CN117950310B - Control method based on programmable electric load shoulder-pushing trainer - Google Patents
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Abstract
The invention discloses a control method based on a programmable electric load shoulder-pushing trainer, which belongs to the technical field of PID control and specifically comprises the following steps: step one: constructing a system model and a PID control system model based on a programmable electric load shoulder-pushing trainer; step two: a model for improving the golf algorithm is constructed, which comprises two parts: d1, merging a random disturbance strategy in an exploration stage of the algorithm, and improving the local searching and global searching capacity of the algorithm by disturbing the individual optimal position of each iteration of the algorithm; d2, adding an adaptive dynamic operator w in the development stage of the algorithm, so as to accelerate the convergence rate of the algorithm; step three: optimizing a current inner loop PID controller and a speed outer loop PID controller by utilizing an improved golf algorithm to obtain optimal Kp, ki and Kd control parameters; step four: the control system uses the optimized control parameters to realize current and speed double closed-loop control.
Description
Technical Field
The invention relates to the technical field of PID control, in particular to a control method based on a programmable electric load shoulder-pushing trainer.
Background
The development of fitness training equipment goes through multiple stages from natural weights to professional strength fitness training machines, and as fitness training machines develop in more professional directions, the effects of the fitness training machines on the improvement of fitness and training efficiency are more and more obvious. A key component in shoulder trainer is the resistance control system, which is the resistance generating device against the muscles in the power machine. The traditional shoulder pushing trainer adopts a tension cable to connect an iron block and a mechanical lever to generate resistance, but the resistance adjustment span is large, the adjustment is inflexible, and the adjustment is difficult to be quickly adjusted to a proper level. Because of the pure mechanical structure, the device can generate larger noise during use, lacks of sudden stop and safety protection functions and is easy to cause training damage.
A programmable electrical load is a device or system that is capable of simulating and generating various parameters such as current, voltage, and resistance to simulate the load conditions under actual operating conditions. Such devices or systems typically have programmable characteristics that can be programmed and controlled by a computer or other control device to simulate various complex load conditions. The motor is used as a main body, the load system adopting the programmable electric load as a resistance source is applied to body-building training equipment, and parameters such as strength, speed and the like can be controlled by controlling the motor, so that more accurate guidance is provided for training. In some high risk exercises, such as weightlifting, squatting, etc., injuries due to excessive force or speed may also be avoided.
The control method based on the programmable electric load shoulder-pushing trainer relates to a PID control method, and is mainly used for controlling the operation of a motor. In the whole motor operation process, current and speed double closed-loop control is required to be ensured so as to achieve stable operation of the motor. Conventional PID control is a linear control method, and may not provide sufficient performance in the face of a complex nonlinear burst environment.
Golf Optimization Algorithm (GOA) is an innovative heuristic based on games in which players need to drive a ball into a hole as much as possible with a limited number of shots during a golf game, and is proposed based on this shot. The basic principle of the algorithm is to simulate the course of a golf ball on a course and find the optimal solution by simulating the strength and direction of the ball striking. The algorithm is divided into two phases, namely an exploration phase and a development phase. The golf optimization algorithm has a higher convergence speed, but has weaker local searching capability, which means that the local optimal solution is easy to fall into, and the bottleneck of optimization is caused.
Disclosure of Invention
The invention aims at: the device aims to solve the defects that the traditional shoulder-pushing trainer has large resistance adjustment span, inflexible adjustment, low safety and the like, and the problem that the traditional PID control cannot provide enough control performance when facing to a complex nonlinear environment. The invention provides a control method based on a programmable electric load shoulder-pushing trainer, which uses a motor as a main body, adopts a load system with the programmable electric load as a resistance source to be applied to the shoulder-pushing trainer, and can enable the running motor to flexibly simulate loads with different weights by changing the torque output of the motor so as to achieve the purpose of body-building training. And the control parameters of the current PID controller and the speed PID controller in the motor control system are optimized by improving the golf optimization algorithm, so that the problems of instability and low responsiveness of the traditional PID control method are solved, and the control stability and the robustness of the shoulder-pushing trainer are improved.
In order to achieve the above purpose, the invention adopts the following technical scheme:
The control method based on the programmable electric load shoulder-pushing trainer comprises a system based on the programmable electric load shoulder-pushing trainer and a PID control system, and specifically comprises the following steps:
Step one: and constructing a system model and a PID control system model based on the programmable electric load shoulder-pushing trainer.
Step two: a model for improving the golf algorithm is constructed, which comprises two parts:
and D1, merging a random disturbance strategy in an exploration stage of the algorithm, and improving the local searching and global searching capacity of the algorithm by disturbing the individual optimal position of each iteration of the algorithm.
D2, adding an adaptive dynamic operator w in the development stage of the algorithm to accelerate the convergence rate of the algorithm, as shown in a formula (1);
（1）；
in the formula, T is the current iteration number, and T is the maximum iteration number.
Step three: and optimizing the current inner loop PID controller and the speed outer loop PID controller by utilizing an improved golf algorithm to obtain optimal Kp, ki and Kd control parameters.
Step four: the control system uses the optimized control parameters to realize current and speed double closed-loop control.
In the first step, the system model based on the programmable electric load shoulder-pushing trainer comprises a power supply, a controller, an energy discharging unit, a detecting unit, a permanent magnet synchronous motor, a motor driving unit, a tension cable, a brake and a client; the controller is a control center of the whole system and is used for controlling the stable operation of the motor; the permanent magnet synchronous motor mainly realizes energy conversion, and converts electric energy input by a power supply into mechanical energy to be output as a resistance source; the motor driving unit drives a motor to run for a power inverter; the detection unit comprises a current-voltage detection circuit; the brake is a handle or a lever; the tension cable is connected with the brake and the motor; the energy discharging unit is used for releasing extra energy generated by acting when the brake acts on the motor; the client is a touch display screen for realizing man-machine interaction, and the torque output of the motor is changed by issuing a command to the controller, so that the running motor can flexibly simulate loads with different weights to achieve the purpose of body building and training.
In the first step, the PID control system model includes an error calculation module, an improved golf algorithm module, a current inner loop PID controller module, a speed outer loop PID controller module, a current detection module, a rotation speed detection module, and a permanent magnet synchronous motor module.
Further, the error calculation module is used for calculating a current error value and a rotation speed error value; the current inner loop PID controller module receives the current error value and outputs a motor reference current value; the speed outer ring PID controller module receives the rotating speed error value and outputs an expected current value of the motor; the current detection module and the rotating speed detection module are respectively used for detecting the current and the rotating speed of the motor.
In the third step, the current inner loop PID controller and the speed outer loop PID controller are optimized by using an improved golf algorithm to obtain optimal Kp, ki and Kd control parameters, and the method comprises the following specific steps:
s1, encoding Kp, ki and Kd parameters of the PID controller into a solution of a golf algorithm search space.
S2, initializing parameters of an improved golf algorithm, including a population scale N, a maximum iteration number T, a space dimension d and searching an upper boundSearch for lower bound。
S3, calculating a current fitness value fitness of the individuals in the golf algorithm population, and recording a current iteration individual optimal solution, wherein a fitness function formula is as follows:
；
Wherein, Indicating the desired output value at time t,The actual output value at time t is shown, and J is the fitness value.
S4, in the algorithm exploration stage, a position update formula integrated with a random disturbance strategy is as follows:
（2）；
Wherein, Is based on the new location of the ith individual of the exploration phase,Is based on the position of the ith individual d dimension of the exploration phase,Is the individual optimal position in the d-th dimension,Is a collectionIs used for the random number in the random number code,Is the position of the ith individual in the d-th dimension, r is a random number between 0 and 1.
In the algorithm development stage, a position update formula of the self-adaptive dynamic operator w is added as follows:
（3）；
Wherein, Is based on the new location of the ith individual of the development phase,The position of the ith individual d dimension of the development stage is based on the other parameters as above.
And S5, calculating individual fitness values of the current iteration population according to the fitness function, and reserving an optimal solution according to a greedy selection strategy, namely discarding the current position and updating the current position as the candidate position if the fitness of the candidate position of the individual is better than that of the current position.
S6, judging whether the current iteration times T reach the maximum iteration times T, if so, stopping optimizing and outputting an optimal solution, and distributing the optimal solution to the PID controller as control parameters Kp, ki and Kd, otherwise, returning to S2 to continue optimizing.
By adopting the technical scheme, the invention has the following beneficial effects: by improving the golf optimization algorithm, the problem that the basic golf optimization algorithm is weak in local searching capability and easy to sink into a local optimal solution is solved, and the performance of the algorithm is improved. In addition, the control performance and the robustness of the system are further improved by adopting the PID double-closed-loop control method based on the improved golf algorithm in the control system of the shoulder-pushing trainer.
Drawings
FIG. 1 is a system model based on a programmable electrical load shoulder trainer.
FIG. 2 is a model of a PID control system based on a programmable electrical load shoulder trainer.
FIG. 3 is a flow chart for optimizing PID parameters for a modified golf algorithm.
FIG. 4 is a graph comparing the best fitness function of a basic golf algorithm and a modified golf algorithm.
FIG. 5 is a graph comparing the effect of the basic golf algorithm and the improved golf algorithm on optimizing PID parameters.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. The following examples are only a part of the examples of the present invention for more clearly explaining the technical aspects of the present invention, and it is understood that the specific examples are only for explaining the present invention, but the embodiments of the present invention are not limited thereto.
Referring to fig. 1-5, the present invention provides a technical solution:
The control method based on the programmable electric load shoulder-pushing trainer comprises a system based on the programmable electric load shoulder-pushing trainer and a PID control system, and specifically comprises the following steps:
Step one: and constructing a system model and a PID control system model based on the programmable electric load shoulder-pushing trainer.
Step two: a model for improving the golf algorithm is constructed, which comprises two parts:
and D1, merging a random disturbance strategy in an exploration stage of the algorithm, and improving the local searching and global searching capacity of the algorithm by disturbing the individual optimal position of each iteration of the algorithm.
D2, adding an adaptive dynamic operator w in the development stage of the algorithm to accelerate the convergence rate of the algorithm, as shown in a formula (1);
（1）；
in the formula, T is the current iteration number, and T is the maximum iteration number.
Step three: and optimizing the current inner loop PID controller and the speed outer loop PID controller by utilizing an improved golf algorithm to obtain optimal Kp, ki and Kd control parameters.
Step four: the control system uses the optimized control parameters to realize current and speed double closed-loop control.
Further, in the first step, as shown in fig. 1, the system model based on the programmable electric load shoulder-pushing trainer includes a power supply, a controller, an energy discharging unit, a detecting unit, a permanent magnet synchronous motor, a motor driving unit, a tension cable, a brake and a client; the controller is a control center of the whole system and is used for controlling the stable operation of the motor; the permanent magnet synchronous motor mainly realizes energy conversion, and converts electric energy input by a power supply into mechanical energy to be output as a resistance source; the motor driving unit drives a motor to run for a power inverter; the detection unit comprises a current-voltage detection circuit; the brake is a handle or a lever; the tension cable is connected with the brake and the motor; the energy discharging unit is used for releasing extra energy generated by acting when the brake acts on the motor; the client is a touch display screen for realizing man-machine interaction, and the torque output of the motor is changed by issuing a command to the controller, so that the running motor can flexibly simulate loads with different weights to achieve the purpose of body building and training.
In the first step, as shown in fig. 2, the PID control system model includes an error calculation module, an improved golf algorithm module, a current inner loop PID controller module, a speed outer loop PID controller module, a current detection module, a rotation speed detection module, and a permanent magnet synchronous motor module.
Further, the error calculation module is used for calculating a current error value and a rotation speed error value; the current inner loop PID controller module receives the current error value and outputs a motor reference current value; the speed outer ring PID controller module receives the rotating speed error value and outputs an expected current value of the motor; the current detection module and the rotating speed detection module are respectively used for detecting the current and the rotating speed of the motor.
Further, in the third step, the current inner loop PID controller and the speed outer loop PID controller are optimized by using an improved golf algorithm to obtain optimal Kp, ki, kd control parameters, as shown in fig. 3, the specific steps are as follows:
s1, encoding Kp, ki and Kd parameters of the PID controller into a solution of a golf algorithm search space.
S2, initializing parameters of an improved golf algorithm, including a population scale N, a maximum iteration number T, a space dimension d and searching an upper boundSearch for lower bound。
S3, calculating a current fitness value fitness of the individuals in the golf algorithm population, and recording a current iteration individual optimal solution, wherein a fitness function formula is as follows:
；
Wherein, Indicating the desired output value at time t,The actual output value at time t is shown, and J is the fitness value.
S4, in the algorithm exploration stage, a position update formula integrated with a random disturbance strategy is as follows:
（2）；
Wherein, Is based on the new location of the ith individual of the exploration phase,Is based on the position of the ith individual d dimension of the exploration phase,Is the individual optimal position in the d-th dimension,Is a collectionIs used for the random number in the random number code,Is the position of the ith individual in the d-th dimension, r is a random number between 0 and 1.
In the algorithm development stage, a position update formula of the self-adaptive dynamic operator w is added as follows:
（3）；
Wherein, Is based on the new location of the ith individual of the development phase,The position of the ith individual d dimension of the development stage is based on the other parameters as above.
And S5, calculating individual fitness values of the current iteration population according to the fitness function, and reserving an optimal solution according to a greedy selection strategy, namely discarding the current position and updating the current position as the candidate position if the fitness of the candidate position of the individual is better than that of the current position.
S6, judging whether the current iteration times T reach the maximum iteration times T, if so, stopping optimizing and outputting an optimal solution, and distributing the optimal solution to the PID controller as control parameters Kp, ki and Kd, otherwise, returning to S2 to continue optimizing.
FIG. 4 is a graph of the best fitness function of a basic golf algorithm versus a modified golf algorithm, with smaller fitness values representing better performance of a population of individuals, according to the criteria of better algorithm performance. It can be seen from the graph that the fitness value of the improved golf algorithm is generally smaller than that of the basic golf algorithm, and the improved golf algorithm can obtain better fitness value in the same iteration times; the improved golf algorithm solves the problems of early convergence and easy sinking into a local optimal solution of the basic golf algorithm, and shows that the method provided by the invention has better performance.
As can be seen from the comparison of the effect of the basic golf algorithm and the improved golf algorithm to optimize the PID parameters of fig. 5, the system using the improved golf algorithm to optimize the PID parameters has a lower overshoot than the system using the basic golf algorithm to optimize the PID parameters, a shorter rise time and adjustment time, and a faster response speed. It can be stated that the improved golf algorithm and PID control system combination provides better control performance and effectiveness than the basic golf algorithm and PID control system combination.
Claims (2)
1. The control method based on the programmable electric load shoulder pushing trainer comprises a system based on the programmable electric load shoulder pushing trainer and a PID control system, and is characterized by comprising the following steps:
Step one: constructing a system model and a PID control system model based on a programmable electric load shoulder-pushing trainer, wherein the system model based on the programmable electric load shoulder-pushing trainer comprises a power supply, a controller, an energy discharging unit, a detection unit, a permanent magnet synchronous motor, a motor driving unit, a tension cable, a brake and a client; the PID control system model comprises an error calculation module, an improved golf algorithm module, a current inner loop PID controller module, a speed outer loop PID controller module, a current detection module, a rotation speed detection module and a permanent magnet synchronous motor module; the controller is a control center of the whole system and is used for controlling the stable operation of the motor; the permanent magnet synchronous motor mainly realizes energy conversion, and converts electric energy input by a power supply into mechanical energy to be output as a resistance source; the motor driving unit drives a motor to run for a power inverter; the detection unit comprises a current-voltage detection circuit; the brake is a handle or a lever; the tension cable is connected with the brake and the motor; the energy discharging unit is used for releasing extra energy generated by acting when the brake acts on the motor; the client is a touch display screen for realizing man-machine interaction;
Step two: a model for improving the golf algorithm is constructed, which comprises two parts:
D1, merging a random disturbance strategy in an exploration stage of the algorithm, and improving the local searching and global searching capacity of the algorithm by disturbing the individual optimal position of each iteration of the algorithm;
D2, adding an adaptive dynamic operator w in the development stage of the algorithm to accelerate the convergence rate of the algorithm, as shown in a formula (1);
wherein T is the current iteration number, and T is the maximum iteration number;
Step three: optimizing the current inner loop PID controller and the speed outer loop PID controller by utilizing an improved golf algorithm to obtain optimal Kp, ki and Kd control parameters, wherein the method comprises the following specific steps of:
S1, encoding Kp, ki and Kd parameters of a PID controller into a solution of a golf algorithm search space;
S2, initializing parameters of an improved golf algorithm, including a population scale N, a maximum iteration number T and a space dimension d, searching an upper bound ub d and searching a lower bound lb d;
S3, calculating a current fitness value fitness of the individuals in the golf algorithm population, and recording a current iteration individual optimal solution, wherein a fitness function formula is as follows:
wherein ref (t) represents an expected output value at time t, act (t) represents an actual output value at time t, and J is an fitness value;
s4, in the algorithm exploration stage, a position update formula integrated with a random disturbance strategy is as follows:
Wherein, Is based on the new location of the ith individual of the exploration phase,/>Based on the position of the ith individual d dimension of the exploration phase, B d is the individual optimal position of the d dimension, I is a random number in the set {1,2}, x i,d is the position of the I individual d dimension, r is a random number between 0 and 1;
in the algorithm development stage, a position update formula of the self-adaptive dynamic operator w is added as follows:
Wherein, Is based on the new location of the ith individual of the development phase,/>Based on the position of the ith individual d dimension in the development stage, other parameters have the same meaning;
S5, calculating individual fitness values of the current iteration population according to the fitness function, and reserving an optimal solution according to a greedy selection strategy, namely discarding the current position and updating the current position as a candidate position if the fitness of the candidate position of the individual is better than that of the current position;
s6, judging whether the current iteration times T reach the maximum iteration times T, if so, stopping optimizing and outputting an optimal solution, and distributing the optimal solution to a PID controller as control parameters Kp, ki and Kd, otherwise, returning to S2 to continue optimizing;
step four: the control system uses the optimized control parameters to realize current and speed double closed-loop control.
2. The method for controlling the shoulder-pushing trainer based on the programmable electric load according to claim 1, wherein the client in the first step changes the torque output of the motor by issuing a command to the controller, so that the running motor can flexibly simulate loads with different weights to achieve the purpose of body-building training.
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