CN109213174A - A kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network - Google Patents
A kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000001514 detection method Methods 0.000 title claims abstract description 27
- 239000010865 sewage Substances 0.000 title claims abstract description 26
- 230000004888 barrier function Effects 0.000 claims abstract description 22
- 230000003044 adaptive effect Effects 0.000 claims abstract description 6
- 210000002569 neuron Anatomy 0.000 claims description 38
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- 239000011159 matrix material Substances 0.000 claims description 9
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0227—Control of position or course in two dimensions specially adapted to land vehicles using mechanical sensing means, e.g. for sensing treated area
- G05D1/0229—Control of position or course in two dimensions specially adapted to land vehicles using mechanical sensing means, e.g. for sensing treated area in combination with fixed guiding means
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
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Abstract
A kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network belongs to intelligent robot technology field.It is removable for barrier in sewage treatment plant, the features such as inspection environment is complicated and changeable, the intelligent barrier avoiding control method utilizes preposition, postposition ultrasonic sensor and the collected environmental information of impact switch, judge simultaneously decision to crusing robot ambient enviroment by fuzzy neural network, it realizes to the intelligent control of crusing robot avoidance, improves the safety in operation and stability of crusing robot；Solve the problems, such as that traditional robot barrier-avoiding method can not shift to an earlier date avoidance and avoidance effect vulnerable to interference.The experimental results showed that this method possesses more quick responding ability, there is stronger adaptive ability to complicated inspection environment, realize the intelligent barrier avoiding control of crusing robot, improve avoidance validity and real-time.
Description
Technical field
The present invention realizes sewage treatment plant's crusing robot using the intelligent patrol detection barrier-avoiding method based on fuzzy neural network
Intelligent barrier avoiding control, the avoidance obstacle of crusing robot are the key technologies for completing patrol task；It will be based on fuzzy neural network
Intelligent patrol detection barrier-avoiding method apply during crusing robot avoidance, realize the safe and efficient completion of patrol task；Sewage
Treatment plant's intelligent patrol detection barrier-avoiding method belongs to intelligent robot technology field；
Background technique
As China's level of urbanization is higher and higher, urban wastewater discharge rises year by year, and all parts of the country are newly-built or extend
All kinds of large and medium-sized sewage treatment plants, and with the expansion of plant area of sewage treatment plant area, outstanding is become to the uninterrupted inspection of plant area
To be important, but traditional manual inspection is difficult to realize uninterrupted inspection, and bigger region of patrolling and examining for patrol officer it is also proposed that
Requirements at the higher level；Therefore, crusing robot is increasingly being applied in the Daily Round Check work of sewage treatment plant；
In sewage treatment plant, the inspection of the flowing of personnel's vehicle, the movement of equipment etc. movable obstruction and complexity
Environment is always the problem that crusing robot faces during inspection；And need crusing robot of the work under unmanned environment
It is difficult to be widely applied, therefore the crusing robot with barrier avoiding function is more with practical value in actual working environment；However it passes
Uniting, there is the crusing robot of barrier avoiding function the avoidances operation such as just to be retreated, be turned to after touching barrier, current
It becomes increasingly complex and has been difficult to competent inspection work under changeable working environment；Therefore obstacle identity can be differentiated, made decisions on one's own
Avoidance mode quick and precisely executes the development trend that avoidance movement is crusing robot avoidance obstacle；Study sewage treatment plant's intelligence
Can inspection barrier-avoiding method, acquire inspection environmental information by multiple sensors, using artificial neural network autonomous learning, it is autonomous certainly
Plan avoidance mode realizes the intelligent patrol detection avoidance of crusing robot, guarantees the stable operation of inspection processing efficient, it has also become at sewage
Manage factory's safe and stable operation there is an urgent need to；
Intelligent patrol detection barrier-avoiding method is mainly the rotation direction for passing through control crusing robot servo motor, realizes different barriers
Hinder the autonomous intelligence under the conditions of species type, varying environment to shift to an earlier date avoidance movement, avoids colliding with barrier, utilize different sensors
More abundant environmental information is acquired, the accuracy of intelligent barrier avoiding decision is improved；Compared to the inspection of tradition touching avoidance obstacle
Robot, intelligent patrol detection barrier-avoiding method the accuracy of avoidance obstacle, stability and in terms of it is more advantageous；
The present invention devises a kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network, mainly passes through
Fuzzy neural network controller controls control target, realizes the intelligent patrol detection avoidance of crusing robot.
Summary of the invention
Present invention obtains a kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network, by being based on
The intelligent patrol detection barrier-avoiding method of fuzzy neural network carries out avoidance obstacle to sewage treatment plant's crusing robot, realizes inspection machine
People's intelligent patrol detection avoidance improves the safety in operation and stability of crusing robot；
Present invention employs the following technical solution and realize step:
1. a kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network, which is characterized in that for dirt
The motion state of water treatment plant's crusing robot is controlled, using the rotation direction of servo motor as control amount, the fortune of robot
Dynamic state is controlled volume；
The following steps are included:
(1) fuzzy neural network designed for control servo motor rotation direction, fuzzy neural network are divided into four layers: defeated
Enter layer, membership function layer, rules layer, output layer；Specifically:
1. input layer: the layer is made of 3 input neurons:
X (t)=[x1(t),x2(t),x3(t)]T (1)
Wherein, X (t) indicates the input of fuzzy neural network, x1It (t) is the machine of the preposition ultrasonic sensor measurement of t moment
The distance between device people and barrier, x2(t) between the robot and barrier for the preposition ultrasonic sensor measurement of t moment
Distance, x3It (t) is the switching signal of t moment impact switch, T is the transposition of matrix；
2. membership function layer: the layer has 3 × M membership function neuron, and each neuron represents a Gauss and is subordinate to letter
Number, is expressed as follows:
Wherein, M is rules layer neuron number, 1 < M≤20；uij(t) it indicates to be subordinate to letter j-th of i-th of t moment input
The output of number neuron, 0 <uij(t)≤1；mijIt (t) is j-th of membership function neuronal center value of i-th of t moment input, 0
<mij(t)；σijIt (t) is the width value of j-th of membership function neuron of i-th of t moment input；I indicates fuzzy neural network
Input number, j indicate fuzzy neural network membership function neuron number, i=1,2,3；J=1,2 ..., M；
3. rules layer: the layer has M regular neuron, the output of each neuron are as follows:
Wherein, fjIt (t) is the output valve of j-th of t moment regular neuron, 0 < fj(t)≤1；
4. output layer: the layer has 1 neuron, and output indicates are as follows:
Wherein, hjIt (t) is the output valve of j-th of consequent neuron of t moment, wijIt (t) is i-th of t moment input for jth
The weight coefficient of a consequent neuron, bjIt (t) is the biasing of j-th of consequent neuron of t moment, y (t) is t moment fuzzy neural
The output of network indicates the rotation direction control signal of servo motor；
(2) Training Fuzzy Neural Networks, specific as follows:
1. utilizing training sample and Adaptive Second-Order algorithm Training Fuzzy Neural Networks；Objective function are as follows:
E (t)=yd(t)-yu(t) (6)
Wherein, e (t) is the error of t moment rotation direction desired value and actual value, yd(t) it is expected for t moment rotation direction
Value, yuIt (t) is t moment rotation direction actual value；
2. the parameter to fuzzy neural network is updated, parameter more new formula are as follows:
Wherein, it includes Jacobi vector of the objective function to parameters local derviation value that J (t), which is t moment, and H (t) is t moment
Pseudo- Hessian matrix, GeIt (t) is t moment gradient vector, λ (t) is t moment autoadapted learning rate, and λ (t-1) is that the t-1 moment is adaptive
Learning rate, I are unit matrix, and Φ (t) is the parameter vector that t moment includes parameters value, and Φ (t+1) is the t+1 moment to include
The parameter vector of parameters value；
(3) it is designed for the barrier-avoiding method of intelligent patrol detection, specifically:
1. calculating the output of fuzzy neural network according to formula (5)；
2. judge the size of the objective function of current time crusing robot intelligent barrier avoiding control signal, if e (t) >
0.01, go to step 3.；If e (t)≤0.01, step is gone to 4.；
3. solving the updated value of parameters according to formula (7), step is gone to 1.；
4. calculating the control amount u at current timec(t)
uc(t)=yu(t) (8)
5. the output valve u of t moment fuzzy neural networkcIt (t) is the amount of actually entering of crusing robot drive system；
(4) the t moment control signal u solved is utilizedc(t) servo motor of crusing robot drive system is controlled
System, t moment control signal uc(t)=1 when, the servo motor of drive system is rotated forward；T moment controls signal uc(t)=0 when, driving
The servo motor of system stops operating；Current time controls signal uc(t)=- 1 when, the servo motor of drive system is reversed.
Creativeness of the invention is mainly reflected in:
(1) present invention is an open space for sewage treatment plant's crusing robot inspection environment, is had a variety of removable
The features such as dynamic barrier, environment is complicated and changeable, crusing robot need to take for different types of barrier in advance avoidance arrange
It applies, to guarantee robot safety itself, completes inspection process, existing tradition touching formula barrier-avoiding method is unable to satisfy this inspection ring
Avoidance requirement under the conditions of border；Environmental information is acquired using multiple sensors, using the intelligent patrol detection based on fuzzy neural network
The advantages that barrier-avoiding method realizes the intelligent patrol detection avoidance of crusing robot, has avoidance timely, adaptable, strong interference immunity；
(2) present invention employs sewage treatment plant's intelligent patrol detection barrier-avoiding methods based on fuzzy neural network to inspection machine
People's inspection process carries out avoidance obstacle, which takes full advantage of the self-learning capability of artificial neural network, for difference
Good avoidance effect can be achieved in inspection environment；Crusing robot is solved in sewage treatment plant's production environment complicated and changeable
Under promptly and accurately avoidance, safety complete patrol task the problem of；
It is important to note that: the present invention is intended merely to description conveniently, using to sewage treatment plant's crusing robot intelligence
Avoidance obstacle, the intelligent patrol detection avoidance etc. of the same invention also applicable power plant crusing robot, as long as using the present invention
Principle controlled and all should belong to the scope of the present invention.
Detailed description of the invention
Fig. 1 is control structure figure of the invention
Fig. 2 is structure of fuzzy neural network figure of the invention
Fig. 3 is control crusing robot avoidance result figure of the invention
Fig. 4 is control crusing robot avoidance resultant error figure of the invention
Specific embodiment
Present invention obtains a kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network, by being based on
The intelligent patrol detection barrier-avoiding method of fuzzy neural network carries out avoidance obstacle to sewage treatment plant's crusing robot, realizes inspection machine
The intelligent control of people's avoidance improves the safety in operation and stability of crusing robot；
1. a kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network,
It is controlled for the motion state of sewage treatment plant's crusing robot, is control with the rotation direction of servo motor
Amount, the motion state of robot are controlled volume, control structure such as Fig. 1；
Characterized by comprising the following steps:
(1) fuzzy neural network designed for control servo motor rotation direction, fuzzy neural network are divided into four layers: defeated
Enter layer, membership function layer, rules layer, output layer；Structure of fuzzy neural network such as Fig. 2, specifically:
1. input layer: the layer is made of 3 input neurons:
X (t)=[x1(t),x2(t),x3(t)]T (1)
Wherein, X (t) indicates the input of fuzzy neural network, x1It (t) is the machine of the preposition ultrasonic sensor measurement of t moment
The distance between device people and barrier, x2(t) between the robot and barrier for the preposition ultrasonic sensor measurement of t moment
Distance, x3It (t) is the switching signal of t moment impact switch, T is the transposition of matrix；
2. membership function layer: the layer has 3 × M membership function neuron, and each neuron represents a Gauss and is subordinate to letter
Number, is expressed as follows:
Wherein, M is rules layer neuron number, 1 < M≤20；uij(t) it indicates to be subordinate to letter j-th of i-th of t moment input
The output of number neuron, 0 <uij(t)≤1；mijIt (t) is j-th of membership function neuronal center value of i-th of t moment input, 0
<mij(t)；σijIt (t) is the width value of j-th of membership function neuron of i-th of t moment input；I indicates fuzzy neural network
Input number, j indicate fuzzy neural network membership function neuron number, i=1,2,3；J=1,2 ..., M；
3. rules layer: the layer has M regular neuron, the output of each neuron are as follows:
Wherein, fjIt (t) is the output valve of j-th of t moment regular neuron, 0 < fj(t)≤1；
4. output layer: the layer has 1 neuron, and output indicates are as follows:
Wherein, hjIt (t) is the output valve of j-th of consequent neuron of t moment, wijIt (t) is i-th of t moment input for jth
The weight coefficient of a consequent neuron, bjIt (t) is the biasing of j-th of consequent neuron of t moment, yuIt (t) is t moment fuzzy neural
The output of network indicates the rotation direction control signal of servo motor；
(2) Training Fuzzy Neural Networks, specific as follows:
1. utilizing training sample and Adaptive Second-Order algorithm Training Fuzzy Neural Networks；Objective function are as follows:
E (t)=yd(t)-yu(t) (6)
Wherein, e (t) is the error of t moment rotation direction desired value and actual value, yd(t) it is expected for t moment rotation direction
Value, yuIt (t) is t moment rotation direction actual value；
2. the parameter to fuzzy neural network is updated, parameter more new formula are as follows:
Wherein, it includes Jacobi vector of the objective function to parameters local derviation value that J (t), which is t moment, and H (t) is t moment
Pseudo- Hessian matrix, GeIt (t) is t moment gradient vector, λ (t) is t moment autoadapted learning rate, and λ (t-1) is that the t-1 moment is adaptive
Learning rate, I are unit matrix, and Φ (t) is the parameter vector that t moment includes parameters value, and Φ (t+1) is the t+1 moment to include
The parameter vector of parameters value；
(3) it is designed for the barrier-avoiding method of intelligent patrol detection, specifically:
1. calculating the output of fuzzy neural network according to formula (5)；
2. judge the size of the objective function of current time crusing robot intelligent barrier avoiding control signal, if e (t) >
0.01, go to step 3.；If e (t)≤0.01, step is gone to 4.；
3. solving the updated value of parameters according to formula (7), step is gone to 1.；
4. calculating the control amount u at current timec(t)
uc(t)=yu(t) (8)
5. the output valve of current time fuzzy neural network is exported as control signal to drive module；
(4) the current time control signal u solved is utilizedc(t) the servo motor rotation direction of drive system is carried out
Control, t moment control signal uc(t)=1 when, the servo motor of drive system is rotated forward；T moment controls signal uc(t)=0 it when, drives
The servo motor of dynamic system stops operating；T moment controls signal uc(t)=- 1 when, the servo motor of drive system is reversed；Fig. 3
Show crusing robot intelligent barrier avoiding as a result, X-axis: time, unit are 5 seconds/sample, Y-axis: servo motor rotation direction, nothing
Unit, black dotted lines are desired rotation direction, and solid line is actual rotation direction；Fig. 4 shows crusing robot intelligent barrier avoiding result
Error, X-axis: time, unit are 5 seconds/sample, Y-axis: rotation direction error amount, no unit；The results show this method
Validity.
Claims (1)
1. a kind of sewage treatment plant's intelligent patrol detection barrier-avoiding method based on fuzzy neural network, which is characterized in that at sewage
The motion state of reason factory's crusing robot is controlled, using the rotation direction of servo motor as control amount, the movement shape of robot
State is controlled volume；
The following steps are included:
(1) fuzzy neural network of signal is controlled designed for generating servo motor rotation direction, fuzzy neural network is divided into four
Layer: input layer, membership function layer, rules layer, output layer；Specifically:
1. input layer: the layer is made of 3 input neurons:
X (t)=[x1(t),x2(t),x3(t)]T (1)
Wherein, X (t) indicates the input of fuzzy neural network, x1(t) for the robot of t moment preposition ultrasonic sensor measurement with
The distance between barrier, x2It (t) is the distance between the robot of the preposition ultrasonic sensor measurement of t moment and barrier, x3
It (t) is the switching signal of t moment impact switch, T is the transposition of matrix；
2. membership function layer: the layer has 3 × M membership function neuron, and each neuron represents a Gauss member function, table
Show as follows:
Wherein, M is rules layer neuron number, 1 < M≤20；uij(t) j-th of membership function mind of i-th of t moment input is indicated
Output through member, 0 <uij(t)≤1；mijIt (t) is j-th of membership function neuronal center value of i-th of t moment input, mij(t)
>0；σijIt (t) is the width value of j-th of membership function neuron of i-th of t moment input；The input of i expression fuzzy neural network
Number, the membership function neuron number of j expression fuzzy neural network, i=1,2,3；J=1,2 ..., M；
3. rules layer: the layer has M regular neuron, the output of each neuron are as follows:
Wherein, fjIt (t) is the output valve of j-th of t moment regular neuron, 0 < fj(t)≤1；
4. output layer: the layer has 1 neuron, and output indicates are as follows:
Wherein, hjIt (t) is the output valve of j-th of consequent neuron of t moment, wijIt (t) is i-th of t moment input for after j-th
The weight coefficient of part neuron, bjIt (t) is the biasing of j-th of consequent neuron of t moment, yuIt (t) is t moment fuzzy neural network
Output, indicate servo motor rotation direction control signal；
(2) Training Fuzzy Neural Networks, specifically:
1. utilizing training sample and Adaptive Second-Order algorithm Training Fuzzy Neural Networks；Objective function are as follows:
E (t)=yd(t)-yu(t) (6)
Wherein, e (t) is the error of t moment rotation direction desired value and actual value, ydIt (t) is t moment rotation direction desired value, yu
It (t) is t moment rotation direction actual value；
2. the parameter to fuzzy neural network is updated, parameter more new formula are as follows:
Wherein, J (t) is the Jacobi vector comprising objective function to parameters local derviation value of t moment, and H (t) is that t moment is pseudo-
Hessian matrix, GeIt (t) is t moment gradient vector, λ (t) is t moment autoadapted learning rate, and λ (t-1) is adaptively to learn at the t-1 moment
Habit rate, I are unit matrix, and Φ (t) is the parameter vector comprising parameters value of t moment, and Φ (t+1) is the packet at t+1 moment
The parameter vector of the value containing parameters；
(3) it is designed for the barrier-avoiding method of intelligent patrol detection, specifically:
1. calculating the output of fuzzy neural network according to formula (5)；
2. judge the size of the objective function of current time crusing robot intelligent patrol detection avoidance obstacle signal, if e (t) >
0.01, go to step 3.；If e (t)≤0.01, step is gone to 4.；
3. solving the updated value of parameters according to formula (7), step is gone to 1.；
4. calculating the control amount u at current timec(t)
uc(t)=yu(t) (8)
5. the output valve u of t moment fuzzy neural networkcIt (t) is the amount of actually entering of crusing robot drive system；
(4) the t moment control signal u solved is utilizedc(t) to the servo motor rotation direction of crusing robot drive system into
Row control, t moment control signal uc(t)=1 when, the servo motor of drive system is rotated forward；T moment controls signal uc(t)=0 when,
The servo motor of drive system stops operating；T moment controls signal uc(t)=- 1 when, the servo motor of drive system is reversed.
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