CN102799106A - Fuzzy logic controller of artificial ecosystem - Google Patents
Fuzzy logic controller of artificial ecosystem Download PDFInfo
- Publication number
- CN102799106A CN102799106A CN2012102910394A CN201210291039A CN102799106A CN 102799106 A CN102799106 A CN 102799106A CN 2012102910394 A CN2012102910394 A CN 2012102910394A CN 201210291039 A CN201210291039 A CN 201210291039A CN 102799106 A CN102799106 A CN 102799106A
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- controller
- artificial ecological
- matlab
- real
- 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.)
- Pending
Links
Images
Landscapes
- Feedback Control In General (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a fuzzy logic controller capable of automatically controlling an artificial ecosystem, which mainly aims to the process control problem of a domestic space controlled ecologic life-support system and a ground facility agricultural system. Based on professional knowledge and expert experiences, a fuzzy reasoning system is created to perform fuzzy control and judgment to the artificial ecosystem comprising wheat, earthworm, microorganism and artificial environment. Based on a mathematical model, MatLab/Simulink is used to build a simulation model of the system and the fuzzy controller for designing the controller and optimizing the dynamic process. MatLab/Real-time Workshop is used to perform fast prototype design to the fuzzy controller simulation model to be an actual controller. By online sampling monitor of key indexes in the system, the key environment factors regulating and influencing system operation are fed back; the system can be operated above the ideal level, and excellently dynamically responds control function and internal/external disturbance, thereby realizing automatic control of the artificial ecosystem.
Description
Technical field
The present invention relates to a kind of method of auto-control artificial ecological system, be primarily aimed at the control and the adjusting of space controlled ecological life support system and surface facility agricultural system operational process.
Background technology
When the front space development of science and technology has solved the problem of the mankind being sent into space, but how the backer has become the space sciemtifec and technical sphere to press for the key issue of solution at the long term survival of space several months even several years.Final solution is that the ground ecosystem is transplanted to the outer space, makes it to become controlled artificial ecological system, and the life support and the guarantee of long-term sustainable are provided for the spacefarer through regulation and control substance circulation effectively, energy Flow and information transmission.Ground industrialized agriculture system also belongs to controlled artificial ecological system simultaneously, and is similar with the former, also need under people's correct intervention, could normally move, and create good economic benefit and ecological benefits.Artificial ecological system be by the vital movement of biology and and artificial environment between complicated interaction drive; Belong to complication system; Have highly non-linear, time variation and uncertainty, its operational process usually can not rest on the fixing working point yet, and can receive the influence of various interior change and external disturbance; Therefore traditional method such as open loop control and PID control etc.; All can't control and regulate artificial ecological system effectively, thereby cause system normally to move as requested, even collapse.The mathematical model of artificial ecological system gamma controller is difficult to confirm through parsing and comprehensive method equally, the controller model complicacy that perhaps obtains through this method, and robustness and adaptivity are poor, are difficult to adapt to the constantly environmental baseline of variation.
Present fuzzy inference system (Fuzzy Inference System; FIS) successfully applied in the middle of the control of complication system; Because can be according to professional knowledge and expertise; Through natural language but not mathematical linguistics the control strategy of complication system is developed, make it be widely used in the realization of Control of Nonlinear Systems device.Therefore we can use FIS to set up fuzzy logic controller (the Fuzzy Logic Controller that system's operational process is carried out fuzzy overall evaluation and closed-loop control; FLC); Artificial ecological system can stably be moved as requested, and have good dynamic response and disturbance rejection performance.It has certain meaning to the foundation and the surface facility Agricultural Development of Chinese Space life-support systems.
Summary of the invention
The objective of the invention is development and can regulate and control to comprise the FLC of the artificial ecological system operation of wheat (Triticum aestivum L.), earthworm (Eisenia foetida Savignnny), microorganism and artificial environment, system is kept fit stably move.For achieving the above object; The technical scheme of taking is: the running status to the real-time online measuring system is carried out fuzzy overall evaluation; Through change system 4 key factors---light intensity, temperature, humidity and aeration speed influence each biological growth and metabolism; And then system regulated effectively, make it to operate in steadily on the desirable level.For realizing automatic control, in MatLab/Simulink, set up the realistic model of FLC controller, and be embedded in the single-chip microcomputer through MatLab/Real-Time Workshop generation C code, the FLC controller is carried out the rapid prototyping design, as the working control device.
Although The present invention be directed to the FLC controller of specific artificial ecological system; But other artificial ecological systems can be on the basis of its concrete property and knowledge experience, the structure (membership function and fuzzy rule) of the FIS that realizes the FLC function and parameter is proofreaied and correct get final product.
Description of drawings
Fig. 1 controls the method flow diagram of artificial ecological system automatically for the present invention;
The model logic controller structural drawing that Fig. 2 sets up for the present invention.
Embodiment
Be described in further detail below in conjunction with the accompanying drawing specific embodiments of the invention.
1. based on the FLC design of Controller of numerical simulation
On the MatLab/Simulink platform, FLC is carried out the numerical simulation design, it comprises 7 inputs, also is simultaneously the evaluation index of operation: the growth rate (x of wheat
1), the quantity (x of earthworm
2), the product (x of two kinds of major microorganisms group quantity
3), earthworm to plant not edible biomass get food speed (x
4), microorganism utilizes speed (x to the earthworm excrement
5), the content (x of small organic molecule in type soil matrix
6) and gas phase in O
2With CO
2Ratio (the x of concentration
7) and 5 outputs: light intensity (u
1), temperature (u
2), humidity (u
3), aeration speed (u
4) and the fuzzy overall evaluation result (u) of FIS.
(1) foundation of FIS input-output mapping: according to relevant ecological, physiology and biochemical knowledge and expertise, utilization if ... The mapping relations between artificial ecological system input and the input set up in the then statement, embodied the control strategy to system's operation.According to the FUZZY MAPPING of being set up, just can be according to the operational process information (x of system of online acquisition
1~ x
7) remove the control signal (u of the build environment factor
1~ u
4).Adopt Gauss (Gaussian) membership function, G (Sigmoidally) membership function and triangle (Triangular) membership function that the input and output data are carried out obfuscation; Fuzzy implication (Implication) method adopts probable or (probabilistic OR) method and the computing of minimum (min) method; Fuzzy polymerization (Aggregation) method adopts maximum (max) computing, and ambiguity solution (defuzzification) method adopts figure center (centroid) method.
(2) fuzzy overall evaluation of FIS and process optimization: the same operational process information (x of system according to online acquisition
1～x
7), utilization if ... The then statement carries out fuzzy overall evaluation to the operation conditions of system.Set up desirable reference fuzzy overall evaluation curve in advance according to designing requirement; This curve has attainable, good time delay, rise time, time to peak, overshoot and stabilization time etc., and is used for the parameter of FLC membership function is optimized.Optimized Algorithm adopts genetic algorithm, senior direct search method and grid adaptable search method, and iterations is 10
9, the error margin of function and independent variable is 10
-9The optimization method of uncertain parameters adopts Monte Carlo method.
2. the FLC based on real-time simulation develops
After FLC having been carried out design and optimization through numerical simulation; Make it to connect with the prototype of artificial ecological system feedback formation hardware in the loop structure of (HIL); Executive real-time emulation is further checked and conclusive evidence the control effect of FLC on MatLab/Real-Time Workshop platform, and it is carried out the rapid prototyping design of controller.Except the main frame of an operation FLC realistic model and human-computer interaction interface, HIL comprises other peripherals, like O
2And CO
2Sensor, wheat biomass image analyzer, microorganism count appearance, digital amplifier, data acquisition board (NIPCI-622137-pin) and physics actuator: pulse-width regulated (PWM) led light source, air-conditioning equipment, gas (liquid) pressure electromagnetic valve, or the like.
The basic procedure of real-time simulation is following: during the artificial ecological system operation, the process regular hour samples to system, obtains x
1～x
7Value; Input FLC realistic model obtains the control output of fuzzy overall evaluation result and actuator; Cause intrasystem correlative environmental factors to change, thereby cause the operational process of system to change, make its fuzzy overall evaluation curve of output with consistent with reference to estimating curve of output; And control action and inside and outside disturbance had the good dynamic response performance, FLC can further optimize according to the working control effect simultaneously.FLC realistic model after optimizing is at last generated the C code, be embedded into the working control device that makes it to become artificial ecological system in the single-chip microcomputer.
Claims (5)
1. be used for the fuzzy logic controller that the artificial ecological system operational process is regulated, it is characterized in that:
(1) no matter how complicated artificial ecological system inner structure and dynamic perfromance be, can on the basis of professional knowledge and expertise, set up fuzzy inference system, and realization is to the modeling of its fuzzy overall evaluation and fuzzy control strategy;
(2) operation conditions desirable according to artificial ecological system set up optimum fuzzy overall evaluation curve, is used for the optimization of Fuzzy Controller Parameters;
(3) numerical simulation with the MatLab/Simulink platform combines with the real-time semi-physical emulation technology of MatLab/Real-Time Workshop platform, accomplishes the numerical simulation design and the rapid prototyping design of fuzzy logic controller.
2. method according to claim 1; It is characterized in that: through the utilization fuzzy inference system; Can set up the control strategy and the model thereof of artificial ecological system according to professional knowledge and expertise fast, this method has advantages such as extremely strong dirigibility, adaptability and fault-tolerance.
3. method according to claim 1 is characterized in that: through the utilization fuzzy inference system, can also carry out Real-Time Evaluation to the operation conditions of artificial ecological system simultaneously, according to the size of each envirment factor of evaluation result feedback regulation.And according to the reference fuzzy overall evaluation curve that designs in advance, the method that the utilization dynamic process is optimized is optimized each parameter of fuzzy controller.
4. method according to claim 1, it is characterized in that: utilization MatLab/Simulink numerical simulation is carried out theoretical digitizing development, the validity of preliminary test control strategy and feasibility to fuzzy controller.
5. method according to claim 1; It is characterized in that: utilization MatLab/Real-Time Workshop real-time simulation is controlled the prototype of actual artificial ecological system; System is in the middle of the good running status, and control action and inside and outside disturbance are had the good dynamic response performance.Fuzzy controller is carried out the rapid prototyping design, realistic model is generated the C code, embed single-chip microcomputer and make it become effective controller of actual artificial ecological system operational process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012102910394A CN102799106A (en) | 2012-08-14 | 2012-08-14 | Fuzzy logic controller of artificial ecosystem |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012102910394A CN102799106A (en) | 2012-08-14 | 2012-08-14 | Fuzzy logic controller of artificial ecosystem |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102799106A true CN102799106A (en) | 2012-11-28 |
Family
ID=47198236
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012102910394A Pending CN102799106A (en) | 2012-08-14 | 2012-08-14 | Fuzzy logic controller of artificial ecosystem |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102799106A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103497013A (en) * | 2013-09-05 | 2014-01-08 | 福建省农业科学院农业生态研究所 | Fermentation treatment apparatus for waste in controlled airtight cabin |
| TWI643042B (en) * | 2017-07-06 | 2018-12-01 | 台灣松下電器股份有限公司 | Environmental simulation system, control unit and control method thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1324165A2 (en) * | 2001-12-28 | 2003-07-02 | Proteo S.p.A. | Automatic system for determining the optimum strategy for controlling a complex industry system in particular for managing water supply networks by means of an ecosystem model |
| CN1601411A (en) * | 2004-09-16 | 2005-03-30 | 上海交通大学 | Forecasting control method of ind procedue based on fuzzy target and fuzzy constraint |
| CN101859118A (en) * | 2010-06-01 | 2010-10-13 | 北京航空航天大学 | Robust controller for adjusting equilibrium of gases in small artificial ecosystem |
| CN102495919A (en) * | 2011-11-18 | 2012-06-13 | 华南农业大学 | Extraction method for influence factors of carbon exchange of ecosystem and system |
-
2012
- 2012-08-14 CN CN2012102910394A patent/CN102799106A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1324165A2 (en) * | 2001-12-28 | 2003-07-02 | Proteo S.p.A. | Automatic system for determining the optimum strategy for controlling a complex industry system in particular for managing water supply networks by means of an ecosystem model |
| CN1601411A (en) * | 2004-09-16 | 2005-03-30 | 上海交通大学 | Forecasting control method of ind procedue based on fuzzy target and fuzzy constraint |
| CN101859118A (en) * | 2010-06-01 | 2010-10-13 | 北京航空航天大学 | Robust controller for adjusting equilibrium of gases in small artificial ecosystem |
| CN102495919A (en) * | 2011-11-18 | 2012-06-13 | 华南农业大学 | Extraction method for influence factors of carbon exchange of ecosystem and system |
Non-Patent Citations (1)
| Title |
|---|
| 李艳超等: "《闭合生态系统中光生物反应器的模糊控制器研究》", 《中南大学学报（自然科学版）》, vol. 42, no. 1, 30 September 2011 (2011-09-30) * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103497013A (en) * | 2013-09-05 | 2014-01-08 | 福建省农业科学院农业生态研究所 | Fermentation treatment apparatus for waste in controlled airtight cabin |
| CN103497013B (en) * | 2013-09-05 | 2015-01-21 | 福建省农业科学院农业生态研究所 | Fermentation treatment apparatus for waste in controlled airtight cabin |
| TWI643042B (en) * | 2017-07-06 | 2018-12-01 | 台灣松下電器股份有限公司 | Environmental simulation system, control unit and control method thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Venkateswaran et al. | Synthetic biology for waste water to energy conversion: IOT and AI approaches | |
| Bernard et al. | Modelling of microalgae culture systems with applications to control and optimization | |
| Gomez et al. | DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis | |
| Celikovsky et al. | Singular perturbation based solution to optimal microalgal growth problem and its infinite time horizon analysis | |
| De Andrade et al. | Optimization of biomass production in outdoor tubular photobioreactors | |
| Pawlowski et al. | Event-based predictive control of pH in tubular photobioreactors | |
| Jayaraman et al. | Modeling and optimization of algae growth | |
| CN101859118B (en) | Robust controller for adjusting equilibrium of gases in small artificial ecosystem | |
| Fernández et al. | Hierarchical control for microalgae biomass production in photobiorreactors | |
| Amini et al. | An integrated growth kinetics and computational fluid dynamics model for the analysis of algal productivity in open raceway ponds | |
| Hu et al. | The design and optimization for light-algae bioreactor controller based on Artificial Neural Network-Model Predictive Control | |
| CN106950824A (en) | Stalk fermentation alcohol fuel process feeding prediction control system and method based on fuzzy neural network | |
| Yan et al. | Study on prediction model of dissolved oxygen about water quality monitoring system based on BP neural network | |
| Hu et al. | Design and optimization of photo bioreactor for O2 regulation and control by system dynamics and computer simulation | |
| CN102799106A (en) | Fuzzy logic controller of artificial ecosystem | |
| Pawlowski et al. | Event-based control systems for microalgae culture in industrial reactors | |
| Berk et al. | Synthesis water level control by fuzzy logic | |
| Hu et al. | Construction of closed integrative system for gases robust stabilization employing microalgae peculiarity and computer experiment | |
| CN117236650A (en) | Intelligent fluid dynamic adjustment method based on deep learning | |
| Stamatelatou et al. | An invariant manifold approach for CSTR model reduction in the presence of multi-step biochemical reaction schemes. Application to anaerobic digestion | |
| López-Pérez et al. | Improving bioethanol production via nonlinear controller with noisy measurements | |
| Hu et al. | Gas equilibrium regulation by closed-loop photo bioreactor built on system dynamics, fuzzy inference system and computer simulation | |
| CN102736540A (en) | Artificial neural network model reference servo controller for regulating microalgae growth rate | |
| Sangregorio-Soto et al. | Application of simultaneous dynamic optimization in the productivity of microalgae continuous culture | |
| CN103885468B (en) | A kind of method for designing based on the controller regulating gas balance in artificial closed ecological system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20121128 |