CN112947056B - Magnetic-levitation train displacement speed tracking control method based on IGWO-BP-PID - Google Patents
Magnetic-levitation train displacement speed tracking control method based on IGWO-BP-PID Download PDFInfo
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Abstract
The invention provides a magnetic-levitation train displacement speed tracking control method based on IGWO-BP-PID. The method comprises the following steps: establishing a train system model according to train dynamics stress analysis, and designing a PID controller according to the train system model; determining the structure of a BP neural network according to the input and the output of a train system model, and training the BP neural network off line based on an improved IGWO algorithm; and obtaining proportional, integral and differential parameters of the PID controller according to the optimized BP neural network model and calculating the traction output of the train system. The invention realizes the real-time parameter adjustment of the PID controller based on the optimized network structure, and realizes the accurate tracking of the speed and the displacement of the train, thereby improving the running performance of the train.
Description
Technical Field
The invention relates to the technical field of magnetic suspension train operation control, in particular to a magnetic suspension train displacement speed tracking control method based on IGWO-BP-PID.
Background
In the high-speed running process of a magnetic suspension train, an automatic driving system needs to accurately and efficiently track a speed curve so as to ensure the safe on-track running of the train. Therefore, the speed tracking control of the magnetic suspension train is the basis of the running of the high-speed magnetic suspension train. However, in the actual running process, the maglev train is subjected to external forces such as air resistance, electromagnetic eddy resistance, ramp resistance and the like, so that the train has large nonlinearity and model parameters are time-varying in the running process.
The traditional PID (proportion-integral-derivative) controller is influenced by the model parameter time change in the train running process, cannot effectively track the ideal speed curve of the train, has larger tracking error and influences the train running performance, therefore, the design of the adaptive speed displacement tracking controller which is suitable for time-varying and non-precise models and has good tracking performance has important engineering application value for automatic and efficient train driving.
Disclosure of Invention
The embodiment of the invention provides a method for controlling the displacement speed tracking of a magnetic suspension train based on IGWO-BP-PID (integrated waveguide-back propagation-proportional integral derivative) so as to realize effective tracking control on the displacement speed of the magnetic suspension train.
In order to achieve the purpose, the invention adopts the following technical scheme.
A magnetic suspension train displacement speed tracking control method based on IGWO-BP-PID comprises the following steps:
establishing a train system model according to train dynamics stress analysis, and designing a PID controller according to the train system model;
determining the structure of a BP neural network according to the input and the output of the train system model, and training the BP neural network off line based on an improved Hui wolf optimization algorithm IGWO algorithm to obtain an optimized BP neural network;
acquiring a state vector of the magnetic-levitation train according to the acquired running state information of the magnetic-levitation train, inputting the state vector of the magnetic-levitation train into the optimized BP neural network model, and outputting proportional, integral and differential parameters of a PID controller by the optimized BP neural network model;
and calculating the traction output of the train system in real time according to the proportional, integral and differential parameters of the PID controller and the train system model, and obtaining the real-time displacement speed information of the train according to the traction output of the train system.
Preferably, the establishing of the train system model according to the train dynamics stress analysis includes:
establishing the following train system model according to train dynamics stress analysis:
F q for train tractive effort, F i As electromagnetic eddy current resistance, F a As air resistance, F s The slope resistance is, m is the car mass, v (t) is the train running speed, and x (t) is the train displacement state.
Preferably, the designing of the PID controller according to the train system model includes:
designing the following PID controllers according to a train system model:
wherein: f q (t) train control tractive effort, k p ，k i ，k d Respectively adaptive proportional, integral and differential parameters of the controller, e (t) is position tracking error,
velocity tracking error, of the form:
wherein: x is a radical of a fluorine atom * (t) is a desired position target value of the train, v * (t) is an ideal speed target value of the train;
the discretized PID controller is as follows:
F(t)＝k p [e(t)-e(t-1)]+k i e(t)+k d [e(t)-2e(t-2)+e(t-1)]。
preferably, the establishing a structure of the BP neural network according to the input and output of the train system model includes:
setting input information x of BP neural network according to train state information in (t) including historical error information, current time error information, target value; setting output parameters yo of BP neural network o (t) is proportional, integral, differential parameter of PID controller, that is, yo o (t)＝[k p ,k i ,k d ] T
Determining the structure of the BP neural network according to the input-output state model of the BP neural network as follows:
hi h (k)＝f(hi h (k)) (h＝1,2,...,p)
yo o (k)＝f(yi o (k)) (o＝1,2,3)
wherein: k denotes the kth sample, f (-) is the activation function, w ih Connection weight, w, of input layer and hidden layer ho Connection weights of hidden layer and output layer, hi h (k) For hidden layer output, p number of hidden layer neurons, b h Threshold for neurons of the hidden layer, b o Is the bias of the output layer neurons.
Preferably, the training of the BP neural network offline based on the improved grey wolf optimization algorithm IGWO algorithm to obtain the optimized BP neural network includes:
training a connection weight parameter of the BP neural network based on an improved grey wolf optimization algorithm IGWO algorithm to obtain a weight coefficient of the optimized BP neural network;
initializing parameter w of BP neural network ih ,w ho And the number p of hidden layer neurons;
initializing a population size M and a maximum iteration number I;
generating an initial population x according to the population size M m ；
Calculating the fitness function value of each individual in the population according to the fitness function, and marking the best three individuals as: x is a radical of a fluorine atom α ,x b ,x δ 。
And guiding the rest individuals in the rest population to update by utilizing the three optimal individuals, wherein the updating process is as follows:
d α ＝|C 1 ·x α -x(t)|,d β ＝|C 2 ·x β -x(t)|,d δ ＝|C 3 ·x δ -x(t)|
x 1 ＝X α -A 1 ·d α ,x 2 ＝X β -A 2 ·d β ,x 3 ＝X δ -A 3 ·d δ
wherein: a =2a · r 1 -a；C＝2·r 2 ，r 1 ,r 2 Is [0,1]The parameter a is linearly decreased to 0 along with the iteration times;
and (3) mutation operator calculation: randomly selecting s individuals to calculate a mutation operator delta:
wherein:
is a variation constant;
and (3) carrying out variation on the randomly selected s individuals to obtain corresponding variant individuals:
and outputting the weight parameters of the BP network according to the optimization result to obtain an optimized BP neural network model.
It can be seen from the technical solutions provided by the embodiments of the present invention that, the embodiments of the present invention provide a PID controller based on a BP network, a weight parameter of a neural network is trained offline based on actual running data of a train through an offline IWGO algorithm, and then real-time parameter adjustment of a speed and displacement tracking controller is realized based on an optimized network structure, so as to realize accurate tracking of train speed and displacement, thereby improving train running performance.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a processing flow chart of a magnetic-levitation train displacement velocity tracking control method based on IGWO-BP-PID according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding of the embodiments of the present invention, the following detailed description will be given by way of example with reference to the accompanying drawings, and the embodiments are not limited to the embodiments of the present invention.
The embodiment of the invention provides a BP neural network-based PID controller of a maglev train, which realizes the high-efficiency tracking of the speed and the position of the train by updating the PID controller parameters in real time under the condition that a maglev train dynamic model is nonlinear time-varying, thereby ensuring the safe and energy-saving running of the train.
The processing flow of the magnetic suspension train displacement speed tracking control method based on IGWO-BP-PID of the embodiment of the invention is shown in figure 1, and comprises the following processing steps:
s1, establishing a dynamic model of the magnetic-levitation train
Establishing a maglev train dynamic model: the maglev train is subjected to external forces such as electromagnetic traction force, electromagnetic eddy resistance, air resistance, ramp resistance and the like in the running process to establish a maglev train dynamic model:
wherein: f q For train tractive effort, F i As resistance to electromagnetic eddy currents, F a As air resistance, F s The slope resistance is m is the carriage mass, v (t) is the train running speed, and x (t) is the train displacement state.
Respectively train running speed and acceleration.
Step S2: designing a PID controller: designing a PID controller of the magnetic-levitation train according to the magnetic-levitation train system model as follows:
wherein: f q (t) train control tractive effort, k p ，k i ，k d The controller is respectively adaptive to proportional, integral and differential parameters. e (t) is the position tracking error,
velocity tracking error, of the form:
wherein: x is the number of * (t) is an ideal position target value, v * (t) is an ideal speed target value.
Discretization of PID controller: since train control needs to be periodically controlled, the discretized PID controller is as follows:
F(t)＝k p [e(t)-e(t-1)]+k i e(t)+k d [e(t)-2e(t-2)+e(t-1)]
and step S3: establishing BP neural network model
Determining the input and output of the BP network: to make more efficient use of train state information, input information x of a neural network is set in (t) including historical error information, current time error information, target value;
the output parameter is proportional, integral and differential parameters of PID controller, that is yo o (t)＝[k p ,k i ,k d ] T 。
Determining the structure of the BP network: determining the structure of the BP network according to an input-output state model of the BP network:
hi h (k)＝f(hi h (k)) (h＝1,2,...,p)
yo o (k)＝f(yi o (k)) (o＝1,2,3)
wherein: k denotes the kth sample, f (-) is the activation function, w ih Connection weight, w, of input layer and hidden layer ho Connection weights of hidden layer and output layer, hi h (k) For hidden layer output, p number of hidden layer neurons, b h Threshold for neurons of the hidden layer, b o Is the bias of the output layer neurons.
And step S4: training and optimizing the BP neural network based on an improved grey wolf optimization algorithm (IGWO) to obtain a weight coefficient of the optimized BP neural network: and training the connection weight parameters of the BP neural network based on an improved grey wolf optimization algorithm (IGWO) according to the historical running data information of the train.
Initializing parameters w of the BP network ih ,w ho And number p of hidden layer neurons;
initializing a population size M and a maximum iteration number I;
generating an initial population x according to the population size M m ；
Calculating the fitness function value of each individual in the population according to the fitness function, wherein the fitness function value is the error between the output of the neural network and the actual output, and the best three individuals are marked as follows according to the fitness values: x is the number of α ,x b ,x δ 。
And guiding the rest individuals in the rest population to update by utilizing the three optimal individuals, wherein the updating process is as follows:
d α ＝|C 1 ·x α -x(t)|,d β ＝|C 2 ·x β -x(t)|,d δ ＝|C 3 ·x δ -x(t)|
x 1 ＝X α -A 1 ·d α ,x 2 ＝X β -A 2 ·d β ,x 3 ＝X δ -A 3 ·d δ
wherein: a =2a · r 1 -a；C＝2·r 2 。r 1 ,r 2 Is [0,1]A random number in between. The parameter a decreases linearly with the number of iterations to 0.
And (3) mutation operator calculation: randomly selecting s individuals to calculate a mutation operator delta:
wherein:
is a variation constant.
And (3) carrying out variation on the randomly selected s individuals to obtain corresponding variant individuals:
and (3) outputting an offline network structure: and (4) iteratively training the BP neural network according to the process, and outputting model parameters of the BP neural network, wherein the model parameters comprise weight parameters among neurons.
And S5, acquiring running state information of the magnetic-levitation train through vehicle-mounted speed and position sensing equipment.
And obtaining the state vector of the magnetic-levitation train according to the obtained running state information of the magnetic-levitation train. And inputting the state vector of the magnetic-levitation train into the optimized BP neural network model, and outputting the proportional, integral and differential parameters of the PID controller by the optimized BP neural network model.
Step S6: traction output calculation: and calculating the traction output of the train system in real time according to the proportional, integral and differential parameters of the PID controller and the train system model. I.e. according to
Wherein k is p ,k i ,k d And outputting the neural network after training.
And then, obtaining real-time displacement speed information of the train according to the traction output of the train system.
In summary, the embodiments of the present invention provide a PID controller based on a BP network, which trains a weight parameter of a neural network offline based on actual operation data of a train through an offline IWGO algorithm, and then realizes real-time parameter adjustment of the PID controller based on an optimized network structure, so as to realize accurate tracking of train speed displacement, thereby improving train operation performance.
The technical scheme of the embodiment of the invention has the advantages of clear principle and simple design, and the PID tracking controller with online adaptive adjustment and good tracking performance is designed aiming at the problem of the position and speed tracking of the maglev train.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
While the invention has been described with reference to specific preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the following claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (3)
1. A magnetic suspension train displacement speed tracking control method based on IGWO-BP-PID is characterized by comprising the following steps:
establishing a train system model according to train dynamics stress analysis, and designing a PID controller according to the train system model;
determining the structure of a BP neural network according to the input and the output of the train system model, and training the BP neural network off line based on an improved Hui wolf optimization IGWO algorithm to obtain an optimized BP neural network model;
acquiring a state vector of the magnetic-levitation train according to the acquired running state information of the magnetic-levitation train, inputting the state vector of the magnetic-levitation train into the optimized BP neural network model, and outputting proportional, integral and differential parameters of a PID controller by the optimized BP neural network model;
calculating the traction output of the train system in real time according to the proportional, integral and differential parameters of the PID controller and the train system model, and obtaining the real-time displacement speed information of the train according to the traction output of the train system;
the structure for establishing the BP neural network according to the input and the output of the train system model comprises the following steps:
setting input information x of BP neural network according to running state information of train in (t) including historical error information, current time error information, target value; setting output parameters yo of BP neural network o (t) is proportional, integral, differential parameter of PID controller, that is, yo o (t)＝[k p (t)，k i (t)，k d (t)] T
Determining the structure of the BP neural network according to the input-output state model of the BP neural network as follows:
ho h (k)＝f(hi h (k))(h＝1,2,...,p)
yo o (k)＝f(yi o (k))(o＝1,2,3)
wherein: k denotes the kth sample, f (-) is the activation function, w ih Connection weight, w, of input layer and hidden layer ho Connection weight, hi, of hidden layer to output layer h (k) For hidden layer output, p number of hidden layer neurons, b h Threshold for neurons of the hidden layer, b o Is the bias of the output layer neurons;
the improved grey wolf optimization-based IGWO algorithm offline training BP neural network to obtain the optimized BP neural network comprises the following steps:
training a connection weight parameter of the BP neural network based on an improved grey wolf optimization IGWO algorithm to obtain a weight coefficient of the optimized BP neural network;
initializing parameter w of BP neural network ih ,w ho And the number p of hidden layer neurons;
initializing a population size M and a maximum iteration number I;
generating an initial population x according to the population size M m ；
Calculating a fitness function value of each individual in the population according to the fitness function, and marking the best three individuals as: x is the number of α ,x b ,x δ ；
And guiding the rest individuals in the rest population to update by using the best three individuals, wherein the updating process is as follows:
d α ＝|C 1 ·x α -x(t)|,d β ＝|C 2 ·x β -x(t)|,d δ ＝|C 3 ·x δ -x(t)|
x 1 ＝x α -A 1 ·d α ,x 2 ＝x b -A 2 ·d β ,x 3 ＝x δ -A 3 ·d δ
wherein: x (t) is the displacement state of the train, r 1 ,r 2 Is [0,1]The parameter a is linearly decreased to 0 along with the iteration times;
and (3) mutation operator calculation: randomly selecting s individuals to calculate a mutation operator delta:
wherein
Is a variation constant;
and (3) carrying out variation on the randomly selected s individuals to obtain corresponding variation individuals:
and outputting the weight coefficient of the BP neural network according to the optimization result to obtain an optimized BP neural network model.
2. The method of claim 1, wherein said modeling a train system based on a train dynamics stress analysis comprises:
establishing a train system model according to train dynamics stress analysis, wherein the train system model comprises the following steps:
F q for train tractive effort, F i As resistance to electromagnetic eddy currents, F a As air resistance, F s The slope resistance is, m is the car mass, v (t) is the train running speed, and x (t) is the train displacement state.
3. The method of claim 2, wherein said designing a PID controller based on said train system model comprises:
designing the following PID controllers according to a train system model:
wherein: f q (t) train tractive effort, k p (t)，k i (t)，k d (t) are adaptive proportional, integral and differential parameters of the controller respectively, e (t) is a position tracking error,
velocity tracking error, of the form:
wherein: x is the number of * (t) is a desired position target value of the train, v * (t) is an ideal speed target value of the train;
the discretized PID controller is as follows:
F(t)＝k p (t)[e(t)-e(t-1)]+k i (t)e(t)+k d (t)[e(t)-2e(t-2)+e(t-1)]。
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