CN108121208A - Based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods - Google Patents
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- 238000005457 optimization Methods 0.000 title claims abstract description 45
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention discloses one kind to be based on PSO ABFO reheat steam temperature PID controller parameter optimization methods, first by the way that the thought of particle cluster algorithm is introduced into normal bacterial foraging algorithm, utilize the travelling step-length of adaptive step stragetic innovation bacterium, again by reheated steam temperature system as the control object of optimal controller, obtain object function, optimizing is scanned for controller parameter, finally the PID controller optimized is applied in Reheated-steam Temperature Control System.This method is on the basis of normal bacterial foraging algorithm, pass through optimizing strategy to bacterium, the redefining of health degree, the improvement of migration strategy and the application of adaptive step strategy, convergence speed of the algorithm and optimization precision is made all to improve a lot, and the parameter optimization method invented is applied in reheated steam temperature system, be well controlled effect, finally significantly increases robustness.
Description
Technical field
The present invention relates to information control technology fields, and PSO-ABFO reheat steam temperature PID controls are based on more particularly to one kind
Device parameter optimization method.
Background technology
By the end of the end of the year in 2016, China's capacity of installed generator was 16.5 hundred million kilowatts, and thermoelectricity installed capacity station total installed capacity holds
The 63.7% of amount, thermal power generation is still the main forms of electricity generation in China, enters new normality however as economy, in face of resource and
The double constraints of environment, the situation that Thermal Power Generation Industry faces are more and more severeer.Reheated-steam Temperature Control System is fired power generating unit control system
The important component of system, the quality of controller design are related to the power generating quality even thermal power unit operation of reheated steam temperature system
Safety is that most of the important content of fired power generating unit design of temperature control system, current Reheated-steam Temperature Control System uses PID
Control technology, with the development of automatic control technology, the stability to be met to fired power generating unit controller and robust performance requirement
Higher and higher, the requirement to controller parameter optimization method is also increasingly increased.
Previous classic optimisation algorithm is all will far from satisfaction in calculating speed, convergence, initial value sensitivity etc.
It asks, the powerless optimization problem for solving to become increasingly complex in practical application.With the generation of various intelligent optimization methods, bacterium looks for
Food algorithm has also obtained good development, and many scholars study it, while achieving certain achievement, also greatly promote
Application of the algorithm in engineering field.
Although also there are some solutions that normal bacterial foraging algorithm is applied to the optimization of heat power engineering system PID controller parameter at present
Certainly scheme, but normal bacterial foraging algorithm is used, convergence rate is slow, and optimization precision is not high, and PID controller parameter is caused to optimize
The robustness of method is relatively low.
The content of the invention
For this purpose, one embodiment of the present of invention proposes that a kind of PSO-ABFO reheat steam temperatures PID controller parameter that is based on optimizes
Method solves the problem of that the slow optimization precision of prior art convergence rate is not high, to promote robustness.
It is according to an embodiment of the invention to be based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods, including：
The PID controller parameter feasible using each group is as a bacterium position, using improved adaptive based on population
Bacterial foraging algorithm is answered to search out optimal PID controller parameter, wherein, it is described to be looked for based on the improved adaptive bacterium of population
Food algorithm comprises the following steps：
Step A, the parameter initialization of algorithm set bacterial population size, and optimized variable dimension, bacterium duplication, chemotactic move
Move maximum times, random initializtion flora position；
Step B, cycling character iterations add 1；
Step C, replication cycle number add 1；
Step D, chemotactic cycle-index add 1；
Step E, calculates the fitness of initial flora, and records the position of the initial optimal bacterium of group, and it is thin to calculate each
The final fitness of bacterium, and go out bacterium reverses direction and travelling step-length respectively；
Step F, more novel bacteria position simultaneously calculate the fitness of bacterium under new position, judge whether to continue to swim in same direction
Dynamic, if the fitness of bacterium is improved under new position, bacterium continues to move about in same direction, more novel bacteria position and adaptation
Degree, until fitness no longer improves or reach maximum steps of random walk, the chemotactic into next bacterium operates；
Step G after chemotactic operates, into bacterium duplicate stage, calculates the health degree of each bacterium, by bacterium according to
Health degree is ranked up, and the selection of the survival of the fittest of bacterium is carried out according to order, the poor a part of bacterium of health degree is eliminated,
The duplication that the higher bacterium of health degree is divided into two keeps total number of bacteria constant, and it is follow-on to be then transferred to step C progress
Chemotactic operates；
Step H, after flora reaches maximum number of copy times, into migration phase；
Step I for each bacterium, randomly generates a random number, right when this random number is more than the probability of setting
Bacterium carries out migration operation；Otherwise, bacterium will keep original position to continue optimizing, be then transferred to step B, carry out the chemotactic of a new round
And duplication；
Step H when migrating number more than maximum transport number, exports optimum results, and algorithm terminates.
It is according to embodiments of the present invention based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods, pass through first
The thought of particle cluster algorithm is introduced into normal bacterial foraging algorithm, is walked using the travelling of adaptive step stragetic innovation bacterium
It is long, then reheated steam temperature system is obtained object function, controller parameter is scanned for as the control object of optimal controller
The PID controller optimized is finally applied in Reheated-steam Temperature Control System by optimizing.This method is in normal bacterial foraging algorithm
On the basis of, pass through optimizing strategy to bacterium, the redefining of health degree, the improvement of migration strategy and adaptive step plan
Application slightly makes convergence speed of the algorithm and optimization precision all improve a lot, and the parameter optimization method application invented
Into reheated steam temperature system, be well controlled effect, finally significantly increases robustness, to improving the automatic of fired power generating unit
Controlled level has important practical significance.
In addition, according to the above embodiment of the present invention be based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods,
There can also be following additional technical characteristic：
Further, in one embodiment of the invention, in the step E, it is thin that each is calculated using the following formula
The final fitness J of bacteriumca(i,j,k,l)；
Wherein, j, k, l represent that bacterium is presently in chemotactic, duplication, migration number respectively, and J (i, j, k, l) is thin for i-th
The fitness of bacterium,For the impact factor of current bacterium and other bacteriums, ha, wa, hr, wr are attractant quantity respectively, attract
Agent rate of release, repellents quantity and rate of release.
Further, in one embodiment of the invention, in the step E, in the step E, using the following formula
Calculate bacterium reverses direction Δ M (i) and travelling step-length C (i)；
Δ M (i)=Δ (i)+(θi-θb)θrR1；
Wherein, θ (i, j, k, l) represents bacterium position coordinates, DibRefer to bacterium i and the positional distance of optimal location bacterium,
WithRefer respectively to the m dimension components of corresponding bacterium position vector, Cmax、CminRefer to the minimum and maximum step-length of bacterium respectively.
Further, in one embodiment of the invention, in the step E, in the step F, using the following formula
More novel bacteria position；
Further, in one embodiment of the invention, in the step E, in the step G, using the following formula
Calculate the health degree of each bacterium
Further, in one embodiment of the invention, in the step E, in the step I, the setting it is general
Rate is 0.25.
The additional aspect and advantage of the present invention will be set forth in part in the description, and will partly become from the following description
It obtains substantially or is recognized by the embodiment of the present invention.
Description of the drawings
In description of the above-mentioned and/or additional aspect and advantage of the embodiment of the present invention from combination accompanying drawings below to embodiment
It will be apparent and be readily appreciated that, wherein：
Fig. 1 is the stream according to embodiments of the present invention based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods
Journey schematic diagram；
Fig. 2 is minimum fitness when being optimized using the method for the present invention to reheated steam temperature system PID controller parameter
Change curve；
Fig. 3 is that system output of the reheat steam temperature under the control action of PID controller obtained by two kinds of the 3rd suboptimization of algorithm is bent
Line；
Fig. 4 is controller output of the reheat steam temperature under the control action of PID controller obtained by two kinds of the 3rd suboptimization of algorithm
Curve；
When Fig. 5 is 380MW operating modes, the system response curve corresponding to two kinds of algorithm optimization results；
When Fig. 6 is 600MW operating modes, the system response curve corresponding to two kinds of algorithm optimization results.
Specific embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, the technical solution in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
Part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
All other embodiments obtained without making creative work belong to the scope of protection of the invention.
Incorporated by reference to Fig. 1, the embodiment of the present invention propose based on PSO-ABFO reheat steam temperature PID controller parameters optimization side
Method, including：
The PID controller parameter feasible using each group is as a bacterium position, using improved adaptive based on population
Bacterial foraging algorithm is answered to search out optimal PID controller parameter, wherein, it is described to be looked for based on the improved adaptive bacterium of population
Food algorithm comprises the following steps：
Step A, the parameter initialization of algorithm set bacterial population size, and optimized variable dimension, bacterium duplication, chemotactic move
Move maximum times, random initializtion flora position；
Wherein, bacterium optimization algorithm (Bacterial Foraging Optimization, BFO) of looking for food is quilt in 2002
A kind of bacterial clump optimization algorithm that one entitled doctor K.M.Passino proposes, basic principle is according to Escherichia coli sheet
Influencing each other between the cilium of body and bacterium completes information exchange, makes bacterium to nutrition by chemotactic, duplication and migration operation
The higher place of concentration is mobile, achievees the purpose that find parametric optimal solution.Research shows standard BFO algorithms because using fixation
Step-length and random reverses direction, the then more blindness of flora optimizing, it is impossible to quickly find optimal solution.And the present embodiment carries
In the method for confession, on the basis of particle group optimizing strategy and adaptive strategy is combined, redefined bacterium health degree and
Position evaluation method provides a kind of improved population adaptive BFO algorithms, hereinafter referred to as PSO-ABFO algorithms.
Bacterium position coordinates is represented with θ (i, j, k, l), and j, k, l represent that bacterium is presently in chemotactic, duplication, migration respectively
Number.Population Size is represented with S, it is necessary to which the dimension solved is p, and herein, the position of each bacterium represents a time
Choosing solution.Bacterium chemotactic, duplication, the maximum times of migration operation are respectively Nc, Nre, Ned, and J (i, j, k, l) is i-th of bacterium
Fitness, size are used for evaluating the quality of candidate solution.
On the basis of the random reverses direction of bacterium, learning coefficient of the bacterium to history optimal location information is introduced, is tied
The concept for closing population global optimum proposes intensified learning direction, and at the same time, PSO-ABFO algorithms remain bacterium and turn at random
Turn direction, ensure that the random search characteristic of algorithm.
Step B, cycling character iterations add 1；
Step C, replication cycle number add 1；
Step D, chemotactic cycle-index add 1；
Step E, calculates the fitness of initial flora, and records the position of the initial optimal bacterium of group, and it is thin to calculate each
The final fitness of bacterium, and go out bacterium reverses direction and travelling step-length respectively；
Wherein, the final fitness J of each bacterium is calculated using the following formulaca(i,j,k,l)；
Wherein, j, k, l represent that bacterium is presently in chemotactic, duplication, migration number respectively, and J (i, j, k, l) is thin for i-th
The fitness of bacterium,For the impact factor of current bacterium and other bacteriums, ha, wa, hr, wr are attractant quantity respectively, attract
Agent rate of release, repellents quantity and rate of release reflect the message intercommunication between bacterium.In order to improve convergence rate, more
During the fitness of novel bacteria, so the impact factor between adding bacteriumIt is according to bacterium and other bacteriums
What position relationship determined.
Bacterium reverses direction Δ M (i) and travelling step-length C (i) are calculated using the following formula；
Δ M (i)=Δ (i)+(θi-θb)θrR1；
Wherein, θ (i, j, k, l) represents bacterium position coordinates, DibRefer to bacterium i and the positional distance of optimal location bacterium,
WithRefer respectively to the m dimension components of corresponding bacterium position vector, Cmax、CminRefer to the minimum and maximum step-length of bacterium respectively.
C (i) is the travelling step-length of bacterium i, and value can carry out appropriate variation according to actual conditions.In addition, the step size computation from bacterium
Formula can be seen that the diminution with bacterium current location and optimal location distance, and travelling step-length can also become smaller, bacterium with it is optimal
Position is closer, and C (i) more tends to Cmin；On the contrary, with optimal location it is distant when, step-length can opposite increase, tend to Cmax。
Step F, more novel bacteria position simultaneously calculate the fitness of bacterium under new position, judge whether to continue to swim in same direction
Dynamic, if the fitness of bacterium is improved under new position, bacterium continues to move about in same direction, more novel bacteria position and adaptation
Degree, until fitness no longer improves or reach maximum steps of random walk, the chemotactic into next bacterium operates；
Wherein, the location information of group's optimum individual is recorded and updated in real time, according to current individual and group's optimum individual
The relation of position, it is corresponding to adjust bacterium travelling step-length, improve the low optimization accuracy of algorithm.In the present embodiment, specifically using following
Formula more novel bacteria position；
Step G after chemotactic operates, into bacterium duplicate stage, calculates the health degree of each bacterium, by bacterium according to
Health degree is ranked up, and the selection of the survival of the fittest of bacterium is carried out according to order, the poor a part of bacterium of health degree is eliminated,
The duplication that the higher bacterium of health degree is divided into two keeps total number of bacteria constant, and it is follow-on to be then transferred to step C progress
Chemotactic operates；
Wherein, standard BFO is after each chemotactic cycle, using the sum of all previous fitness of bacterium as bacterial activity (health
Degree), and carry out the duplication of bacterium on this basis and eliminate, but so easily cover and lose the optimal letter of history of bacterium
Breath, makes convergence speed of the algorithm not fast enough.In order to improve the rapidity of algorithm, this method is directly by bacterium history adaptive optimal control degree
Bacterium is ranked up as the health degree of bacterium, and by health degree, operation is replicated and eliminated to bacterium, keeps bacterium total
Number is constant.In the present embodiment, the specific health degree that each bacterium is calculated using the following formula
Step H, after flora reaches maximum number of copy times, into migration phase；
Step I for each bacterium, randomly generates a random number, right when this random number is more than the probability of setting
Bacterium carries out migration operation；Otherwise, bacterium will keep original position to continue optimizing, be then transferred to step B, carry out the chemotactic of a new round
And duplication；
Wherein, the probability of the setting is preferably set for 0.25.After bacterium reaches maximum number of copy times, for each thin
Bacterium randomly generates a random number, and when this random number is more than the probability of setting, migration operation is carried out to bacterium；Otherwise, carefully
Bacterium will keep original position to continue optimizing.In order to allow population not because losing more excellent solution during migration operation, to each random initializtion
Parameter is compared with original parameter, and only the bacterium of better performances just carries out next iteration.
Step H when migrating number more than maximum transport number, exports optimum results, and algorithm terminates.
It is according to embodiments of the present invention based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods, pass through first
The thought of particle cluster algorithm is introduced into normal bacterial foraging algorithm, is walked using the travelling of adaptive step stragetic innovation bacterium
It is long, then reheated steam temperature system is obtained object function, controller parameter is scanned for as the control object of optimal controller
The PID controller optimized is finally applied in Reheated-steam Temperature Control System by optimizing.This method is in normal bacterial foraging algorithm
On the basis of, pass through optimizing strategy to bacterium, the redefining of health degree, the improvement of migration strategy and adaptive step plan
Application slightly makes convergence speed of the algorithm and optimization precision all improve a lot, and the parameter optimization method application invented
Into reheated steam temperature system, be well controlled effect, finally significantly increases robustness, to improving the automatic of fired power generating unit
Controlled level has important practical significance.
The present invention is further detailed with an example below：
Be verification the present embodiment propose based on the effective of PSO-ABFO reheat steam temperature PID controller parameter optimization methods
Property, using certain 600MW fired power generating unit, the relation mould of burner pivot angle aperture and reheat steam temperature in load 500MW operating modes
Type is as research object, as shown in formula (10)：
The major parameter of PSO-ABFO algorithms sets as shown in table 1, wherein k in simulation processpm、kpn、kim、kinRespectively
Controller parameter Search Range.
Table 1PSO-ABFO algorithm parameters are set
Meanwhile selection standard BFO algorithms (hereinafter referred to as BFO) carry out pid parameter optimization as a comparison, two kinds of algorithms are to formula
(10) the continuous 5 progress controller parameter optimum results data of transmission function object in are as shown in table 2.
2 two kinds of algorithm optimizing emulation data of table
From Table 2, it can be seen that optimization method optimum results proposed by the invention are more stablized than standard BFO algorithms,
And the adaptive optimal control degree smaller of bacterium, optimization performance are obviously improved.
In addition, Fig. 2 is minimum fitness change curve comparison diagram in third time optimization process, from Fig. 2, it can be seen that
PSO-ABFO algorithms can faster converge to optimal value, and the low optimization accuracy of algorithm compares compared with standard BFO algorithms
It is high.
Fig. 3 and Fig. 4 is respectively system curve of output of the reheat steam temperature in PID controller obtained by two kinds of the 3rd suboptimization of algorithm
With controller curve of output.From figure 3, it can be seen that under the control action of PI controllers obtained by PSO-ABFO algorithm optimizations,
The overshoot smaller of reheated steam temperature system step response, regulating time are shorter, system has been stabilized to set-point quickly, show system
Possess good dynamic and steady-state behaviour；From the point of view of Fig. 4, the amplitude of controller output and concussion frequency all smallers, control effect
Improve more apparent.
Further, it is the robustness of verification PSO-ABFO algorithms, while chooses the unit in 600MW and 380MW
Reheated steam temperature system model is emulated, and target transfer function as shown in formula (11), (12), is separately optimized respectively with two kinds of algorithms
The results are shown in Table 3 under 600MW and 380MW operating modes.
During 600MW：
During 380MW：
When 380MW and 600MW operating modes being set forth in Fig. 5, Fig. 6, the system corresponding to two kinds of algorithm optimization results is rung
Curve is answered, table 3 is optimization data of two kinds of algorithm optimization results under the conditions of 600MW and 380MW, it can be seen that image parameter
When changing greatly, the PI parameters of PSO-ABFO optimizations still have system preferable control effect, therefore, illustrate that PSO-ABFO is calculated
Method namely method provided by the invention have better robustness.
Table 3600MW and 380MW optimize data
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system, including the system of processor or other can be held from instruction
Row system, device or equipment instruction fetch and the system executed instruction) it uses or combines these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium " can any can be included, store, communicate, propagate or pass
Defeated program is for instruction execution system, device or equipment or the dress used with reference to these instruction execution systems, device or equipment
It puts.
The more specific example (non-exhaustive list) of computer-readable medium includes following：It is connected up with one or more
Electrical connection section (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other are suitable
Medium, because can be for example by carrying out optical scanner to paper or other media, then into edlin, interpretation or if necessary with it
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each several part of the present invention can be realized with hardware, software, firmware or combination thereof.Above-mentioned
In embodiment, software that multiple steps or method can in memory and by suitable instruction execution system be performed with storage
Or firmware is realized.If for example, with hardware come realize in another embodiment, can be under well known in the art
Any one of row technology or their combination are realized：With for the logic gates to data-signal realization logic function
Discrete logic, have suitable combinational logic gate circuit application-specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description
Point is contained at least one embodiment of the present invention or example.In the present specification, schematic expression of the above terms is not
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiments or example in combine in an appropriate manner.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
In the case of departing from the principle of the present invention and objective a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The scope of invention is limited by claim and its equivalent.
Claims (6)
1. one kind is based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods, which is characterized in that including：
The PID controller parameter feasible using each group is as a bacterium position, using improved adaptive thin based on population
Bacterium foraging algorithm searches out optimal PID controller parameter, wherein, it is described to be looked for food calculation based on the improved adaptive bacterium of population
Method comprises the following steps：
Step A, the parameter initialization of algorithm set bacterial population size, and optimized variable dimension, bacterium duplication, chemotactic, migration are most
Big number, random initializtion flora position；
Step B, cycling character iterations add 1；
Step C, replication cycle number add 1；
Step D, chemotactic cycle-index add 1；
Step E, calculates the fitness of initial flora, and records the position of the initial optimal bacterium of group, calculates each bacterium
Final fitness, and go out bacterium reverses direction and travelling step-length respectively；
Step F, more novel bacteria position simultaneously calculate the fitness of bacterium under new position, judge whether to continue to move about in same direction,
If the fitness of bacterium is improved under new position, bacterium continues to move about in same direction, more novel bacteria position and fitness, directly
No longer improve to fitness or reach maximum steps of random walk, the chemotactic into next bacterium operates；
Step G after chemotactic operates, into bacterium duplicate stage, calculates the health degree of each bacterium, by bacterium according to health
Degree is ranked up, and the selection of the survival of the fittest of bacterium is carried out according to order, the poor a part of bacterium of health degree is eliminated, health
The duplication that the higher bacterium of degree is divided into two keeps total number of bacteria constant, is then transferred to step C and carries out follow-on chemotactic
Operation；
Step H, after flora reaches maximum number of copy times, into migration phase；
Step I for each bacterium, randomly generates a random number, when this random number is more than the probability of setting, to bacterium
Carry out migration operation；Otherwise, bacterium will keep original position to continue optimizing, then be transferred to step B, carry out the chemotactic of a new round and answer
System；
Step H when migrating number more than maximum transport number, exports optimum results, and algorithm terminates.
2. according to claim 1 based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods, feature exists
In in the step E, the final fitness J of each bacterium is calculated using the following formulaca(i,j,k,l)；
<mrow>
<msub>
<mi>J</mi>
<mrow>
<mi>c</mi>
<mi>a</mi>
</mrow>
</msub>
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<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>J</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mi>J</mi>
<mrow>
<mi>c</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>b</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
<mrow>
<msubsup>
<mi>J</mi>
<mrow>
<mi>c</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>b</mi>
</mrow>
</msubsup>
<mo>=</mo>
<mo>-</mo>
<msub>
<mi>h</mi>
<mi>a</mi>
</msub>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<msub>
<mi>w</mi>
<mi>a</mi>
</msub>
<msubsup>
<mi>D</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>b</mi>
</mrow>
<mn>2</mn>
</msubsup>
</mrow>
</msup>
<mo>+</mo>
<msub>
<mi>h</mi>
<mi>r</mi>
</msub>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<msub>
<mi>w</mi>
<mi>r</mi>
</msub>
<msubsup>
<mi>D</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>b</mi>
</mrow>
<mn>2</mn>
</msubsup>
</mrow>
</msup>
<mo>;</mo>
</mrow>
Wherein, j, k, l represent that bacterium is presently in chemotactic, duplication, migration number respectively, and J (i, j, k, l) is i-th of bacterium
Fitness,For the impact factor of current bacterium and other bacteriums, ha, wa, hr, wr are that attractant quantity, attractant are released respectively
Put speed, repellents quantity and rate of release.
3. according to claim 2 based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods, feature exists
In in the step E, using the following formula calculating bacterium reverses direction Δ M (i) and travelling step-length C (i)；
Δ M (i)=Δ (i)+(θi-θb)θrR1；
<mrow>
<mi>C</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>D</mi>
<mrow>
<mi>i</mi>
<mi>b</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>&beta;</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mrow>
<mn>1</mn>
<mo>+</mo>
<mi>&mu;</mi>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>D</mi>
<mrow>
<mi>i</mi>
<mi>b</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>&beta;</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mfrac>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mfrac>
<mn>1</mn>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>D</mi>
<mrow>
<mi>i</mi>
<mi>b</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>&beta;</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mfrac>
<mo>+</mo>
<mi>&mu;</mi>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mi>i</mi>
<mi>b</mi>
</mrow>
</msub>
<mo>=</mo>
<msqrt>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>P</mi>
</msubsup>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>&theta;</mi>
<mi>m</mi>
<mi>i</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>&theta;</mi>
<mi>m</mi>
<mi>b</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
<mo>;</mo>
</mrow>
<mrow>
<mi>&beta;</mi>
<mo>=</mo>
<msqrt>
<mfrac>
<mrow>
<msub>
<mi>C</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
<msub>
<mi>C</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
<mrow>
<msub>
<mi>C</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
</mfrac>
</msqrt>
<mo>;</mo>
</mrow>
<mrow>
<mi>&mu;</mi>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msub>
<mi>C</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mfrac>
<mo>;</mo>
</mrow>
Wherein, θ (i, j, k, l) represents bacterium position coordinates, and Dib refers to the positional distance of bacterium i and optimal location bacterium,WithRefer respectively to the m dimension components of corresponding bacterium position vector, Cmax、CminRefer to the minimum and maximum step-length of bacterium respectively.
4. according to claim 3 based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods, feature exists
In in the step F, using the following formula more novel bacteria position；
<mrow>
<mi>&theta;</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>&theta;</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>C</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mfrac>
<mrow>
<msub>
<mi>&Delta;</mi>
<mi>M</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>&Delta;</mi>
<mi>M</mi>
<mi>T</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<msub>
<mi>&Delta;</mi>
<mi>M</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>.</mo>
</mrow>
5. according to claim 4 based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods, feature exists
In in the step G, using the health degree of each bacterium of the following formula calculating
<mrow>
<msubsup>
<mi>J</mi>
<mrow>
<mi>h</mi>
<mi>e</mi>
<mi>a</mi>
<mi>l</mi>
<mi>t</mi>
<mi>h</mi>
</mrow>
<mi>i</mi>
</msubsup>
<mo>=</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<mi>J</mi>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mn>1</mn>
<mo>:</mo>
<msub>
<mi>N</mi>
<mi>c</mi>
</msub>
<mo>,</mo>
<mi>k</mi>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>.</mo>
</mrow>
6. according to claim 1 to 5 any one based on PSO-ABFO reheat steam temperature PID controller parameters optimization side
Method, which is characterized in that in the step I, the probability set is 0.25.
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