Press storage position temperature control method based on PID (Proportion Integration Differentiation) algorithm

Abstract

The invention relates to the technical field of press heat dissipation, in particular to a press storage location temperature control method based on a PI D algorithm, and the method comprises the steps: 1, collecting a real-time temperature signal of a press storage location through a temperature sensor when the press storage location works normally, and transmitting the real-time temperature signal to a PLC control unit; 2, the temperature signal is fed back and transmitted to a PID controller through a PLC control unit, the PID controller outputs a control signal to regulate and control the rotating speed of a side face storage location fan of the press, and meanwhile storage location temperature information of the side face fan is collected and fed back to the PLC control unit; and 3, when the temperature of the storage location does not reach the parameter value set by the user, the PID controller outputs a control signal to the electric ball valve, the electric ball valve controls the opening degree of the ball valve according to the received control signal so as to adjust the water temperature, and meanwhile, the flowmeter records the real-time water inflow and feeds back a signal to the PID controller.

Description

Technical Field

[0001] The invention relates to the technical field of heat dissipation of a press machine, and in particular to a temperature control method of a press machine storage position based on a PID algorithm. Background Art

[0002] In the existing industrial manufacturing field, especially in processes involving metal forming, plastic injection molding, etc., the press machine is a key equipment, and its operating efficiency and stability directly affect the quality and production efficiency of the product. In order to maintain a suitable temperature in the working area of ​​the press machine and prevent equipment failure or product quality problems caused by overheating, electric fans are generally used to force ventilation and heat dissipation of water tanks or condensers on both sides of the press machine. The original intention of this design is to accelerate the dissipation of heat by enhancing air flow, thereby maintaining a stable temperature in the working environment of the press machine. However, the currently widely used electric fan system based on switch control has many shortcomings, which are specifically reflected in the following aspects:

[0003] Large temperature fluctuations and poor stability: The existing control system only controls the start and stop of the fan through simple switch logic, that is, when the real-time temperature of the storage location is lower than the preset process temperature threshold, the fan runs at full speed; once the temperature exceeds the threshold, the fan stops working immediately. This non-continuous control method causes the storage location temperature to frequently fluctuate around the set value, making it difficult to maintain within a stable range, which has an adverse effect on the working accuracy of the press and product quality.

[0004] High energy consumption and short equipment life: The frequent start and stop of the fan motor not only increases the power consumption, but also aggravates the mechanical wear of the motor components, thus shortening the service life of the fan. In the long run, it not only increases the maintenance cost, but also affects the overall stability and reliability of the production line.

[0005] Single control strategy and insufficient precision: The existing temperature control strategy lacks flexibility and cannot be fine-tuned according to the actual changes in the warehouse temperature. Even when the fan is running, it runs at full speed at a fixed speed and cannot dynamically adjust the wind speed according to the real-time temperature difference. As a result, the final warehouse temperature often deviates from the ideal temperature required by the process, making it difficult to meet the needs of high-precision production. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the present application provides a press storage temperature control method based on PID algorithm, which achieves stable, energy-saving and precise control of the storage temperature by introducing intelligent adjustment mechanism and refined control strategy.

[0007] The technical means adopted by the present invention to solve its technical problems is: a temperature control method for a press storage position based on a PID algorithm, and the improvement thereof is that the method comprises: step 1: when the press storage position is working normally, a temperature sensor is used to collect a real-time temperature signal of the press storage position and send it to a PLC control unit; step 2: the temperature signal is fed back to a PID controller through the PLC control unit, and the PID controller outputs a control signal to adjust the speed of the press storage position fan on the side, and at the same time collects the storage position temperature information of the side fan and feeds it back to the PLC control unit; step 3: when the storage position temperature does not reach the parameter value set by the user, the PID controller outputs a control signal to an electric ball valve, and the electric ball valve controls the opening and closing degree of the ball valve according to the received control signal to adjust the water temperature, and at the same time the flow meter records the real-time water inflow and feeds back the signal to the PID controller.

[0008] Step 2 described in the above technical solution includes: comparing the real-time temperature value of the press position measured by the press position temperature sensor with the set process requirement temperature value to obtain the press position temperature deviation E, and at the same time calculating the deviation change rate Ec. The position temperature deviation E and the deviation change rate Ec are processed by the PLC control unit through the fuzzy control algorithm and genetic algorithm, and then the output control signal is passed to the PID controller.

[0009] The parameter selection described in the above technical solution includes:

[0010] (1) PID controller controls three basic parameters:

[0011] For the three basic parameters of the PID controller, the set values ​​are in the range of Kp1 = [0,5], Ki1 = [0,5], and Kd1 = [0,1];

[0012] (2) Deviation E and deviation change rate Ec:

[0013] The Ec domain level of the deviation E and the deviation change rate is:

[0014] E, Ec={-5,-4,-3,-2,-1,0,1,2,3,4,5};

[0015] (3) Three basic parameters of fuzzy control:

[0016] The basic domain of the three parameters of fuzzy control is:

[0017] Kp, Ki, Kd=={-5,-4,-3,-2,-1,0,1,2,3,4,5};

[0018] (4) Three basic parameters after genetic algorithm optimization: Kp = Kp1 + ΔK1*K1, Ki = Ki2 + ΔK2*K2, Kd = Kd3 + ΔK3*K3, where Kp1, Ki2, Kd3 are the preset values ​​of the three parameters, ΔK1, ΔK2, ΔK3 are the outputs after fuzzy control, and K1, K2, K3 are the three proportional parameters after genetic algorithm optimization;

[0019] (5) Proportional parameters:

[0020] The value ranges of the three scale parameters are K1 = [0.5, 5], K2 = [0.5, 1], K3 = [0.5, 1];

[0021] (6) Objective function:

[0022] The objective function is:

[0023] Where, e(t) is the system deviation, u(t) is the output of the PID controller, and t u is the rise time, ω1, ω2, ω3 are weights;

[0024] (7) Fitness function:

[0025] The fitness function is the objective function:

[0026] In the above technical solution, when using genetic algorithm to find the optimal solution, the following method is adopted:

[0027] Assume that A1 is the best individual of the parent generation and A2 is the best individual of the offspring generation. If A1 is better than A2, retain A1; otherwise retain A2.

[0028] The PID controller in the above technical solution outputs a control signal to regulate the speed of the storage fan on the side of the press machine, including: in response to the control signal output by the PID controller, the drive motor of the storage fan adopts PWM pulse width modulation, and the speed of the drive motor is adjusted by adjusting the PWM duty cycle. When the speed is lower than the expected value, the duty cycle is increased; otherwise, the duty cycle is reduced.

[0029] The electric ball valve described in the above technical solution controls the opening and closing degree of the ball valve according to the received control signal and thus adjusts the water temperature, including: when the outlet water temperature is close to the parameter value set by the user, the PID controller outputs a signal to the electric ball valve, and the electric ball valve controls the opening and closing degree of the internal ball valve to appropriately increase according to the received signal, adjusts the opening size of the water valve and thus adjusts the water temperature, ensuring that the storage temperature is the user-set value.

[0030] The method described in the above technical solution also integrates a self-learning function, which dynamically optimizes the parameters of the PID controller by continuously collecting data during the production process and using a machine learning algorithm.

[0031] The self-learning function described in the above technical solution includes but is not limited to: pattern recognition based on historical data to predict future temperature change trends; and control strategy optimization based on reinforcement learning to optimize the control strategy of the PID controller through continuous trial and error and reward mechanism.

[0032] The beneficial effects of the present invention are: combining the multivariables of the fan and the water supply ball valve, performing PID speed regulation according to the read temperature value and the predicted temperature value, solving the temperature overshoot and fluctuation problems caused by the long sampling period, improving the accuracy and stability of PID speed regulation, and extending the temperature refresh time of some components from the design perspective. The PID algorithm solves the problem of storage insulation and achieves the purpose of energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of a method for controlling the temperature of a press bed based on a PID algorithm according to an embodiment of the present invention;

[0034] Figure 2 The present invention is a logic block diagram of a method for controlling the temperature of a press machine based on a PID algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0036] The following will clearly and completely describe the concept, specific structure and technical effects of the present invention in combination with the embodiments and drawings, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by technicians in this field without creative work are all within the scope of protection of the present invention. In addition, all the connection / connection relationships involved in the patent do not refer to the direct connection of components, but refer to the formation of a better connection structure by adding or reducing connection accessories according to the specific implementation situation. The various technical features in the invention can be combined interchangeably without conflicting with each other.

[0037] like Figure 1-2 As shown, the present application provides a method for controlling the temperature of a press bed based on a PID algorithm, the method comprising:

[0038] Step 1: When the press machine is working normally, the real-time temperature signal of the press machine is collected by the temperature sensor and sent to the PLC control unit.

[0039] The temperature sensor and the PLC control unit communicate with each other via 485 communication mode and Modbus communication protocol.

[0040] Step 2: The temperature signal is fed back to the PID controller through the PLC control unit. The PID controller outputs a control signal to adjust the speed of the fan at the side storage position of the press machine, and at the same time collects the storage position temperature information of the side fan and feeds it back to the PLC control unit.

[0041] Among them, the parameter gain of the PID controller is -1.865, the integral time is 0.001 (min), the differential time is 0.0 (min), and the sampling time is 0.1 (s).

[0042] Objective function Mn = Kc*e n +Ki*1∑n+M initial +Kd*(e n -e n-1 ).

[0043] Where Mn is the calculated value of the loop output at sampling time n, Kc is the loop gain, and e n is the loop deviation value at sampling time n, e n-1 is the deviation value of the previous loop (i.e., sampling time n-1), Ki is the proportional constant of the integral term, M initial is the initial value of the loop output, and Kd is the proportional constant of the differential term.

[0044] Next, the real-time temperature value of the press position measured by the press position temperature sensor is compared with the set process requirement temperature value to obtain the press position temperature deviation E, and the deviation change rate Ec is calculated at the same time. The position temperature deviation E and the deviation change rate Ec are processed by the PLC control unit through the fuzzy control algorithm and genetic algorithm, and then the output control signal is passed to the PID controller.

[0045] The fuzzy control algorithm is a control strategy based on fuzzy logic, which processes fuzzy information by describing fuzzy sets, thereby obtaining fuzzy output and performing control.

[0046] In a possible implementation, the present application provides a RBF fuzzy neural network to adjust the control parameters of a PID controller.

[0047] The RBF fuzzy neural network includes an input layer, a fuzzification layer, a fuzzy reasoning layer, a normalization layer and an output layer. The input variables are the deviation E and the deviation change rate Ec between the target temperature and the actual collected temperature, and the output variables are the proportional parameter Kp, the integral parameter Ki and the differential parameter Kd of the PID controller; the input layer is directly connected to the input variables; the fuzzification layer uses a membership function to fuzzify the output of the input layer; the fuzzy reasoning layer uses the output of the fuzzification layer to calculate the fitness of each fuzzy rule in the fuzzy rule base; the normalization layer performs normalization operation on the fitness of each fuzzy rule output by the fuzzy reasoning layer; the output layer assigns weights to the output of the normalization layer and sums them, and the output is the setting result of Kp, Ki and Kd.

[0048] The genetic algorithm is a computational model of the biological evolution process that simulates the natural selection and genetic mechanisms of Darwin's theory of biological evolution. It is a method of searching for the optimal solution by simulating the natural evolution process.

[0049] Its basic principles include the following aspects:

[0050] Initialize the population: Randomly generate a group of individuals as the initial population, each of which represents a potential solution to the problem.

[0051] Fitness evaluation: Each individual in the current population is evaluated through the fitness function (objective function) to determine its fitness. The fitness function evaluates the fitness of the individual and converts it into a numerical score for subsequent selection operations.

[0052] Selection operation: According to the size of fitness, several individuals are selected from the population as parents with a certain probability. Individuals with high fitness have a high probability of being selected, and individuals with low fitness are eliminated.

[0053] Crossover operation: The selected parent individuals are crossed (or called recombination) to generate new offspring individuals. The crossover operation simulates the gene recombination process in biological evolution, generating new gene combinations by exchanging some genes of the parent individuals.

[0054] Mutation operation: Randomly mutate the genes of offspring individuals to produce new genotypes and phenotypes. Mutation operation simulates the gene mutation process in biological evolution and increases the diversity of the population by introducing new genes.

[0055] Iterative evolution: Repeat fitness evaluation, selection, crossover and mutation operations to generate a new population. As the number of iterations increases, the individuals in the population gradually adapt to the environment, their quality is continuously improved, and eventually the optimal solution or approximate optimal solution to the problem is found.

[0056] This application uses the following method when using genetic algorithm to find the optimal solution:

[0057] Assume that A1 is the best individual of the parent generation and A2 is the best individual of the offspring generation. If A1 is better than A2, retain A1; otherwise retain A2.

[0058] The specific parameters of the PID controller include:

[0059] (1) PID controller controls three basic parameters:

[0060] For the three basic parameters of the PID controller, the set values ​​are in the range of Kp1 = [0,5], Ki1 = [0,5], and Kd1 = [0,1];

[0061] (2) Deviation E and deviation change rate Ec:

[0062] The Ec domain level of the deviation E and the deviation change rate is:

[0063] E, Ec={-5,-4,-3,-2,-1,0,1,2,3,4,5};

[0064] (3) Three basic parameters of fuzzy control:

[0065] The basic domain of the three parameters of fuzzy control is:

[0066] Kp, Ki, Kd=={-5,-4,-3,-2,-1,0,1,2,3,4,5};

[0067] (4) Three basic parameters after genetic algorithm optimization: Kp = Kp1 + ΔK1*K1, Ki = Ki2 + ΔK2*K2, Kd = Kd3 + ΔK3*K3, where Kp1, Ki2, Kd3 are the preset values ​​of the three parameters, ΔK1, ΔK2, ΔK3 are the outputs after fuzzy control, and K1, K2, K3 are the three proportional parameters after genetic algorithm optimization;

[0068] (5) Proportional parameters:

[0069] The value ranges of the three scale parameters are K1 = [0.5, 5], K2 = [0.5, 1], K3 = [0.5, 1];

[0070] (6) Objective function:

[0071] The objective function is:

[0072] Where, e(t) is the system deviation, u(t) is the output of the PID controller, and t u is the rise time, ω1, ω2, ω3 are weights;

[0073] (7) Fitness function:

[0074] The fitness function is the objective function:

[0075] Through the above steps, the temperature of the press position is controlled by the linkage of fuzzy control and PID control, and the three parameters of the PID controller are dynamically generated. Then, the real-time temperature value of the press position measured by the press position temperature sensor is compared with the set process requirement temperature value to obtain the press position temperature deviation E, and the deviation change rate Ec is calculated. Then, the genetic algorithm is used to select the appropriate fitness function in the optimization search to search the fitness values ​​of the three parameters of the PID controller, and the optimal solution is calculated to achieve optimal control.

[0076] In the above steps, the PID controller outputs a control signal to adjust the speed of the fan at the side storage position of the press machine, including:

[0077] In response to the control signal output by the PID controller, the drive motor of the storage fan adopts PWM pulse width modulation, and the speed of the drive motor is adjusted by adjusting the PWM duty cycle. When the speed is lower than the expected value, the duty cycle is increased; otherwise, the duty cycle is reduced.

[0078] The above method can realize precise control of the fan in the press storage position, solve the temperature overshoot and fluctuation problems caused by the long sampling period, and improve the accuracy and stability of PID speed regulation.

[0079] Step 3: When the storage temperature does not reach the parameter value set by the user, the PID controller outputs a control signal to the electric ball valve. The electric ball valve controls the opening and closing degree of the ball valve according to the received control signal to adjust the water temperature. At the same time, the flow meter records the real-time water inflow and feeds back the signal to the PID controller.

[0080] Specifically, when the outlet water temperature is close to the parameter value set by the user, the PID controller outputs a signal to the electric ball valve. The electric ball valve controls the internal ball valve to open and close appropriately according to the received signal, adjusts the water valve opening size and then adjusts the water temperature to ensure that the storage temperature is the user set value.

[0081] Optionally, in one possible implementation, the present application also integrates a self-learning function, which continuously collects data during the production process and uses a machine learning algorithm to dynamically optimize the parameters of the PID controller to adapt to temperature control requirements under different production conditions, thereby improving the system's adaptability and control accuracy.

[0082] In another possible implementation, the self-learning function includes but is not limited to: pattern recognition based on historical data to predict future temperature change trends; and control strategy optimization based on reinforcement learning to optimize the control strategy of the PID controller through continuous trial and error and reward mechanisms.

[0083] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for controlling the temperature of a press bed based on a PID algorithm, characterized in that: The method comprises: Step 1: When the press bed storage position is working normally, the real-time temperature signal of the press bed storage position is collected by using a temperature sensor and sent to the PLC control unit; Step 2: The temperature signal is fed back to the PID controller through the PLC control unit, and the PID controller outputs a control signal to adjust the speed of the fan at the side storage position of the press machine, and at the same time collects the storage position temperature information of the side fan and feeds it back to the PLC control unit; Step 3: When the storage temperature does not reach the parameter value set by the user, the PID controller outputs a control signal to the electric ball valve. The electric ball valve controls the opening and closing degree of the ball valve according to the received control signal to adjust the water temperature. At the same time, the flow meter records the real-time water inflow and feeds back the signal to the PID controller.

2. The method for controlling the temperature of a press bed based on a PID algorithm according to claim 1, characterized in that: The step 2 comprises: The real-time temperature value of the press position measured by the temperature sensor of the press position is compared with the set process requirement temperature value to obtain the temperature deviation E of the press position, and the deviation change rate Ec is calculated at the same time. The temperature deviation E and the deviation change rate Ec are processed by the PLC control unit through the fuzzy control algorithm and genetic algorithm, and then the output control signal is passed to the PID controller.

3. The method for controlling the temperature of a press bed based on a PID algorithm according to claim 2 is characterized in that: The parameter selection includes: (1) PID controller controls three basic parameters: For the three basic parameters of the PID controller, the set values ​​are in the range of Kp1 = [0,5], Ki1 = [0,5], and Kd1 = [0,1]; (2) Deviation E and deviation change rate Ec: The Ec domain level of the deviation E and the deviation change rate is: E, Ec={-5,-4,-3,-2,-1,0,1,2,3,4,5}; (3) Three basic parameters of fuzzy control: The basic domain of the three parameters of fuzzy control is: Kp, Ki, Kd=={-5,-4,-3,-2,-1,0,1,2,3,4,5}; (4) Three basic parameters after genetic algorithm optimization: Kp = Kp1 + ΔK1*K1, Ki = Ki2 + ΔK2*K2, Kd = Kd3 + ΔK3*K3, where Kp1, Ki2, Kd3 are the preset values ​​of the three parameters, ΔK1, ΔK2, ΔK3 are the outputs after fuzzy control, and K1, K2, K3 are the three proportional parameters after genetic algorithm optimization; (5) Proportional parameters: The value ranges of the three scale parameters are K1 = [0.5, 5], K2 = [0.5, 1], K3 = [0.5, 1]; (6) Objective function: The objective function is: Where, e(t) is the system deviation, u(t) is the output of the PID controller, and t u is the rise time, ω1, ω2, ω3 are weights; (7) Fitness function: The fitness function is the objective function:

4. The method for controlling the temperature of a press bed based on a PID algorithm according to claim 2, characterized in that: When using genetic algorithms to find the optimal solution, the following approach is used: Assume that A1 is the best individual of the parent generation and A2 is the best individual of the offspring generation. If A1 is better than A2, retain A1; otherwise retain A2.

5. The method for controlling the temperature of a press bed based on a PID algorithm according to claim 1, characterized in that: The PID controller outputs a control signal to adjust the speed of the fan at the side of the press machine, including: In response to the control signal output by the PID controller, the drive motor of the storage fan adopts PWM pulse width modulation, and the speed of the drive motor is adjusted by adjusting the PWM duty cycle. When the speed is lower than the expected value, the duty cycle is increased; otherwise, the duty cycle is reduced.

6. The method for controlling the temperature of a press bed based on a PID algorithm according to claim 1, characterized in that: The electric ball valve controls the opening and closing degree of the ball valve according to the received control signal to adjust the water temperature, including: When the outlet water temperature is close to the parameter value set by the user, the PID controller outputs a signal to the electric ball valve. The electric ball valve controls the internal ball valve to open and close appropriately according to the received signal, adjusts the water valve opening size and then adjusts the water temperature to ensure that the storage temperature is the user set value.

7. The method for controlling the temperature of a press bed based on a PID algorithm according to claim 1, characterized in that: This method also integrates a self-learning function, which dynamically optimizes the parameters of the PID controller by continuously collecting data from the production process and using a machine learning algorithm.

8. The method for controlling the temperature of a press bed based on a PID algorithm according to claim 7, characterized in that: The self-learning function includes but is not limited to: pattern recognition based on historical data to predict future temperature change trends; and control strategy optimization based on reinforcement learning to optimize the control strategy of the PID controller through continuous trial and error and reward mechanisms.

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