CN106354018A - Dissolved oxygen intelligent control system based on RBF (radial basis function) neural network - Google Patents

Abstract

The invention relates to a dissolved oxygen (DO) intelligent control system based on the RBF (radial basis function) neural network and belongs to the fields of water treatment and intelligent control. By building a control module, a hardware platform is built to form a set of complete DO intelligent control system; and DO can be accurately and more easily controlled in real time in a sewage treatment process. The DO intelligent control system based on the RBF neural network is applied to the sewage treatment process, and an electrically operated valve of an air blower is accurately controlled, so that accurate control on concentration of the DO is realized. The DO intelligent control system provided by the invention can be used for solving the problems that traditional switch control and PID control cannot realize stable and accurate control on a sewage treatment process with the characteristics such as high nonlinearity, strong coupling, time variance, large lag and serious uncertainty and realizes intelligent control on the concentration of the DO, and results show that the system provided by the invention realizes control on the concentration of the DO, and can improve sewage treatment effect and reduce energy consumption.

Description

Tank dissolved oxygen intelligent control system based on rbf neutral net

Technical field

The tank dissolved oxygen intelligent control system based on rbf neutral net is designed, by designing rbf neutral net control in the present invention Device processed completes building of control module, and forms a set of complete tank dissolved oxygen intelligent control system by building hardware platform； Achieve and the dissolved oxygen do being relatively difficult to real-time and accurate control in sewage disposal process is controlled.Will be based on rbf nerve The tank dissolved oxygen intelligent control system application sewage disposal process of network, by the accurate control of aerator electrically operated valve thus reality The now accurate control to dissolved oxygen do concentration.Dissolved oxygen intelligent control belongs to water treatment field, belongs to Based Intelligent Control again Field.

Background technology

In recent years, being gradually increased with China's urbanization, urban population also sharply increases, industry progressively develop thus Produce substantial amounts of sewage, China actively builds sewage treatment facility, the quick sewage treatment capacity promoting city and industrial scene, Simultaneously also up to standard to sewage disposal it is made that strict regulation.However, existing sewage treatment plant faces ensures that sewage disposal reaches Target serious challenge.Therefore, sewage treatment plant to need to improve emission compliance rate not high, pollutant levels remove and not etc. remain Major issue in sewage disposal process, the especially process to industrial wastewater.

Because dissolved oxygen plays an important role in sewage disposal process, the existing control skill of most of sewage treatment plant Art, such as on-off control, pid controls etc. has certain defect it is impossible to the features such as, multivariate non-linear to having, large time delay Dissolved oxygen do concentration in sewage disposal is preferably controlled.Sewage disposal mainly will by the metabolic function of microorganism Organic matter degradation is inorganic matters, thus realizing the removal to pollutant.The species of Organic substance has a lot, but its general character is exactly micro- Need under biodegradation to consume the dissolved oxygen in water, equally, the concentration of oxygen in water also directly influences the growth of microorganism. Keep suitable dissolved oxygen do concentration in Aerobic Pond, wastewater treatment efficiency is played with vital effect.

Intelligence control system mainly passes through to adjust the electronic of aerator in aerating system to controlling of dissolved oxygen do concentration Valve opening is adjusted, and has broken away from the manual regulation that sewage sewage treatment plant depends on artificial experience always, increased simultaneously The reliability adjusting, reduces the accidentalia thinking that factor causes.Compared to simple pid control system, time delay, Control fluctuation and the higher problem of the energy consumption that causes of excessive redundancy aspect, intelligence control system can effectively avoid.

The present invention relates to the design and research of the intelligence control system based on rbf nerve network controller, this control system System is based primarily upon rbf nerve network controller, and the system controlled quentity controlled variable of dissolved oxygen accurately being calculated and passing through to develop will Control signal is accurately delivered to actuator and completes to control, and solves artificial experience and traditional pid controls insoluble asking Topic.By build the hardware platforms such as data acquisition, data transfer and air compressor control and communication network achieve data acquisition, Transmission and the issuing and executing of control signal.By integrated to the exploitation of each functional module, form dissolved oxygen intelligent and control System, improves stability and the reliability of control, has ensured effluent quality simultaneously and has reduced consumption and reduce artificial The operating cost that factor is brought to interference and the operator of control process.

Content of the invention

The present invention obtains the intelligence control system based on rbf nerve network controller, devises the rbf nerve for controlling Network controller solves the control problem in sewage disposal process and is carried out modularized encapsulation；Build data acquisition, number According to process and storage and control function module hardware system it is ensured that the Effec-tive Function of system；Controlled by this system System, in sewage, dissolved oxygen concentration can reach most preferably, solves the problems, such as that in sewage disposal process, dissolved oxygen is difficult to precise control； Meanwhile, it is capable to provide abundant man machine interface, simplify control, adjust operation；Stability and the reality of sewage disposal process are ensured Show On-line Control；

Present invention employs following technical scheme and realize step:

Tank dissolved oxygen intelligent control system based on rbf neutral net

(1) tank dissolved oxygen intelligent control system design, the system is mainly by being provided in line traffic control based on rbf neural network algorithm Decision-making processed, to tackle the non-linear of sewage disposal process, big time-varying, large time delay and close coupling；The spy controlling for dissolved oxygen do Point, devises the hardware system of control system, including instrumentation, electrical equipment, data acquisition, data processing and storage, control The several main functional module of function modoularization processed, is implemented as follows:

Field instrument includes dissolved oxygen sensing instrument, temperature measuring set, ph measuring instrument and cod analyser and nh₄- n analyzes Instrument；Site Detection instrument is connected with plc, and plc is entered by rs232 and rs485 in fieldbus with memory module with data processing Row communication, data processing is connected by communication interface with control function module with memory module, and electrical equipment is mainly aerator And electrically operated valve, it is connected between electrical equipment and plc；After control function module provides control strategy online, will be controlled by plc Signal processed is issued to actuator electrically operated valve；

Tank dissolved oxygen intelligent control system based on rbf neutral net described in (1) as requested, lives for batch-type interval Property sludge system in dissolved oxygen do concentration be controlled, with aerator aeration rate as controlled quentity controlled variable, dissolved oxygen do concentration be controlled volume； It is characterized in that, comprise the following steps:

(1) the sewage disposal system forecast model based on rbf neutral net for the design, forecast model rbf neutral net is divided into Three layers: input layer, hidden layer and output layer；Forecast model rbf neutral net inputs as u (k)=[u₁(k),u₂(k)]^t, u₂(k) =u₁(k-1), u₁K () is k moment dissolved oxygen do concentration control amount, u₁(k-1) for the dissolved oxygen do concentration control amount in k-1 moment, t Transposition for matrix；Forecast model rbf neutral net is output as dissolved oxygen do concentration prediction value；Its calculation is as follows:

1. initialize forecast model rbf neutral net: determine the connected mode of neutral net 2-p-1, i.e. input layer nerve Unit is 2, and hidden layer neuron is p, and p is the positive integer more than 2；Output layer neuron is 1；Forecast model rbf nerve Network input layer to hidden layer connection weight be 1, hidden layer and output interlayer connection weight carry out in the range of [0,1] with Machine assignment；The output of neutral net is expressed as follows:

$y_m\left(k\right)=\underset{j=1}{\overset{p}{\mathrm{\σ}}}{w}_j\left(k\right)f_j\left(u\right(k\left)\right)---\left(1\right)$

Wherein, y_mK () is the output of k moment forecast model rbf neutral net, w_j(k) be j-th neuron of hidden layer and The connection weight of output layer, j=1,2 ..., p；f_jIt is the output of forecast model rbf neutral net j-th neuron of hidden layer, Its computing formula is:

$f_j\left(u\right(k\left)\right)=\exp (-|\left|u\right(k)-{\mathrm{\μ}}_j(k\left)\right||/{\mathrm{\σ}}_j^2(k\left)\right)---\left(2\right)$

Wherein, μ_jK () represents j-th neuronal center value of k moment hidden layer, σ_jK () represents j-th god of k moment hidden layer Through first center width；

2. performance indications j of forecast model rbf neutral net are defined_m(k)

$j_m\left(k\right)=\frac{1}{2}{e}_m^2\left(k\right)---\left(3\right)$

e_m(k)=y (k)-y_m(k)

(4)

Wherein, y (k) is the dissolved oxygen do concentration value of k moment actual measurement, e_mK () is k moment dissolved oxygen do concentration value Error；

3. the parameter of forecast model rbf neutral net is updated

${\mathrm{\δw}}_j\left(k\right)=\frac{\∂j_m\left(k\right)}{\∂w_j\left(k\right)}=-e_m\left(k\right)f_j\left(u\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(5\right)$

w_j(k+1)=w_j(k)-ηδw_j(k)=w_j(k)+ηe_m(k)f_j(u(k))(1-y(k))y(k) (6)

${\mathrm{\μ}}_j(k+1)={\mathrm{\μ}}_j\left(k\right)-\mathrm{\η}\frac{\∂j_m\left(k\right)}{\∂{\mathrm{\μ}}_j\left(k\right)}---\left(7\right)$

${\mathrm{\σ}}_j(k+1)={\mathrm{\σ}}_j\left(k\right)-\mathrm{\η}\frac{\∂j_m\left(k\right)}{\∂{\mathrm{\σ}}_j\left(k\right)}$

Wherein, δ w_jK () is the correction of j-th hidden layer neuron of k moment and output layer neuron connection weight, w_j K () is the connection weight of j-th hidden layer neuron of k moment and output layer neuron, w_j(k+1) it is to imply for j-th in the k+1 moment Layer neuron and the connection weight of output layer neuron, μ_j(k+1) j-th neuronal center value of k+1 moment hidden layer, σ are represented_j (k+1) represent the center width of k+1 j-th neuron of moment hidden layer；η is learning rate, η ∈ (0,1]；

4. judge the size of the object function of current time dissolved oxygen do prediction, if j_m(k) > 0.01, then repeat step ③；If j_mK () < 0.01, then go to the output y that 1. step calculates forecast model rbf neutral net_m(k)；

(2) it is designed for the rbf nerve network controller controlling；X (k)=[x₁(k),x₂(k)]^tFor rbf neutral net control The input of device processed, x₁K () is the error of k moment dissolved oxygen do concentration set point and actual value, x₂K () is that k moment dissolved oxygen do is dense Degree setting value and the rate of change of actual value error；

1. initialize rbf nerve network controller: determine the connected mode of neutral net 2-m-1, i.e. input layer For 2, hidden layer neuron is m, and m is the positive integer more than 2；Output layer neuron is 1；Rbf nerve network controller The connection weight of input layer to hidden layer is 1, and the connection weight of hidden layer and output interlayer is assigned in the range of [0,1] at random Value；The output of neutral net is expressed as follows:

$u\left(k\right)=\underset{i=1}{\overset{m}{\mathrm{\&sigma;}}}{w}_i^c\left(k\right)f_i\left(x\right(k\left)\right)---\left(9\right)$

Wherein, u (k) is the output of k moment rbf nerve network controller, w_i ^cK () implies for rbf nerve network controller I-th neuron of layer and the connection weight of output layer, i=1,2 ..., m；f_iIt is rbf i-th neuron of neutral net hidden layer Output, its computing formula is:

$f_i\left(x\right(k\left)\right)=\exp (-|\left|x\right(k)-{\mathrm{\&mu;}}_i^c(k\left)\right||/{\left({\mathrm{\&sigma;}}_i^c\left(k\right)\right)}^2)---\left(10\right)$

Wherein, μ_i ^cK () represents k i-th neuronal center value of moment rbf nerve network controller hidden layer, σ_i ^c(k) table Show the center width of k i-th neuron of moment rbf nerve network controller hidden layer；

2. define index j of rbf nerve network controller_c(k)

$j_c\left(k\right)=\frac{1}{2}{e}^2\left(k\right)---\left(11\right)$

E (k)=r (k)-y (k)

(12)

Wherein, e (k) is the error of k moment dissolved oxygen do concentration, and r (k) is k moment dissolved oxygen do concentration set point；

3. the parameter of rbf nerve network controller is updated

${\mathrm{\&delta;w}}_i^c\left(k\right)=\frac{\&part;j_c\left(k\right)}{\&part;w_i^c\left(k\right)}=-e\left(k\right)f\left(x\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(13\right)$

$w_i^c(k+1)=w_i^c\left(k\right)-{\mathrm{\&eta;}}_1{\mathrm{\&delta;w}}_i^c\left(k\right)=w_i^c\left(k\right)+{\mathrm{\&eta;}}_{1}e\left(k\right)f\left(x\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(14\right)$

${\mathrm{\&mu;}}_i^c(k+1)={\mathrm{\&mu;}}_i^c\left(k\right)-{\mathrm{\&eta;}}_1\frac{\&part;j_c\left(k\right)}{\&part;{\mathrm{\&mu;}}_i^c\left(k\right)}---\left(15\right)$

${\mathrm{\&sigma;}}_i^c(k+1)={\mathrm{\&sigma;}}_i^c\left(k\right)-{\mathrm{\&eta;}}_1\frac{\&part;j_c\left(k\right)}{\&part;{\mathrm{\&sigma;}}_i^c\left(k\right)}---\left(16\right)$

Wherein, δ w_i ^cK () is k i-th neuron of moment rbf nerve network controller hidden layer and output layer nerve Rhizoma Coptidis Meet the correction of weights, w_i ^c(k+1) rbf nerve network controller hidden layer the i-th neuron for the k+1 moment and output layer nerve The connection weight of unit；μ_i ^c(k+1) k+1 i-th neuronal center value of moment rbf nerve network controller hidden layer, σ are represented_i ^c(k + 1) represent the center width of k+1 i-th neuron of moment rbf nerve network controller hidden layer；η₁For learning rate, η₁∈(0, 1]；

4. judge the size of the object function of current time dissolved oxygen do, if j_c(k) > 0.01, then repeat step is 3.；As Fruit j_cK () < 0.01, then go to output u (k) that 1. step calculates rbf nerve network controller；

(3) using the u (k) solving, dissolved oxygen do is controlled, u (k) is controlled quentity controlled variable for the k moment for aeration rate, control System processed is output as the concentration value of actual dissolved oxygen do.

The creativeness of the present invention is mainly reflected in:

(1) present invention is a mistake with the features such as non-linear, close coupling, big time-varying for current sewage disposal process Journey, needs to control dissolved oxygen do concentration in a rational scope, but according to the existing control method of sewage treatment plant, difficult To realize stable and to be accurately controlled；Very strong self adaptation and self-learning capability are had according to neutral net, devises rbf nerve Network Prediction Model and rbf nerve network controller, it is achieved that the On-line Control of dissolved oxygen, have good stability, real-time is good And control accuracy high the features such as；

(2) present invention devises rbf neural network prediction model and rbf nerve network controller, and control method is preferably Solve the unmanageable problem of nonlinear system it is achieved that the real-time precise control of dissolved oxygen concentration；Solve the dirt of complexity Water treatment procedure only relies on solution artificial experience and realizes control problem, has the features such as energy consumption is low, and structure is simple；

Brief description

Fig. 1 is control system architecture figure of the present invention

Fig. 2 is control system model figure of the present invention

Fig. 3 is neural net model establishing of the present invention and controller architecture figure

Fig. 4 is rbf neutral net network structure of the present invention

Fig. 5 is control system dissolved oxygen do concentration results figure of the present invention

Fig. 6 is control system dissolved oxygen do concentration error figure of the present invention

Specific embodiment

The present invention obtains tank dissolved oxygen intelligent control system based on rbf neutral net it is achieved that molten in sewage disposal process The precise control of solution oxygen do concentration；It is controlled by this system, in sewage, dissolved oxygen concentration can reach most preferably, solves dirt In water treatment procedure, dissolved oxygen is difficult to the problem of precise control, improves the precision of dissolved oxygen do concentration control；Meanwhile, simplify The operating process controlling is it is achieved that automatic on-line controls；

Present invention employs following technical scheme and realize step:

(1) tank dissolved oxygen intelligent control system design, the system is mainly by being provided in line traffic control based on rbf neural network algorithm Decision-making processed devises the hardware system of control system, and such as Fig. 1 gives the structure chart of control system, including instrumentation, electrically Equipment, data acquisition, data processing and storage, the several main functional module of control function modularity；

Field instrument includes dissolved oxygen sensing instrument, temperature measuring set, ph measuring instrument and cod analyser and nh₄- n analyzes Instrument；Site Detection instrument is connected with plc, and plc is carried out by fieldbus rs232 and rs485 with memory module with data processing Communication, data processing is connected by communication interface with control function module with memory module, electrical equipment mainly aerator with And electrically operated valve, it is connected between electrical equipment and plc；After control function module provides control strategy online, will be controlled by plc Signal is issued to actuator electrically operated valve；

(2) in Control System Design, design rbf neural network prediction model and design rbf nerve network controller are embedding Enter in control module, provide control strategy online, give the model of control system in Fig. 2, simply describe control system The basic function possessing.

Present invention obtains a kind of neutral net dissolved oxygen do concentration control method based on gradient descent algorithm it is achieved that The precise control of dissolved oxygen do concentration in sewage disposal process；The method is the method by being declined based on data-driven and gradient Solve the control problem in sewage disposal process；After being controlled by the method, in sewage, dissolved oxygen concentration can reach Good, solve the problems, such as that in sewage disposal process, dissolved oxygen is difficult to precise control, improve the precision of dissolved oxygen do concentration control； Meanwhile, ensure the stability of sewage disposal process and achieve On-line Control；

Present invention employs following technical scheme and realize step:

A kind of dissolved oxygen accuracy control method based on rbf neutral net,

It is controlled for dissolved oxygen do concentration in batch-type interval activated Sludge System, with aerator aeration rate for controlling Amount, dissolved oxygen do concentration is controlled volume, control structure figure such as Fig. 3；

(1) the sewage disposal system forecast model based on rbf neutral net for the design, forecast model rbf neutral net is divided into Three layers: input layer, hidden layer and output layer；Prediction mould rbf neutral net inputs as u (k)=[u₁(k),u₂(k)]^t, u₂(k)= u₁(k-1), u₁K () is k moment dissolved oxygen do concentration control amount, u₁(k-1) for the dissolved oxygen do concentration control amount in k-1 moment, t is The transposition of matrix；Forecast model rbf neutral net is output as dissolved oxygen do concentration prediction value；Its calculation is as follows:

1. initialize forecast model rbf neutral net: determine the connected mode of neutral net 2-p-1, i.e. input layer nerve Unit is 2, and hidden layer neuron is 15 for p；Output layer neuron is 1；Forecast model rbf neural network input layer is to hidden Connection weight containing layer is 1, and the connection weight of hidden layer and output interlayer carries out random assignment in the range of [0,1]；Nerve net The output of network is expressed as follows:

$y_m\left(k\right)=\underset{j=1}{\overset{p}{\mathrm{\&sigma;}}}{w}_j\left(k\right)f_j\left(u\right(k\left)\right)---\left(17\right)$

Wherein, y_mK () is the output of k moment forecast model rbf neutral net, w_j(k) be j-th neuron of hidden layer and The connection weight of output layer, j=1,2 ..., p；f_jIt is the output of forecast model rbf neutral net j-th neuron of hidden layer, Its computing formula is:

$f_j\left(u\right(k\left)\right)=\exp (-|\left|u\right(k)-{\mathrm{\&mu;}}_j(k\left)\right||/{\mathrm{\&sigma;}}_j^2(k\left)\right)---\left(18\right)$

Wherein, μ_jK () represents j-th neuronal center value of k moment hidden layer, σ_jK () represents j-th god of k moment hidden layer Through first center width；

2. performance indications j of forecast model rbf neutral net are defined_m(k)

$j_m\left(k\right)=\frac{1}{2}{e}_m^2\left(k\right)---\left(19\right)$

e_m(k)=y (k)-y_m(k)

(20)

Wherein, y (k) is the dissolved oxygen do concentration value of k moment actual measurement, e_mK () is k moment dissolved oxygen do concentration value Error；

3. the parameter of forecast model rbf neutral net is updated

${\mathrm{\&delta;w}}_j\left(k\right)=\frac{\&part;j_m\left(k\right)}{\&part;w_j\left(k\right)}=-e_m\left(k\right)f_j\left(u\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(21\right)$

w_j(k+1)=w_j(k)-ηδw_j(k)=w_j(k)+ηe_m(k)f_j(u(k))(1-y(k))y(k) (22)

${\mathrm{\&mu;}}_j(k+1)={\mathrm{\&mu;}}_j\left(k\right)-\mathrm{\&eta;}\frac{\&part;j_m\left(k\right)}{\&part;{\mathrm{\&mu;}}_j\left(k\right)}---\left(23\right)$

${\mathrm{\&sigma;}}_j(k+1)={\mathrm{\&sigma;}}_j\left(k\right)-\mathrm{\&eta;}\frac{\&part;j_m\left(k\right)}{\&part;{\mathrm{\&sigma;}}_j\left(k\right)}---\left(24\right)$

Wherein, δ w_jK () is the correction of j-th hidden layer neuron of k moment and output layer neuron connection weight, w_j K () is the connection weight of j-th hidden layer neuron of k moment and output layer neuron, w_j(k+1) it is to imply for j-th in the k+1 moment Layer neuron and the connection weight of output layer neuron, μ_j(k+1) j-th neuronal center value of k+1 moment hidden layer, σ are represented_j (k+1) represent the center width of k+1 j-th neuron of moment hidden layer；Learning rate η=0.1；

4. judge the size of the object function of current time dissolved oxygen do prediction, if j_m(k) > 0.01, then repeat step ③；If j_mK () < 0.01, then go to the output y that 1. step calculates forecast model rbf neutral net_m(k)；

(2) it is designed for the rbf nerve network controller controlling；X (k)=[x₁(k),x₂(k)]^tFor rbf neutral net control The input of device processed, x₁K () is the error of k moment dissolved oxygen do concentration set point and actual value, x₂K () is that k moment dissolved oxygen do is dense Degree setting value and the rate of change of actual value error；

1. initialize rbf nerve network controller: determine the connected mode of neutral net 2-m-1, i.e. input layer For 2, hidden layer neuron is 17 for m；Output layer neuron is 1；Rbf nerve network controller input layer is to hidden layer Connection weight be 1, hidden layer and output interlayer connection weight carry out random assignment in the range of [0,1]；Neutral net Output is expressed as follows:

$u\left(k\right)=\underset{i=1}{\overset{m}{\mathrm{\&sigma;}}}{w}_i^c\left(k\right)f_i\left(x\right(k\left)\right)---\left(25\right)$

Wherein, u (k) is the output of k moment rbf nerve network controller, w_i ^cK () implies for rbf nerve network controller I-th neuron of layer and the connection weight of output layer, i=1,2 ..., m；f_iIt is rbf i-th neuron of neutral net hidden layer Output, its computing formula is:

$f_i\left(x\right(k\left)\right)=\exp (-|\left|x\right(k)-{\mathrm{\&mu;}}_i^c(k\left)\right||/{\left({\mathrm{\&sigma;}}_i^c\left(k\right)\right)}^2)---\left(26\right)$

Wherein, μ_i ^cK () represents k i-th neuronal center value of moment rbf nerve network controller hidden layer, σ_i ^c(k) table Show the center width of k i-th neuron of moment rbf nerve network controller hidden layer；

2. define index j of rbf nerve network controller_c(k)

$j_c\left(k\right)=\frac{1}{2}{e}^2\left(k\right)---\left(27\right)$

E (k)=r (k)-y (k)

(28)

Wherein, e (k) is the error of k moment dissolved oxygen do concentration, and r (k) is k moment dissolved oxygen do concentration set point；

3. the parameter of rbf nerve network controller is updated

${\mathrm{\&delta;w}}_i^c\left(k\right)=\frac{\&part;j_c\left(k\right)}{\&part;w_i^c\left(k\right)}=-e\left(k\right)f\left(x\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(29\right)$

$w_i^c(k+1)=w_i^c\left(k\right)-{\mathrm{\&eta;}}_1{\mathrm{\&delta;w}}_i^c\left(k\right)=w_i^c\left(k\right)+{\mathrm{\&eta;}}_{1}e\left(k\right)f\left(x\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(30\right)$

${\mathrm{\&mu;}}_i^c(k+1)={\mathrm{\&mu;}}_i^c\left(k\right)-{\mathrm{\&eta;}}_1\frac{\&part;j_c\left(k\right)}{\&part;{\mathrm{\&mu;}}_i^c\left(k\right)}---\left(31\right)$

${\mathrm{\&sigma;}}_i^c(k+1)={\mathrm{\&sigma;}}_i^c\left(k\right)-{\mathrm{\&eta;}}_1\frac{\&part;j_c\left(k\right)}{\&part;{\mathrm{\&sigma;}}_i^c\left(k\right)}---\left(32\right)$

Wherein, δ w_i ^cK () is k i-th neuron of moment rbf nerve network controller hidden layer and output layer nerve Rhizoma Coptidis Meet the correction of weights, w_i ^c(k+1) rbf nerve network controller hidden layer the i-th neuron for the k+1 moment and output layer nerve The connection weight of unit；μ_i ^c(k+1) k+1 i-th neuronal center value of moment rbf nerve network controller hidden layer, σ are represented_i ^c(k + 1) represent the center width of k+1 i-th neuron of moment rbf nerve network controller hidden layer；Learning rate, η₁=0.1；

4. judge the size of the object function of current time dissolved oxygen do, if j_c(k) > 0.01, then repeat step is 3.；As Fruit j_cK () < 0.01, then go to output u (k) that 1. step calculates rbf nerve network controller；

(3) using the u (k) solving, dissolved oxygen do is controlled, u (k) is controlled quentity controlled variable for the k moment for aeration rate, control System processed is output as the concentration value of actual dissolved oxygen do；The dissolved oxygen do concentration value of Fig. 5 display system, x-axis: time, unit It is 15 minutes/sample, y-axis: dissolved oxygen do concentration, unit is mg/litre, and solid line is expectation dissolved oxygen do concentration value, and dotted line is Actual dissolved oxygen do exports concentration value；Reality output dissolved oxygen do concentration and the error such as Fig. 6 expecting dissolved oxygen do concentration, x-axis: Time, unit is 15 minutes/sample, y-axis: dissolved oxygen do concentration error value, unit is mg/litre, and result proves the method Effectiveness.

Claims (1)

1. the tank dissolved oxygen intelligent control system based on rbf neutral net it is characterised in that:

It is several that hardware includes instrumentation, electrical equipment, data acquisition, data processing and memory module, control function modularity Main functional module, is implemented as follows:

Field instrument includes dissolved oxygen sensing instrument, temperature measuring set, ph measuring instrument and cod analyser and nh₄- n analyser；Existing Field instrumentation is connected with plc, and plc is led to by rs232 and rs485 in fieldbus with memory module with data processing News, data processing is connected by communication interface with control function module with memory module, and electrical equipment includes aerator and electricity Movable valve, is connected between electrical equipment and plc；After control function module provides control strategy online, by plc by control signal It is issued to actuator electrically operated valve；

Dissolved oxygen do concentration in batch-type interval activated Sludge System is controlled, with aerator aeration rate as controlled quentity controlled variable, molten Solution oxygen do concentration is controlled volume；

Comprise the following steps:

(2) the sewage disposal system forecast model based on rbf neutral net for the design, forecast model rbf neutral net is divided into three layers: Input layer, hidden layer and output layer；Forecast model rbf neutral net inputs as u (k)=[u₁(k),u₂(k)]^t, u₂(k)=u₁ (k-1), u₁K () is k moment dissolved oxygen do concentration control amount, u₁(k-1) for the dissolved oxygen do concentration control amount in k-1 moment, t is square The transposition of battle array；Forecast model rbf neutral net is output as dissolved oxygen do concentration prediction value；Its calculation is as follows:

1. initialize forecast model rbf neutral net: determine the connected mode of neutral net 2-p-1, that is, input layer is 2 Individual, hidden layer neuron is p, and p is the positive integer more than 2；Output layer neuron is 1；Forecast model rbf neutral net is defeated The connection weight entering layer to hidden layer is 1, and the connection weight of hidden layer and output interlayer is assigned in the range of [0,1] at random Value；The output of neutral net is expressed as follows:

$y_m\left(k\right)=\underset{j=1}{\overset{p}{\&sigma;}}{w}_j\left(k\right)f_j\left(u\right(k\left)\right)---\left(1\right)$

Wherein, y_mK () is the output of k moment forecast model rbf neutral net, w_jK () is j-th neuron of hidden layer and output The connection weight of layer, j=1,2 ..., p；f_jIt is the output of forecast model rbf neutral net j-th neuron of hidden layer, its meter Calculating formula is:

$f_j\left(u\right(k\left)\right)=\exp (-|\left|u\right(k)-{\mathrm{\&mu;}}_j(k\left)\right||/{\mathrm{\&sigma;}}_j^2(k\left)\right)---\left(2\right)$

Wherein, μ_jK () represents j-th neuronal center value of k moment hidden layer, σ_jK () represents k j-th neuron of moment hidden layer Center width；

2. performance indications j of forecast model rbf neutral net are defined_m(k)

$j_m\left(k\right)=\frac{1}{2}{e}_m^2\left(k\right)---\left(3\right)$

e_m(k)=y (k)-y_m(k) (4)

Wherein, y (k) is the dissolved oxygen do concentration value of k moment actual measurement, e_mK () is the error of k moment dissolved oxygen do concentration value；

3. the parameter of forecast model rbf neutral net is updated

${\mathrm{\&delta;w}}_j\left(k\right)=\frac{\&part;j_m\left(k\right)}{\&part;w_j\left(k\right)}=-e_m\left(k\right)f_j\left(u\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(5\right)$

w_j(k+1)=w_j(k)-ηδw_j(k)=w_j(k)+ηe_m(k)f_j(u(k))(1-y(k))y(k) (6)

$\begin{array}{cc}{\mathrm{\&mu;}}_j(k+1)={\mathrm{\&mu;}}_j\left(k\right)-\mathrm{\&eta;}\frac{\&part;j_m\left(k\right)}{\&part;{\mathrm{\&mu;}}_j\left(k\right)}---\left(7\right)& {\mathrm{\&sigma;}}_j(k+1)={\mathrm{\&sigma;}}_j\left(k\right)-\mathrm{\&eta;}\frac{\&part;j_m\left(k\right)}{\&part;{\mathrm{\&sigma;}}_j\left(k\right)}\end{array}---\left(8\right)$

Wherein, δ w_jK () is the correction of j-th hidden layer neuron of k moment and output layer neuron connection weight, w_jK () is J-th hidden layer neuron of k moment and the connection weight of output layer neuron, w_j(k+1) it is j-th hidden layer god of k+1 moment Connection weight through unit and output layer neuron, μ_j(k+1) j-th neuronal center value of k+1 moment hidden layer, σ are represented_j(k+1) Represent the center width of k+1 j-th neuron of moment hidden layer；η is learning rate, η ∈ (0,1]；

4. judge the size of the object function of current time dissolved oxygen do prediction, if j_m(k) > 0.01, then repeat step is 3.；As Fruit j_mK () < 0.01, then go to the output y that 1. step calculates forecast model rbf neutral net_m(k)；

(3) it is designed for the rbf nerve network controller controlling；X (k)=[x₁(k),x₂(k)]^tFor rbf nerve network controller Input, x₁K () is the error of k moment dissolved oxygen do concentration set point and actual value, x₂K () sets for k moment dissolved oxygen do concentration Definite value and the rate of change of actual value error；

1. initialize rbf nerve network controller: determine the connected mode of neutral net 2-m-1, that is, input layer is 2 Individual, hidden layer neuron is m, and m is the positive integer more than 2；Output layer neuron is 1；Rbf nerve network controller inputs The connection weight of layer to hidden layer is 1, and the connection weight of hidden layer and output interlayer carries out random assignment in the range of [0,1]； The output of neutral net is expressed as follows:

$u\left(k\right)=\underset{i=1}{\overset{m}{\&sigma;}}{w}_i^c\left(k\right)f\left(x\right(k\left)\right)---\left(9\right)$

Wherein, u (k) is the output of k moment rbf nerve network controller, w_i ^cK () is rbf nerve network controller hidden layer i-th Individual neuron and the connection weight of output layer, i=1,2 ..., m；f_iIt is the defeated of rbf neutral net i-th neuron of hidden layer Go out, its computing formula is:

$f_i\left(x\right(k\left)\right)=\exp (-|\left|x\right(k)-{\mathrm{\&mu;}}_i^c(k\left)\right||/{\left({\mathrm{\&sigma;}}_i^c\left(k\right)\right)}^2)---\left(10\right)$

Wherein, μ_i ^cK () represents k i-th neuronal center value of moment rbf nerve network controller hidden layer, σ_i ^cWhen () represents k k Carve the center width of rbf nerve network controller i-th neuron of hidden layer；

2. define index j of rbf nerve network controller_c(k)

$j_c\left(k\right)=\frac{1}{2}{e}^2\left(k\right)---\left(11\right)$

E (k)=r (k)-y (k) (12)

Wherein, e (k) is the error of k moment dissolved oxygen do concentration, and r (k) is k moment dissolved oxygen do concentration set point；

3. the parameter of rbf nerve network controller is updated

${\mathrm{\&delta;w}}_i^c\left(k\right)=\frac{\&part;j_c\left(k\right)}{\&part;w_i^c\left(k\right)}=-e\left(k\right)f\left(x\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(13\right)$

$w_i^c(k+1)=w_i^c\left(k\right)-{\mathrm{\&eta;}}_1{\mathrm{\&delta;w}}_i^c\left(k\right)=w_i^c\left(k\right)+{\mathrm{\&eta;}}_{1}e\left(k\right)f\left(x\right(k\left)\right)(1-y(k\left)\right)y\left(k\right)---\left(14\right)$

${\mathrm{\&mu;}}_i^c(k+1)={\mathrm{\&mu;}}_i^c\left(k\right)-{\mathrm{\&eta;}}_1\frac{\&part;j_c\left(k\right)}{\&part;{\mathrm{\&mu;}}_i^c\left(k\right)}---\left(15\right)$

${\mathrm{\&sigma;}}_i^c(k+1)={\mathrm{\&sigma;}}_i^c\left(k\right)-{\mathrm{\&eta;}}_1\frac{\&part;j_c\left(k\right)}{\&part;{\mathrm{\&sigma;}}_i^c\left(k\right)}---\left(16\right)$

Wherein, δ w_i ^cK () is k i-th neuron of moment rbf nerve network controller hidden layer and output layer neuron connection weight The correction of value, w_i ^c(k+1) rbf nerve network controller hidden layer the i-th neuron for the k+1 moment and output layer neuron Connection weight；μ_i ^c(k+1) k+1 i-th neuronal center value of moment rbf nerve network controller hidden layer, σ are represented_i ^c(k+1) Represent the center width of k+1 i-th neuron of moment rbf nerve network controller hidden layer；η₁For learning rate, η₁∈(0,1]；

4. judge the size of the object function of current time dissolved oxygen do, if j_c(k) > 0.01, then repeat step is 3.；If j_c K () < 0.01, then go to output u (k) that 1. step calculates rbf nerve network controller；

(4) using the u (k) solving, dissolved oxygen do is controlled, u (k) is controlled quentity controlled variable for the k moment for aeration rate, controls system System is output as the concentration value of actual dissolved oxygen do.