω2、ω3Is a weighting constant;
step 3.4, selecting a genetic operator; the roulette selection method determines the probability of selection by using the ratio of the individual fitness value to the overall fitness value in the overall population, and the formula is as follows:
Figure FDA0003292372600000022
wherein, PiIs the probability that the individual i is genetically selected, fjIs the global fitness value of the superposition of all individuals, fiIs the fitness value of the individual i;
step 3.5, crossover and mutation operations; the genetic algorithm mutually exchanges partial genes of two paired individuals according to a certain mode through cross operation, the cross probability is set to be 0.86, and a two-point cross algorithm is adopted, so that two new individuals are formed; meanwhile, in order to improve the local search capability, the mutation probability is set to be 0.04, and certain gene values of individuals are changed by adopting mutation operation to generate new individuals;
step 4, designing a DDPG auxiliary controller of the temperature system: the DDPG auxiliary controller realizes auxiliary control on the temperature system by detecting the temperature value of the temperature control system, the reward value for executing the action and the system temperature value after executing the action;
in step 4, the process of designing the temperature system DDPG auxiliary controller is represented as follows:
the DDPG algorithm obtains sample data(s) through a temperature control systemt,at,rt+1,st+1) Wherein s istIs the temperature value of the temperature control system at time t, atIs that the system is in stAction performed in the state rt+1Is the system is in state stLower execution action atIs given a prize value of st+1The temperature control system is performing action atAnd (3) measuring the temperature value of the system, putting the sample data into an experience pool, randomly sampling the minimum batch data from the experience pool to learn and update, and finally executing the following actions by the DDPG auxiliary controller:
at＝μ(st|θμ)+Nt (3)
in the formula, NtIs random noise, the function μ () is the optimal behavior strategy, θμRepresenting policy network parameters; the DDPG auxiliary controller is used for compensating the temperature tracking error of the temperature control system and improving the control performance of the fuzzy PID main controller;
and 5, performing linkage control on the main controller and the auxiliary controller to obtain a temperature regulation and control system with robustness, and embedding the temperature regulation and control system into an upper computer for practical application.
CN202110206340.XA 2021-02-24 2021-02-24 Temperature control method based on DDPG-fuzzy PID Active CN112947627B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110206340.XA CN112947627B (en) 2021-02-24 2021-02-24 Temperature control method based on DDPG-fuzzy PID

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110206340.XA CN112947627B (en) 2021-02-24 2021-02-24 Temperature control method based on DDPG-fuzzy PID

Publications (2)

Publication Number Publication Date
CN112947627A CN112947627A (en) 2021-06-11
CN112947627B true CN112947627B (en) 2021-11-23

Family

ID=76245941

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110206340.XA Active CN112947627B (en) 2021-02-24 2021-02-24 Temperature control method based on DDPG-fuzzy PID

Country Status (1)

Country Link
CN (1) CN112947627B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115268270A (en) * 2022-07-29 2022-11-01 华能伊敏煤电有限责任公司 Multi-source heat supply network heater optimized scheduling method, system, equipment and medium
CN115755589A (en) * 2022-12-16 2023-03-07 上海第二工业大学 Optimization method based on genetic algorithm in reaction kettle fuzzy control system
CN116203342B (en) * 2023-04-27 2023-07-07 成都中航华测科技有限公司 Temperature control method for device testing process based on high-low temperature experiment box

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102787915A (en) * 2012-06-06 2012-11-21 哈尔滨工程大学 Diesel engine electronic speed adjusting method based on reinforced study of proportion integration differentiation (PID) controller
CN206453107U (en) * 2016-08-17 2017-09-01 上海普丽盛三环食品设备工程有限公司 A kind of UHT sterilization temperatures PID control structure
CN109193075A (en) * 2018-09-28 2019-01-11 合肥工业大学 Power battery of pure electric automobile method for controlling cooling system based on intensified learning
CN110323981A (en) * 2019-05-14 2019-10-11 广东省智能制造研究所 A kind of method and system controlling permanent magnetic linear synchronous motor
CN111449040A (en) * 2020-03-12 2020-07-28 清远市智慧农业研究院 Minimum error variable pesticide application control method and system
CN111459212A (en) * 2020-04-21 2020-07-28 金陵科技学院 Constant-temperature water bath control method based on feedforward feedback fuzzy self-tuning PID control algorithm

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7672739B2 (en) * 2005-08-11 2010-03-02 University Of South Florida System for multiresolution analysis assisted reinforcement learning approach to run-by-run control
CN105807607B (en) * 2016-05-11 2018-09-25 杭州电子科技大学 A kind of genetic algorithm optimization predictive fuzzy PID coking furnace temprature control methods
US10915073B2 (en) * 2017-12-15 2021-02-09 Exxonmobil Research And Engineering Company Adaptive PID controller tuning via deep reinforcement learning

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102787915A (en) * 2012-06-06 2012-11-21 哈尔滨工程大学 Diesel engine electronic speed adjusting method based on reinforced study of proportion integration differentiation (PID) controller
CN206453107U (en) * 2016-08-17 2017-09-01 上海普丽盛三环食品设备工程有限公司 A kind of UHT sterilization temperatures PID control structure
CN109193075A (en) * 2018-09-28 2019-01-11 合肥工业大学 Power battery of pure electric automobile method for controlling cooling system based on intensified learning
CN110323981A (en) * 2019-05-14 2019-10-11 广东省智能制造研究所 A kind of method and system controlling permanent magnetic linear synchronous motor
CN111449040A (en) * 2020-03-12 2020-07-28 清远市智慧农业研究院 Minimum error variable pesticide application control method and system
CN111459212A (en) * 2020-04-21 2020-07-28 金陵科技学院 Constant-temperature water bath control method based on feedforward feedback fuzzy self-tuning PID control algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Longitudinal Tracking Control of Vehicle Platooning Using DDPG-based PID;Junru Yang;《2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)》;20201220;全文 *
基于强化学习的无人船路径跟随控制;张旋武;《中国优秀博硕士学位论文全文数据库(硕士)》;20200301;全文 *

Also Published As

Publication number Publication date
CN112947627A (en) 2021-06-11

Similar Documents

Publication Publication Date Title
CN112947627B (en) Temperature control method based on DDPG-fuzzy PID
CN107272403A (en) A kind of PID controller parameter setting algorithm based on improvement particle cluster algorithm
Man et al. Genetic algorithms for control and signal processing
WO2021196997A1 (en) Method for predicting and controlling water level of series of water conveyance canals on basis of fuzzy neural network
JPH04127205A (en) Process controller
CN107868979A (en) A kind of silicon single crystal diameter control method based on permanent casting speed control structure
CN109472397B (en) Polymerization process parameter adjusting method based on viscosity change
CN110097929A (en) A kind of blast furnace molten iron silicon content on-line prediction method
CN109143853B (en) Self-adaptive control method for liquid level of fractionating tower in petroleum refining process
CN113138555A (en) GRNN electric spindle thermal error modeling method based on genetic algorithm optimization
Wang et al. Adaptive critic control design with knowledge transfer for wastewater treatment applications
CN108460462A (en) A kind of Interval neural networks learning method based on interval parameter optimization
Kusumoputro et al. System identification and control of pressure process rig system using Backpropagation Neural Networks
CN105955014A (en) Method for controlling coke furnace chamber pressure based on distributed dynamic matrix control optimization
CN112668234A (en) Intelligent control method for steelmaking endpoint of converter
Qiao et al. Decoupling control for wastewater treatment process based on recurrent fuzzy neural network
CN109408896B (en) Multi-element intelligent real-time monitoring method for anaerobic sewage treatment gas production
CN102393645A (en) Control method of high-speed electro-hydraulic proportional governing system
Wang et al. Data-driven tracking control design with reinforcement learning involving a wastewater treatment application
CN111240201B (en) Disturbance suppression control method
Arshad et al. Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning.
Chen et al. Neuroadaptive tracking control of affine nonlinear systems using echo state networks embedded with multiclustered structure and intrinsic plasticity
CN116243604A (en) Self-adaptive neural network sliding mode control method, device and medium for sewage denitrification process
Liu et al. The covariance intersection fusion estimation algorithm weighted by diagonal matrix based on genetic simulated annealing algorithm and machine learning
Cabrera et al. Identification of a fed-batch fermentation process: comparison of computational and laboratory experiments

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right

Effective date of registration: 20221125

Address after: 030006 No. B011, Floor 6, No. 49, Zhengyang Street, Taiyuan Tanghuai Park, Comprehensive Reform Demonstration Zone, Taiyuan City, Shanxi Province (settled in Jinchuang Incubator)

Patentee after: Shanxi Ruihai energy saving network control electric central heating Co.,Ltd.

Address before: 518109 909, Building 49, No. 3, Queshan Yunfeng Road, Gaofeng Community, Dalang Street, Longhua District, Shenzhen, Guangdong

Patentee before: Shenzhen luchen Information Technology Service Co.,Ltd.

Effective date of registration: 20221125

Address after: 518109 909, Building 49, No. 3, Queshan Yunfeng Road, Gaofeng Community, Dalang Street, Longhua District, Shenzhen, Guangdong

Patentee after: Shenzhen luchen Information Technology Service Co.,Ltd.

Address before: No. 99 Jiangning Road, Nanjing District hirokage 210000 cities in Jiangsu Province

Patentee before: JINLING INSTITUTE OF TECHNOLOGY

TR01 Transfer of patent right