CN112827995B - PID self-tuning method for variable air volume ventilation cabinet surface air speed - Google Patents
PID self-tuning method for variable air volume ventilation cabinet surface air speed Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B08—CLEANING
- B08B—CLEANING IN GENERAL; PREVENTION OF FOULING IN GENERAL
- B08B15/00—Preventing escape of dirt or fumes from the area where they are produced; Collecting or removing dirt or fumes from that area
- B08B15/02—Preventing escape of dirt or fumes from the area where they are produced; Collecting or removing dirt or fumes from that area using chambers or hoods covering the area
- B08B15/023—Fume cabinets or cupboards, e.g. for laboratories
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The invention provides a PID self-tuning method for the wind speed of a variable air volume ventilation cabinet surface, which comprises the following steps: step S1, acquiring the surface wind speed value of the variable air volume fume hood and the door height value of the lifting door: acquiring a surface wind speed value through a wind speed sensor, and acquiring a door height value through a position sensor; step S2, establishing a motion mode for simulation, a mathematical evaluation model of ideal control effect and a particle motion model, and optimizing in the particle range on the basis to obtain an optimization result: and step S3, applying the optimizing result to a PID self-tuning system of the variable air volume ventilator surface air speed. The invention simulates the motion mode of the lifting door in actual use, and combines the PSO optimization algorithm to perform PID parameter self-tuning of machine learning, thereby reducing the labor cost and the tool cost, and reducing the influence of the system due to the difference of experience and technology of tuning personnel.
Description
Technical Field
The invention relates to the technical field of detection and control of a fume hood, in particular to a PID (proportion integration differentiation) self-tuning method for the surface wind speed of a variable air volume fume hood.
Background
The fume chamber is important safety equipment in laboratories such as chemistry, biology, and poisonous, harmful, the odorous gas that produces in the mainly used discharge experimentation, traditional fume chamber system is mostly simple mechanical structure, and the during operation air volume is fixed unchangeable, or through the size of artifical manual control amount of wind, can not reach good ventilation effect according to the concrete needs and the condition automatically regulated air volume of experiment operation, has certain influence to experimental environment's safety. The standards set by the american national standards institute indicate that generally speaking, too low a face wind speed of a fume hood is likely to cause insufficient control effect on the operating face pollutants, and the pollutants in the hood are likely to overflow; the surface wind speed is too high, air flow is easy to generate at the corners of the fume hood to cause the overflow of pollutants, and the surface wind speed of the fume hood is controlled within the range of 0.4-0.6 m/s. At present, the control of the wind speed of the surface of the ventilator cabinet generally adopts a PID (Proportional-Integral-Derivative) for control, but parameters of the PID need to be manually set, are greatly influenced by the level and experience of a setter and the external environment during application, and are generally difficult to obtain a good control effect.
Disclosure of Invention
The invention provides a PID (proportion integration differentiation) self-tuning method for the surface wind speed of a variable air volume ventilation cabinet, and aims to solve the technical problems that the control effect of automatically adjusting the ventilation volume of the ventilation cabinet is poor and the good ventilation effect cannot be achieved in the background technology.
In order to achieve the aim, the PID self-tuning method for the wind speed of the variable air volume ventilator surface provided by the invention comprises the following steps:
step S1, acquiring the surface wind speed value of the variable air volume fume hood and the door height value of the lifting door: acquiring a surface wind speed value through a wind speed sensor, and acquiring a door height value through a position sensor;
step S2, establishing a motion mode for simulation, a mathematical evaluation model of ideal control effect and a particle motion model, and optimizing in the particle range on the basis to obtain an optimization result:
the action mode comprises an action mode with stable surface wind speed value and high door response speed;
in the mathematical evaluation model of the ideal control effect, the ideal control effect requirement is the control effect requirement taking response time, stabilization time and control overshoot as fitness parameters;
the particle motion model is established based on particles with characteristics of surface wind speed value initial speed, surface wind speed value speed change, door height initial position and door height position change and corresponds to three PID parameter dimensions of response time, stabilization time and control overshoot;
the optimization in the particle range is the optimization of the opposite wind speed value and the gate height value;
and step S3, applying the optimizing result to the PID parameter for power supply.
Preferably, in step S2, the operation mode is specifically:
the stable requirement of the surface wind speed value is as follows: the surface wind speed value is stabilized at 0.5m/s under the working heights of 25%, 50% and 100% of the maximum value door height value;
the requirement that the door has high response speed is as follows: after the lifting door is in a closed state for 30s, the lifting door is pulled up to a working height of 50cm at a speed of 0.5m/s, the surface wind speed value reaches 0.45m/s, namely response time, and the response time is not more than 3 s;
the action mode specifically comprises the following steps: and (3) closing the lifting door for 30s by using a PID parameter set which can be stable at a relatively low door height position, stabilizing the wind speed value of the waiting surface to be 0.5m/s, pulling the lifting door to a relatively high working height position for 30s at a speed of 0.5m/s, closing the lifting door for 30s, and repeating the steps.
Preferably, in step S2, the mathematical evaluation model of the ideal control effect is specifically:
the mathematical evaluation model is shown in formula (1):
C＝Ts+0.5Tw+60(κ+0.04) (1)
wherein Ts is response time, Tw is stabilization time, and kappa is control overshoot;
in order to obtain the ideal control effect, the requirements of the mathematical evaluation model are as follows: the response time (Ts) and the steady time (Tw) are small, wherein the steady time (Tw) can be minimized by controlling the overshoot amount (kappa) to be less than 10% of the target air volume.
Preferably, in step S2, the particle motion model is a PSO particle motion model, specifically:
randomly distributing particle groups with the population size Np in a three-dimensional area, wherein the particles have the characteristics of initial positions, initial speeds, position change and speed change;
the expression of the particle velocity change characteristic after iteration is shown as formula (2):
υi＝ωυi-1+random(0.5,1)C0(Pbest-X)+random(0.5,1)C1(Gbest-X) (2)
in the formula (2), viIs the target speed after the change; omega is an inertia coefficient; upsilon isi-1The particle velocity for the previous generation; pbestHistorical optimal solutions for individuals are obtained; gbestThe global historical optimal solution is obtained; c0，C1Respectively representing individual self-credibility and community credibility; random gives the particles biological freedom; x is the current particle position;
the characteristic of the position change of the particles after iteration is represented by formula (3):
Xi＝Xi-1+υi (3)
x in the formula (3)iIs a target position; xi-1The particle position is unchanged.
Preferably, in the step S2, the optimizing for PSO in the particle range specifically includes the following steps:
step S21, initializing a three-dimensional particle swarm with a certain scale, and setting a population activity range, a population quantity and an individual speed boundary in a PSO algorithm; randomly generating an initial population position and an individual speed:
step S22, each particle executes a simulation action conforming to the action mode once, and records response time, stabilization time and control overshoot, and the fitness is obtained through the mathematical evaluation model of the ideal control effect;
step S23, repeating the step S22 to traverse the first generation particles, and recording the global optimal value and the individual optimal value;
step S24, repeating the step S22 and the step S23 through the particle flight rules to calculate the next generation of particles, updating the positions and the speeds of the particles based on the particle motion model, and recording the updated global optimal value and the updated individual optimal value;
and S25, repeating the step S24 until the iteration is finished, and obtaining a global optimal solution.
Preferably, the step S1 specifically includes the following steps:
s11, installing an air speed sensor, a position sensor, a lifting door system and an electric air valve with a controllable angle on the appearance of the fume hood, wherein the air speed sensor and the electric air valve with the controllable angle are used for collecting a measurement information sequence comprising a surface air speed value, and the lifting door system and the position sensor are used for collecting a measurement information sequence of a door height value;
s12, sending measurement commands to the wind speed sensor and the position sensor respectively through an STM32 single chip microcomputer and receiving returned measurement information sequences comprising a surface wind speed value and a door height value;
and step S13, calculating and converting the collected measurement information sequence into a unified measurement value, and storing the unified measurement value in a register for later use.
Preferably, the step S13 specifically includes the following steps:
step S131, conducting the numerical value stored in the register to a judger;
step S132, judging the information sequence type of the numerical value conducted in the step S131 through a judger, if the information sequence type is the measurement result of displacement or wind speed, returning the data information sequence to a register, extracting the numerical value of the data information sequence, conducting the extracted numerical value to a converter, and conducting the conversion of measurement and balance;
and step S133, calculating the wind speed value in m/S and the gate height value in cm by carrying out binary conversion and unit scaling on the data bit extracted in the step S132, and returning the conversion result to the register.
Preferably, step S3 is specifically: the optimization result of step S2 is led to the PID parameter and saved for direct use after power-up again.
The invention can obtain the following beneficial effects:
the automatic control system is based on the variable air volume control technology, simulates the movement mode of the lifting door in actual use, and combines the PSO optimization algorithm to perform machine learning PID parameter self-tuning, so that the requirement on hardware equipment is not increased generally, and a professional is not required to use an instrument to adjust parameters in a new environment, thereby reducing the labor cost and the tool cost; the PSO particle swarm optimization algorithm is used for optimizing, the obtained optimal value is smaller than the original manual setting and floating value under the same fitness function, and the influence of the system due to the experience and technical difference of setting personnel is reduced.
Drawings
Fig. 1 is a schematic diagram of a PID self-tuning method for wind speed of a variable air volume ventilator according to a preferred embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
Aiming at the existing problems, the invention provides a PID self-tuning method for the wind speed of a variable air volume ventilator cabinet, as shown in figure 1, the PID self-tuning method for the wind speed of the variable air volume ventilator cabinet comprises the following steps:
step S1, acquiring the surface wind speed value of the variable air volume fume hood and the door height value of the lifting door: acquiring a surface wind speed value through a wind speed sensor, and acquiring a door height value through a position sensor;
step S2, establishing a motion mode for simulation, a mathematical evaluation model of ideal control effect and a particle motion model, and optimizing in the particle range on the basis to obtain an optimization result:
the action mode comprises an action mode with stable surface wind speed value and high door response speed;
in the mathematical evaluation model of the ideal control effect, the ideal control effect requirement is the control effect requirement taking response time, stabilization time and control overshoot as fitness parameters;
the particle motion model is established based on particles with characteristics of surface wind speed value initial speed, surface wind speed value speed change, door height initial position and door height position change and corresponds to three PID parameter dimensions of response time, stabilization time and control overshoot;
the optimization in the particle range is the optimization of the opposite wind speed value and the gate height value;
and step S3, applying the optimizing result to the PID parameter for power supply.
The step S1 specifically includes the following steps:
s11, installing an air speed sensor, a position sensor, a lifting door system and an electric air valve with a controllable angle on the appearance of the fume hood, wherein the air speed sensor and the electric air valve with the controllable angle are used for collecting a measurement information sequence comprising a surface air speed value, and the lifting door system and the position sensor are used for collecting a measurement information sequence of a door height value;
s12, sending measurement commands to the wind speed sensor and the position sensor respectively through an STM32 single chip microcomputer and receiving returned measurement information sequences comprising a surface wind speed value and a door height value;
and step S13, calculating and converting the collected measurement information sequence into a unified measurement value, and storing the unified measurement value in a register for later use.
The step S13 specifically includes the following steps:
step S131, conducting the numerical value stored in the register to a judger;
step S132, judging the information sequence type of the numerical value conducted in the step S131 through a judger, if the information sequence type is the measurement result of displacement or wind speed, returning the data information sequence to a register, extracting the numerical value of the data information sequence, conducting the extracted numerical value to a converter, and conducting the conversion of measurement and balance;
and step S133, calculating the wind speed value in m/S and the gate height value in cm by carrying out binary conversion and unit scaling on the data bit extracted in the step S132, and returning the conversion result to the register.
In step S2, the operation mode specifically includes:
because the relevant VAV (Variable Air Volume, Variable Air Volume control technology) Air valve detection standard requirements are as follows: the surface wind speed value is stable. The stable requirement of the surface wind speed value is as follows: the surface wind speed value is stabilized at 0.5m/s under the working heights of 25%, 50% and 100% of the maximum value door height value;
the requirement that the door has high response speed is as follows: after the lifting door is in a closed state for 30s, the lifting door is pulled up to a working height of 50cm at a speed of 0.5m/s, the surface wind speed value reaches 0.45m/s, namely response time, and the response time is not more than 3 s;
and because the interference and tracing test requires the stabilization time to be as short as possible, the obtained simulation action mode is specifically as follows: and (3) closing the lifting door for 30s by using a PID parameter set which can be stable at a relatively low door height position, stabilizing the wind speed value of the waiting surface to be 0.5m/s, pulling the lifting door to a relatively high working height position for 30s at a speed of 0.5m/s, closing the lifting door for 30s, and repeating the steps.
The simulation process of the actual situation by adopting the action mode comprises the following steps:
s201: substituting the initial parameter combination, closing the lifting door for 30s, keeping the wind speed of the waiting surface to be stable near 0.5m/s, and pulling the lifting door up to the working height (50cm) at the speed of 0.5 m/s;
s202: using PID control, changing the opening of an air valve to maintain the surface air speed, and closing the lifting door after 20 s;
s203: and repeating S201 and S202 until the algorithm is ended.
In step S2, the mathematical evaluation model of the ideal control effect specifically includes:
the ideal control effect is as follows: the control effect is required to be small in response time (Ts) and stable time (Tw), and the stable time can be minimized by controlling the overshoot (kappa) to be less than 10% of the target air volume;
the mathematical evaluation model is shown in formula (1): :
C＝Ts+0.5Tw+60(κ+0.04) (1)
wherein Ts is the response time, Tw is the stabilization time, and κ is the control overshoot.
As can be seen from the detection standards, the response time (Ts) and the settling time (Tw) are required to be small for the control effect, and the settling time can be minimized by controlling the overshoot (k) to be less than 10% of the target air volume.
In order to obtain the ideal control effect, the requirements of the mathematical evaluation model are as follows: the response time (Ts) and the steady time (Tw) are small, wherein the steady time (Tw) can be minimized by controlling the overshoot amount (kappa) to be less than 10% of the target air volume.
In step S2, the particle motion model is a PSO particle motion model, and specifically includes:
randomly distributing particle groups with the population size Np in a three-dimensional area, wherein the particles have the characteristics of initial positions, initial speeds, position change and speed change;
the expression of the particle velocity change characteristic after iteration is shown as formula (2):
υi＝ωυi-1+random(0.5,1)C0(Pbest-X)+random(0.5,1)C1(Gbest-X) (2)
in the formula (2), viIs the target speed after the change; omega is an inertia coefficient; upsilon isi-1The particle velocity for the previous generation; pbestHistorical optimal solutions for individuals are obtained; gbestThe global historical optimal solution is obtained; c0，C1Respectively representing individual self-credibility and community credibility; random gives the particles biological freedom; x is the current particle position;
the characteristic of the position change of the particles after iteration is represented by formula (3):
Xi＝Xi-1+υi (3)
x in the formula (3)iIs a target position; xi-1The particle position is unchanged.
In the step S2, the optimizing in the particle range is PSO optimizing, which specifically includes the following steps:
step S21, initializing a three-dimensional particle swarm with a certain scale, and setting a population activity range, a population quantity and an individual speed boundary in a PSO algorithm; randomly generating an initial population position and an individual speed:
the population activity range refers to the data range of model training, and generally, the larger the range is, the longer the training time is. Not the response time. Tw is the stabilization time, kappa is the range of controlling overshoot, the population number is N, and the individual speed boundary is 0.4-0.6;
step S22, each particle executes a simulation action conforming to the action mode once, and records response time, stabilization time and control overshoot, and the fitness is obtained through the mathematical evaluation model of the ideal control effect;
step S23, repeating the step S22 to traverse the first generation particles, and recording the global optimal value and the individual optimal value;
step S24, repeating the step S22 and the step S23 through the particle flight rules to calculate the next generation of particles, updating the positions and the speeds of the particles based on the particle motion model, and recording the updated global optimal value and the updated individual optimal value;
the "particle flight rules" specifically refer to continuous training of the model according to the optimal solution direction.
And S25, repeating the step S24 until the iteration is finished, and obtaining a global optimal solution. After the iteration is finished, the overall motion trail of the population moves towards the direction of the overall optimal solution.
Step S3 specifically includes: the optimization result of step S2 is led to the PID parameter and saved for direct use after power-up again. Thus, the PID self-aligning method of the whole VAV fume hood system based on the STM32 single chip microcomputer is completed.
The invention can obtain the following beneficial effects:
the method is based on the VAV automatic control system, simulates the movement mode of the lifting door in actual use, combines the PSO optimization algorithm to perform machine learning PID parameter self-tuning, generally has no need of increasing hardware equipment, does not need professionals to use instruments to adjust parameters in a new environment, and reduces labor cost and tool cost; the PSO particle swarm optimization algorithm is used for optimizing, the obtained optimal value is smaller than the original manual setting and floating value under the same fitness function, and the influence of the system due to the experience and technical difference of setting personnel is reduced.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A PID self-tuning method for the wind speed of a variable air volume ventilator surface is characterized by comprising the following steps:
step S1, acquiring the surface wind speed value of the variable air volume fume hood and the door height value of the lifting door: acquiring a surface wind speed value through a wind speed sensor, and acquiring a door height value through a position sensor;
step S2, establishing a motion mode for simulation, a mathematical evaluation model of ideal control effect and a particle motion model, and optimizing in the particle range on the basis to obtain an optimization result:
the action mode comprises an action mode with stable surface wind speed value and high door response speed;
in the mathematical evaluation model of the ideal control effect, the ideal control effect requirement is the control effect requirement taking response time, stabilization time and control overshoot as fitness parameters;
the particle motion model is established based on particles with characteristics of surface wind speed value initial speed, surface wind speed value speed change, door height initial position and door height position change and corresponds to three PID parameter dimensions of response time, stabilization time and control overshoot;
the optimization in the particle range is the optimization of the opposite wind speed value and the gate height value;
and step S3, applying the optimizing result to the PID parameter for power supply.
2. The PID self-tuning method for the wind speed of the variable air volume ventilator according to claim 1, wherein in the step S2, the operation mode specifically comprises:
the stable requirement of the surface wind speed value is as follows: the surface wind speed value is stabilized at 0.5m/s under the working heights of 25%, 50% and 100% of the maximum door height value of the lifting door;
the requirement that the door has high response speed is as follows: after the lifting door is in a closed state for 30s, the lifting door is pulled up to a working height of 50cm at a speed of 0.5m/s, the surface wind speed value reaches 0.45m/s, namely response time, and the response time is not more than 3 s;
the action mode specifically comprises the following steps: and (3) closing the lifting door for 30s by using a PID parameter set which can be stable at a relatively low door height position, stabilizing the wind speed value of the waiting surface to be 0.5m/s, pulling the lifting door to a relatively high working height position for 30s at a speed of 0.5m/s, closing the lifting door for 30s, and repeating the steps.
3. The PID self-tuning method for the wind speed of the variable air volume ventilator cabinet according to claim 1, wherein in the step S2, the mathematical evaluation model of the ideal control effect is specifically:
the mathematical evaluation model is shown in formula (1):
C＝Ts+0.5Tw+60(κ+0.04) (1)
wherein Ts is response time, Tw is stabilization time, and kappa is control overshoot;
in order to obtain the ideal control effect, the requirements of the mathematical evaluation model are as follows: the response time (Ts) and the steady time (Tw) are small, wherein the steady time (Tw) can be minimized by controlling the overshoot amount (kappa) to be less than 10% of the target air volume.
4. The PID self-tuning method for the wind speed of the variable air volume ventilation cabinet according to claim 1, wherein in the step S2, the particle motion model is a PSO particle motion model, specifically:
randomly distributing particle groups with the population size Np in a three-dimensional area, wherein the particles have the characteristics of initial positions, initial speeds, position change and speed change;
the expression of the particle velocity change characteristic after iteration is shown as formula (2):
υi＝ωυi-1+random(0.5,1)C0(Pbest-X)+random(0.5,1)C1(Gbest-X) (2)
in the formula (2), viIs the target speed after the change; omega is an inertia coefficient; v. ofi-1The particle velocity for the previous generation; pbestHistorical optimal solutions for individuals are obtained; gbestThe global historical optimal solution is obtained; c0，C1Respectively representing individual self-credibility and community credibility; random gives the particles biological freedom; x is the current particle position;
the characteristic of the position change of the particles after iteration is represented by formula (3):
Xi＝Xi-1+vi (3)
x in the formula (3)iIs a target position; xi-1The particle position is unchanged.
5. The PID self-tuning method for the variable air volume ventilator face air speed according to claim 4, wherein in the step S2, the optimizing in the particle range is the PSO optimizing, and the method specifically comprises the following steps:
step S21, initializing a three-dimensional particle swarm with a certain scale, and setting a population activity range, a population quantity and an individual speed boundary in a PSO algorithm; randomly generating an initial population position and an individual speed:
step S22, each particle executes a simulation action conforming to the action mode once, and records response time, stabilization time and control overshoot, and the fitness is obtained through the mathematical evaluation model of the ideal control effect;
step S23, repeating the step S22 to traverse the first generation particles, and recording the global optimal value and the individual optimal value;
step S24, repeating the step S22 and the step S23 through the particle flight rules to calculate the next generation of particles, updating the positions and the speeds of the particles based on the particle motion model, and recording the updated global optimal value and the updated individual optimal value;
and S25, repeating the step S24 until the iteration is finished, and obtaining a global optimal solution.
6. The PID self-tuning method for the wind speed of the variable air volume ventilator cabinet according to claim 1, wherein the step S1 specifically comprises the following steps:
s11, installing an air speed sensor, a position sensor, a lifting door system and an electric air valve with a controllable angle on the appearance of the fume hood, wherein the air speed sensor and the electric air valve with the controllable angle are used for collecting a measurement information sequence comprising a surface air speed value, and the lifting door system and the position sensor are used for collecting a measurement information sequence of a door height value;
s12, sending measurement commands to the wind speed sensor and the position sensor respectively through an STM32 single chip microcomputer and receiving returned measurement information sequences comprising a surface wind speed value and a door height value;
and step S13, calculating and converting the collected measurement information sequence into a unified measurement value, and storing the unified measurement value in a register for later use.
7. The PID self-tuning method for the wind speed of the variable air volume ventilator cabinet according to claim 6, wherein the step S13 specifically comprises the following steps:
step S131, conducting the numerical value stored in the register to a judger;
step S132, judging the information sequence type of the numerical value conducted in the step S131 through a judger, if the information sequence type is the measurement result of the door height or the wind speed, returning the information sequence type to a register, extracting the numerical value, conducting the numerical value to a converter, and conducting the measurement and the balance conversion;
and step S133, calculating the wind speed value in m/S and the gate height value in cm by carrying out binary conversion and unit scaling on the data bit extracted in the step S132, and returning the conversion result to the register.
8. The PID self-tuning method for the wind speed of the variable air volume ventilator cabinet according to claim 1, wherein the step S3 is specifically as follows: the optimization result of step S2 is led to the PID parameter and saved for direct use after power-up again.
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| CN203791316U (en) * | 2014-04-14 | 2014-08-27 | 北京成威博瑞实验室设备有限公司 | Self-adapting variable air volume ventilation cabinet |
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