A chip temperature stability control method

Abstract

The present invention relates to a chip temperature stability control method; the method comprises: obtaining the difference between the temperature of the chip and a preset operating temperature; constructing a fractional-order PID controller, and constructing a multi-objective constrained fitness function and a single-objective fitness function based on the control time and the driving power; optimizing the parameters of the fractional-order PID controller using a multi-objective adaptive Hippo algorithm based on the fitness function; the multi-objective adaptive Hippo algorithm classifies populations and assigns corresponding fitness functions to different populations for position optimization, thereby achieving global optimization of the controller parameters; utilizing the fractional-order PID controller after parameter optimization, generating control parameters based on the difference between the temperature of the chip and the preset operating temperature; and heating the chip to the preset operating temperature based on the control parameters to complete chip temperature stability control. The present invention solves the problems of premature convergence and local optima in the optimization algorithms in the prior art, thereby improving the chip temperature stability control effect.

Description

Technical Field

[0001] The present invention belongs to the field of semiconductor technology, and in particular relates to a chip temperature stability control method. Background Art

[0002] Automotive chips such as clocks and inertial navigation systems can experience operating frequency drift due to ambient temperature fluctuations. In severe cases, this can lead to synchronization errors in sensor networks, impacting the normal operation and safety of vehicle systems. Research has shown that temperature control is one of the most effective methods to address frequency drift in these chips. Fractional-order PID extends the order of traditional PID from the integer domain to the real domain, achieving better temperature control and potentially further improving chip temperature stability. However, parameter tuning is challenging, necessitating a method for optimizing fractional-order PID parameters. Common optimization methods include particle swarm optimization, whale optimization, and hippopotamus optimization. For example, in 2019, Wei Lixin et al. proposed using a particle swarm optimization algorithm to improve the parameter selection process for fractional-order PID control of a rotating inverted pendulum, enhancing the stability and robustness of the control effect. In 2024, Zhang Yanli et al. proposed using the whale optimization algorithm to optimize the design of fractional-order PID controllers for first-order and second-order systems, demonstrating the effectiveness of swarm intelligence optimization algorithms for solving fractional-order controller parameter tuning problems. In 2024, Mohammad Hussien Amiri and others proposed the hippopotamus optimization algorithm (HO), which simulates the natural behavior of hippos and has the characteristics of strong evolutionary ability, fast search speed and strong optimization ability.

[0003] However, the above methods all have problems such as premature convergence and falling into local optimality, which limits the optimization effect of control parameters. If the existing optimization algorithm is directly used, it will affect the chip temperature stability control effect. Summary of the Invention

[0004] In view of the above analysis, the present invention aims to disclose a chip temperature stability control method, which solves the problems in the prior art that the optimization algorithm is prone to converge prematurely and fall into local optimality, and extends the standard Hippo algorithm to multi-objective and constrained conditions, thereby increasing the applicability and practical value of the algorithm and improving the chip temperature stability control effect. Moreover, this method does not require redesign of existing chips, thereby improving the versatility of the method and reducing the chip temperature control cost.

[0005] The purpose of the present invention is mainly achieved through the following technical solutions:

[0006] The present invention discloses a chip temperature stability control method, comprising:

[0007] Obtaining a difference between the temperature of the chip and a preset operating temperature; the preset operating temperature is a rated maximum operating temperature of the chip;

[0008] A fractional-order PID controller is constructed, and a multi-objective constrained fitness function and a single-objective fitness function are constructed based on the control time and driving power respectively;

[0009] Based on the fitness function, the parameters of the fractional-order PID controller are optimized by a multi-objective adaptive Hippo algorithm; the improved Hippo algorithm classifies the populations and assigns corresponding fitness functions to different populations for position optimization, thereby achieving global optimization of the controller parameters;

[0010] Using the parameter-optimized PID controller, control parameters are generated based on the difference between the temperature of the chip and the preset operating temperature; and based on the control parameters, the chip is heated to the preset operating temperature to complete the chip temperature stability control.

[0011] Furthermore, the optimization of the fractional-order PID controller parameters by the multi-objective adaptive Hippo algorithm includes:

[0012] S1: Constructing a corresponding hippo population based on the controller parameters of the fractional-order PID, and randomly initializing the positions of the hippopotamuses in the hippo population, wherein the hippo positions are possible solutions for the corresponding controller parameters;

[0013] S2: randomly dividing the hippo population into a male hippo population and an immature hippo population, and selecting a dominant male hippo from the male hippo population based on a multi-objective constrained fitness function, and selecting a temporary leader from the immature hippo population based on a single-objective fitness function;

[0014] S3: updating the position of the male hippo population based on the multi-objective constrained fitness function; and updating the position of the immature hippo population based on the single-objective fitness function;

[0015] S4: Exchange the population of the temporary leader with the non-dominant male hippopotamus after the position update; and repeat S2-S3 to iteratively optimize the position of each hippopotamus to obtain the optimal solution of the hippopotamus position corresponding to each controller parameter, which is the optimal PID controller parameter.

[0016] Furthermore, based on the multi-objective constrained fitness function, the position of the male hippo population is updated, including:

[0017] Based on the current position of each hippopotamus in the male hippopotamus population, the predicted position of each hippopotamus is generated by sequentially using the exploration phase position update strategy, the defense position update strategy, and the escape position update strategy in the hippopotamus algorithm;

[0018] Based on the multi-objective fitness function value corresponding to each predicted position, the optimal solution for the position of each hippopotamus is obtained; among them, the optimal solution obtained by the previous position update strategy is the initial position of the next update strategy. After each update strategy obtains the optimal solution for the position of each hippopotamus, the position of each hippopotamus is updated based on the optimal solution.

[0019] Furthermore, the updating of the position of the immature hippopotamus population based on the single-objective fitness function includes:

[0020] Generating a predicted exploration phase position of the immature hippopotamus population based on the temporary leader position, the dominant male hippopotamus position, and a preset adaptive factor, and updating the exploration phase position based on the corresponding single-target fitness value;

[0021] Based on the updated exploration phase position, each hippopotamus position is updated in turn through the defense position update strategy and the escape position update strategy of the hippopotamus algorithm and the fitness function.

[0022] Furthermore, the multi-objective constrained fitness function is expressed as:

[0023]

[0024] The single-objective fitness function is expressed as:

[0025] ITAE=∫t|e(t)|dt;

[0026] Where e(t) is the difference between the chip temperature at time t and the preset operating temperature, λ1 and λ2 are the weight coefficients of the control time and driving power; v c (t) is the control quantity at each moment; P is the power, P max is the maximum power limit; Vcc is the supply voltage; ts is the adjustment time; t0 is the stabilization time required by the chip design; Ess is the steady-state error; E0 is the maximum steady-state error required by the chip design; σ tc The overshoot of the system, T max It is the maximum overshoot that the chip can accept.

[0027] Furthermore, the temporary leader position in the immature hippopotamus population is obtained by the following formula:

[0028]

[0029] The predicted location of the immature hippo population during the exploration phase is generated using the following formula:

[0030]

[0031] Among them, X DFB is the position of the temporary leader, is the optimal hippo position in the immature hippo population, ITAEFBhippo is the optimal fitness value of the immature hippo obtained at the current number of iterations, and ITAEbest is the global optimal fitness value in the immature hippo population before this update; is the exploration stage position of the immature hippopotamus, X i,j is the current position of the i-th hippopotamus in the j-th parameter dimension, X Dhippo is the optimal hippopotamus position in the current iteration, c1 is the gathering factor, ω(t) is the adaptive factor, G i is the average position of multiple randomly selected hippos.

[0032] Furthermore, the temporary leader and the non-dominant male hippopotamus exchange populations, which is expressed as:

[0033]

[0034] in, To exchange the position of non-dominant male hippopotamus to the immature hippo population, For the non-dominant male hippopotamus to exchange positions before the population, is the position of the temporary leader after exchanging to the male hippo population, imaxFB and imaxM are the number of hippos in the immature hippo population and the male hippo population respectively; X DFB is the position of the temporary leader; X Dhippo is the global optimal hippopotamus position; r7 and r8 are random perturbation factors with equal probability in the interval [0,1].

[0035] Furthermore, the controller parameters include parameter K p , K i , K d , λ and μ; the fractional-order PID is expressed as:

[0036] u(t)=K p e(t)+K i s -λ e(t)+K d s -μ e(t);

[0037] Wherein, u(t) is the output of the fractional-order PID, e(t) is the difference between the chip temperature at time t and the preset operating temperature, K p is the proportionality coefficient, K i is the integral coefficient, K d is the differential coefficient, S is a complex frequency domain variable, and λ and μ are non-integer orders with respect to S.

[0038] Furthermore, the temperature of the chip is obtained by a temperature measuring resistor, and the chip is heated by a heating resistor;

[0039] The temperature measuring resistor and the heating resistor are both arranged on the surface of the chip in the form of metal films through secondary packaging.

[0040] Furthermore, based on the temperature measuring resistor and three low temperature coefficient resistors, a temperature measuring bridge is constructed to detect the chip temperature;

[0041] Based on the control parameters, the heating resistor is heated by a driving element to achieve heating of the chip.

[0042] The present invention can achieve at least the following beneficial effects:

[0043] 1. The present invention proposes an improved Hippopotamus optimization algorithm that incorporates population division, population communication, and target decomposition to solve practical engineering problems faced in tuning fractional-order PID parameters of vehicle-mounted chip temperature control systems under multi-objective and constrained conditions. The present invention first divides the immature hippos and male hippos of the Hippopotamus optimization algorithm into two sub-populations, achieving population division and target decomposition for optimization objectives with different degrees of constraints, thereby releasing the exploration characteristics of immature hippos. A temporary leader is set for the immature hippopotamus population and an adaptive gathering factor is introduced to guide the immature hippopotamus population to move. Population communication is also introduced, with the temporary leader added to the dominant male hippopotamus population, continuously providing a wide range of search capabilities for the male hippopotamus population, achieving population communication, improving the algorithm convergence speed, and avoiding falling into local optimality. The improved optimization algorithm of the present invention is used to tune the fractional-order PID parameters for vehicle-mounted chip temperature stability control, thereby improving the temperature control system effect of the chip under environments such as sudden temperature changes and rapid changes. The present invention also extends the standard Hippopotamus algorithm to multi-objective and constrained conditions, increasing the algorithm's applicability and practical value.

[0044] 2. The present invention improves the temperature stability of the chip by arranging temperature measuring resistors and heating resistors on the chip surface and keeping the chip operating at the rated maximum operating temperature. This improves the temperature control effect without changing the chip structure and reduces the control cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference symbols denote the same components.

[0046] Figure 1 is a flow chart of a chip temperature stability control method in an embodiment of the present invention;

[0047] Figure 2A schematic diagram of the arrangement positions of the temperature measuring resistor and the heating resistor in an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of the multi-objective adaptive Hippo algorithm flow in an embodiment of the present invention;

[0049] Figure 4 Schematic diagram of a chip temperature control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, which constitute a part of this application and are used to illustrate the principles of the present invention together with the embodiments of the present invention.

[0051] The embodiment of the present invention discloses a chip temperature stability control method, such as Figure 1 As shown, the following steps are included:

[0052] Step S1: obtaining a difference between the temperature of the chip and a preset operating temperature; the preset operating temperature is the rated maximum operating temperature of the chip;

[0053] Specifically, this embodiment takes the on-board clock chip as an example. In order to keep the clock chip at a constant temperature, in the absence of effective heat dissipation and cooling means, the temperature of the clock chip needs to be kept at a high level to avoid chip temperature fluctuations. For automotive-grade chips, the operating temperature range needs to meet -40°C to 125°C. Therefore, for automotive-grade chips, the operating temperature of the clock chip can be kept in an environment with a rated maximum operating temperature of 125°C to avoid the problem of unstable chip temperature caused by the inability to effectively cool down due to the increase in ambient temperature. In order to achieve the above functions, this embodiment obtains the temperature of the chip through a temperature measuring resistor and heats the chip through a heating resistor; the temperature measuring resistor and the heating resistor are both arranged on the surface of the chip in the form of a metal film through secondary packaging. Based on the temperature measuring resistor and three low temperature coefficient resistors, a temperature measuring bridge is constructed to detect the chip temperature; and the heating resistor is heated by a driving element to heat the chip; that is, in this embodiment, a temperature measuring resistor RT and a heating resistor RH are set on the chip surface. The temperature measuring resistor RT is used to measure the temperature of the chip and provide an input signal for the chip temperature control circuit. The heating resistor RH is driven by the output signal of the chip temperature control circuit to provide a constant temperature for the chip, thereby improving its temperature stability. Figure 2As shown, this embodiment packages the clock chip as a top-level module atop a bottom-level module, utilizing the bottom-level module for support. A temperature-measuring resistor RT and a heating resistor RH are attached to the top surface of the bottom-level module in the form of a metal film, contacting the core of the clock chip. The temperature-measuring resistor RT utilizes the metal's sensitivity to temperature to provide sensing, while a DC voltage is applied to the heating resistor RH, heating the clock chip through the electrothermal effect. Because the clock chip's internal physical dimensions are on the micrometer scale and it has a well-insulated outer shell, the internal temperature of the chip can be considered approximately uniform.

[0054] Step S2: construct a fractional-order PID controller, and construct a multi-objective constrained fitness function and a single-objective fitness function based on the control time and driving power respectively;

[0055] Specifically, in control system design, PID controllers are widely used due to their simple implementation, good control effect, and strong robustness. Fractional-order PID extends the order of traditional PID from the integer domain to the real domain, thereby achieving better temperature control effect. In order to further improve the temperature stability of the chip, this embodiment uses a fractional-order PID controller for temperature control based on the difference between the chip temperature detected by the temperature measuring resistor and the preset operating temperature.

[0056] Among them, the fractional-order PID controller parameters include parameter K p , K i , K d , λ and μ; the fractional-order PID is expressed as:

[0057] u(t)=K p e(t)+K i s -λ e(t)+K d s -μ e(t);

[0058] Wherein, u(t) is the output of the fractional-order PID, e(t) is the difference between the chip temperature at time t and the preset operating temperature, K p is the proportionality coefficient, K i is the integral coefficient, K d is the differential coefficient, s is the complex variable in the Laplace transform, and λ and μ are non-integer orders with respect to s.

[0059] It can be seen that integer-order PID is a special case of fractional-order PID where λ and μ are 1. Fractional-order PID can achieve more refined control performance settings. In a control system, steady-state error and adjustment time are the main parameters of system performance. ITAE (Time Absolute Error Score) is a commonly used indicator in control system design. This standard can be used to measure the dynamic and steady-state performance of the system. The smaller its value, the better the system performance. Therefore, ITAE is often used in existing control systems to measure the performance of the control system. This embodiment uses ITAE as a single-objective fitness function for the iterative optimization of the Hippo algorithm. The mathematical expression of ITAE is expressed as:

[0060] ITAE=∫t|e(t)|dt;

[0061] Wherein, t is the system operation time, and e(t) is the error at each moment. In this embodiment, it is the difference between the chip temperature at moment t and the preset operating temperature.

[0062] If the ITAE of the system is too high, it means that the control effect is poor. Therefore, this embodiment uses ITAE as a single-objective fitness function and optimizes all parameters in the system through iterative calculation to achieve the minimum ITAE and the optimal control effect.

[0063] However, for the chip temperature control application of this embodiment, in addition to control effect, power consumption cost is also an issue that needs to be considered in chip optimization. If only ITAE is used as the objective function, the optimization algorithm often requires huge heating and driving power to achieve the fastest chip speed and stability, which is contrary to the chip power consumption cost design requirements. Therefore, the purpose of this embodiment is to achieve the optimal compromise between control effect and power consumption cost. Therefore, a multi-objective constrained fitness function is proposed to iteratively optimize the Hippo algorithm.

[0064] Among them, the multi-objective constrained fitness function is expressed as:

[0065]

[0066] Where e(t) is the difference between the chip temperature at time t and the preset operating temperature, λ1 and λ2 are the weight coefficients of the control time and driving power, which are used to balance the control effect and power consumption cost; c (t) is the control quantity at each moment; P is the power, P max is the maximum power limit; Vcc is the supply voltage; ts is the adjustment time; t0 is the stabilization time required by the chip design; Ess is the steady-state error, and E0 is the maximum steady-state error required by the chip design; σ tc The overshoot of the system, T max It is the maximum overshoot that the chip can accept.

[0067] Step S3: Based on the fitness function, the parameters of the fractional-order PID controller are optimized by a multi-objective adaptive Hippo algorithm; the improved Hippo algorithm classifies the populations and assigns corresponding fitness functions to different populations for position optimization, thereby achieving global optimization of the controller parameters;

[0068] Specifically, through the above analysis, it can be seen that the parameter tuning of the fractional-order PID in this paper is relatively difficult, and it is necessary to complete the controller parameter tuning under multi-objective and multi-constraint conditions. In order to solve the problem that the parameters of the fractional-order PID are difficult to tune in the chip temperature control system. This embodiment proposes to tune the parameters of the fractional-order PID through an improved Hippo optimization algorithm. The Hippo optimization algorithm is proposed by simulating the natural behavior of hippos. It is a meta-heuristic algorithm. The algorithm has been proven to have the advantages of fast search and high precision. Existing studies have shown that its performance is better than traditional particle swarm, gray wolf and other algorithms. The standard Hippo algorithm optimizes and updates the control parameters through three stages: exploration position update, defense position update and escape position update.

[0069] Specifically, the multi-objective adaptive Hippo algorithm optimizes the parameters of the fractional-order PID controller through the following steps:

[0070] S31: constructing a corresponding hippo population based on each controller parameter of the fractional-order PID, and randomly initializing each hippo position in the hippo population, wherein the hippo position is a possible solution of the corresponding controller parameter;

[0071] S32: randomly dividing the hippo population into a male hippo population and an immature hippo population, and selecting a dominant male hippo from the male hippo population based on a multi-objective constrained fitness function, and selecting a temporary leader from the immature hippo population based on a single-objective fitness function;

[0072] S33: updating the position of the male hippo population based on the multi-objective constrained fitness function; and updating the position of the immature hippo population based on the single-objective fitness function;

[0073] S34: exchanging the population of the temporary leader with the non-dominant male hippopotamus after the position update;

[0074] S35: Repeat S33-S34 to iteratively optimize the position of each hippopotamus to obtain the optimal solution for the male hippopotamus position corresponding to the minimized multi-objective constrained fitness function value, which is the optimal fractional-order PID controller parameter.

[0075] More specifically, in the Hippo algorithm, the position of the Hippo represents the possible solutions for each PID parameter, and its initialization process is expressed as:

[0076] X i,j =Xi,j min +r1×(X i,j max -X i,j min );

[0077] Where, X i,j is the possible position of the i-th hippopotamus in the j-th control parameter dimension, j represents the number of variables, i.e. the j-th control parameter; r1 is a random number between [0,1]; X i,j max 、X i,j min are the upper and lower bounds of the hippopotamus position respectively.

[0078] Furthermore, the location of the male hippo population is updated by the following method:

[0079] Based on the current position of each hippopotamus in the male hippopotamus population, the predicted position of each hippopotamus is generated by sequentially using the exploration phase position update strategy, the defense position update strategy, and the escape position update strategy in the hippopotamus algorithm;

[0080] Based on the multi-objective fitness function value corresponding to each predicted position, the optimal solution for the position of each hippopotamus is obtained; among them, the optimal solution obtained by the previous position update strategy is the initial position of the next update strategy. After each update strategy obtains the optimal solution for the position of each hippopotamus, the position of each hippopotamus is updated based on the optimal solution.

[0081] The location of the immature hippo population is updated using the following method:

[0082] Generating a predicted exploration phase position of the immature hippopotamus population based on the temporary leader position, the dominant male hippopotamus position, and a preset adaptive factor, and updating the exploration phase position based on the corresponding single-target fitness value;

[0083] Based on the updated exploration phase position, each hippopotamus position is updated in turn through the defense position update strategy and the escape position update strategy of the hippopotamus algorithm and the single-objective fitness function.

[0084] More specifically, in the iterative process of the standard Hippo algorithm, the position of the hippopotamus is first updated through the exploration phase position update strategy of the first segment; in the exploration phase update strategy, each male hippopotamus X Mhippo It will be based on the dominant male hippo in the iteration, that is, the current extreme point X Dhippo Update its own position to show the gathering characteristics. The update formula is as follows:

[0085]

[0086] Where, is the position of the male hippopotamus; r2 is a random number between [0,1]; X Dhippo is the optimal hippo position before this iteration, that is, the position of the dominant male hippo; I1 is a random number in the interval [1, 2].

[0087] In addition, for immature Hippopotamus X FBhippo For example, due to their innate curiosity, they often show a tendency to move away from the group, and their position update in the exploration phase is expressed as:

[0088]

[0089] in, t is the current iteration number, τ is the maximum iteration number; r3 is a random number between [0,2]; I2 is a random number in the interval [1,2]; G i is the average position of multiple randomly selected hippos.

[0090] However, in this embodiment, in order to optimize the chip temperature fractional-order PID controller parameters, it is necessary to meet the two objectives of system control performance and minimum power consumption cost, and there are actual constraints. The existence of multiple objectives and constraints will lead to discontinuity of the feasible solution domain, so that the algorithm cannot cross the boundary of the feasible domain and fall into local optimality. The standard Hippo algorithm is proposed for single-objective function problems, and there is currently no relevant research on the Hippo algorithm with multiple objectives. For this reason, Figure 3 As shown, this embodiment proposes improvements to the standard Hippo algorithm by adding population division, population communication, and target decomposition. Male hippos and immature hippos in the standard Hippo algorithm are divided into two populations to achieve target decomposition by optimizing the objective function. The male hippo population is used as the dominant population to ensure the convergence of the results. The immature hippos are used as the subpopulation, and the objective function is simplified to a single-objective ITAE without considering the constraints. This further utilizes the exploration characteristics of immature hippos to explore more areas, and the population division is achieved through parallel operation of the two populations.

[0091] Specifically, before the immature hippopotamus population is updated during the exploration phase, a temporary leader X is first set for the immature hippopotamus population. DFB , causing immature hippos to gradually move closer to temporary leaders and move away from male groups to explore more areas.

[0092] Specifically, this embodiment obtains the temporary leader position in the immature hippopotamus population by the following formula:

[0093]

[0094] The exploration stage position of the immature hippo population is generated by the following formula:

[0095]

[0096] Among them, X DFB is the position of the temporary leader, is the optimal hippo position in the immature hippo population, ITAEFBhippo is the optimal fitness value of the immature hippo obtained at the current number of iterations, and ITAEbest is the global optimal fitness value in the immature hippo population before this update; Predicting the location of an immature hippopotamus during its exploration phase, X i,j is the current position of the i-th hippopotamus in the j-th parameter dimension, X Dhippo is the optimal hippopotamus position in the current iteration, c1 is the gathering factor, ω(t) is the adaptive factor, G i is the average position of multiple randomly selected hippos.

[0097] After obtaining the current exploration phase position of each hippo, the multi-objective constrained fitness function is used to determine whether the male hippo position obtained this time is better than the optimal fitness value before the update of this phase, and the single-objective fitness function is used to determine whether the immature hippo position obtained this time is better than the optimal fitness value before the update of this phase. If it is better, the hippo position obtained in this iteration is retained. If it is worse, the update of this phase is abandoned, as shown in the following formula:

[0098]

[0099] in, is the optimal position of the male hippopotamus after each iteration, is the optimal position of the immature hippo after each iteration, g represents the fitness function, gMhippo is the fitness value of the male hippo position obtained in the current stage, gbest is the fitness value of the optimal hippo position before this iterative update, ITAEFBhippo is the optimal fitness value of the immature hippo obtained in the current number of iterations, and ITAEbest is the global optimal fitness value in the immature hippo population before this update.

[0100] Furthermore, for both immature and male hippo populations, this embodiment uses the second-stage defense update strategy of the standard hippo algorithm to update the hippo position; the defense update strategy refers to the situation where hippos become targets of predators due to their deviation from the group. To resist predators, hippos rotate themselves toward the predators and use their powerful jaws and vocalizations to block and repel attackers. At this stage, hippos may behave in a way that approaches the predator to induce it to retreat, and the predator's position in the search space is Expressed as:

[0101]

[0102] Where r4 is a random number between [0,1].

[0103] Hippopotamus position during the defense update strategy phase Expressed as:

[0104]

[0105] Where RL is a random vector with Lévy distribution; ζ, c, d, g, and r4 are random numbers between [2, 4], [1, 1.5], [2, 3], and [-1, 1], respectively; is the fitness g or ITAE of the predator position in the population, g i For each hippo in its population, the corresponding fitness gMhippo or ITEAFBhippo, during which the hippopotamus To limit the range of activity, if it is less than g i , it indicates that the predator is very close to the hippopotamus, and the hippopotamus quickly turns towards the predator and moves towards it, causing it to retreat. Otherwise, the hippopotamus's defensive action is small; D is the distance from the hippopotamus to the predator, defined as follows:

[0106]

[0107] Among them, for male hippo populations, a is M, and for immature hippo populations, a is FB;

[0108] After obtaining the hippopotamus position of the defense update strategy, the multi-objective constrained fitness function is still used to determine whether the male hippopotamus position obtained this time is better than the optimal fitness value before the update of this stage, and the single-objective fitness function is used to determine whether the immature hippopotamus position obtained this time is better than the optimal fitness value before the update of this stage. If they are better, the male hippopotamus and immature hippopotamus positions of this iteration are retained respectively. If the results are worse, the update of this stage is abandoned, as shown in the following formula:

[0109]

[0110] Among them, gMhippor is the fitness value of the male hippo position obtained at the current iteration number of the defense phase, gbest is the fitness value of the optimal hippo position before this iterative update, ITAEFBhippor is the optimal fitness value of the immature hippo obtained at the current iteration number of the defense phase, and ITAEbest is the global optimal fitness value in the immature hippo population before this update.

[0111] Furthermore, the third stage is the escape position update strategy stage. Another option for the hippopotamus is to try to leave the danger and try to run to a nearby cluster to avoid harm from the predator. This strategy allows the hippopotamus to find a safe location near its current location, thereby enhancing the utilization ability in local search. Its calculation method is:

[0112]

[0113] Where X local min 、X local max are the upper and lower bounds of the current safe community location, r5 is a random number between [0,1]; r6 is a random number with a normal distribution.

[0114] After obtaining the hippopotamus position in the escape position update strategy phase, the multi-objective constrained fitness function is still used to determine whether the male hippopotamus position obtained this time is better than the optimal fitness value before the update in this phase, and the single-objective fitness function is used to determine whether the immature hippopotamus position obtained this time is better than the optimal fitness value before the update in this phase. If they are better, the male hippopotamus and immature hippopotamus positions of this iteration are retained respectively. If the results are worse, the update in this phase is abandoned, as shown in the following formula:

[0115]

[0116] Among them, gMhippoε is the fitness value of the male hippo position obtained at the current iteration number of the escape phase, gbest is the fitness value of the optimal hippo position before this iterative update, ITAEFBhippoε is the optimal fitness value of the immature hippo obtained at the current iteration number of the escape phase, and ITAEbest is the global optimal fitness value in the immature hippo population before this update.

[0117] In order to prevent the overall parameter optimization process from falling into a local optimum, this embodiment uses immature hippos to provide new individuals for male hippos, thereby generating information exchange and realizing population communication; that is, after the escape update strategy stage, a population communication link is added, the temporary leader of the immature hippopotamus is exchanged with a male hippopotamus in the population, and the temporary leader is added to the dominant male hippopotamus population, continuously providing a wide range of search capabilities for the male hippopotamus population, and a non-dominant male hippopotamus is added to the immature hippopotamus population for the next round of iterative update;

[0118] Preferably, the temporary leader and the non-dominant male hippopotamus are exchanged for populations using the following formula:

[0119]

[0120] in, To exchange the position of non-dominant male hippopotamus to the immature hippo population, For the non-dominant male hippopotamus to exchange positions before the population, is the position of the temporary leader after exchanging to the male hippo population, imaxFB and imaxM are the number of hippos in the immature hippo population and the male hippo population respectively; X DFB is the position of the temporary leader; X Dhippo is the global optimal hippopotamus position; r7 and r8 are random perturbation factors with equal probability in the interval [0,1].

[0121] After multiple iterative updates, the optimal solution for the male hippopotamus position corresponding to the minimized multi-objective constrained fitness function value is obtained as the optimal fractional-order PID controller parameters. This embodiment uses a single-objective, unconstrained fitness function to increase the exploratory nature of the overall algorithm, helping the hippopotamus algorithm to escape the feasible domain island caused by constraints and multiple objectives. Furthermore, through population communication, the feasible solution of immature hippopotamuses is continuously transferred to the male hippopotamus population to provide more feasible domain information, thereby improving the optimization effect of the algorithm.

[0122] Step S4: Based on the difference between the temperature of the chip and the preset operating temperature, a control parameter is generated by using the optimized PID controller; and based on the control parameter, the chip is heated to the preset operating temperature to complete the chip temperature stability control;

[0123] Specifically, such as Figure 4 As shown, the parameters of the optimized fractional-order PID are K p , K i , K d , λ and μ, when there is an error between the preset chip operating temperature and the chip temperature fed back by the measuring resistor, the fractional-order PID controller is based on the input temperature error and the optimized controller parameter K p , K i , K d , λ and μ, calculate the control quantity and drive the subsequent heating resistors to stabilize the temperature of the controlled object, i.e. the chip, at the preset temperature.

[0124] In summary, the chip temperature stability control method of the present invention improves the temperature stability of the chip by setting a temperature measuring resistor and a heating resistor on the chip surface and making the chip always operate at the rated maximum operating temperature. Through fractional-order PID control, the temperature stability of the chip is improved, the temperature control effect is improved without changing the chip structure, and the control cost is reduced. The present invention also proposes an improved Hippo optimization algorithm that incorporates population division, population communication, and target decomposition to solve the practical engineering problems faced in the fractional-order PID parameter tuning of the on-board chip temperature control system under multi-objective and constraint conditions. The present invention first divides the immature and mature hippos of the Hippo optimization algorithm into two sub-populations, and implements population division and target decomposition for optimization goals with different degrees of constraints, thereby releasing the exploration characteristics of immature hippos; a temporary leader is set for the immature hippo population and an adaptive gathering factor is introduced to guide the immature hippo population to move; and population communication is added, and the temporary leader is added to the dominant male hippo population, continuously providing a large-scale search capability for the male hippo population, realizing population communication, improving the algorithm convergence speed, and avoiding falling into local optimality. The improved optimization algorithm of the present invention is used to adjust the fractional-order PID parameters for the temperature stabilization control of the vehicle-mounted chip, thereby improving the temperature control effect of the chip under environments such as sudden temperature changes and rapid changes.

[0125] Those skilled in the art will appreciate that all or part of the process steps of the methods in the above embodiments can be implemented by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0126] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A chip temperature stability control method, characterized in that: include: Obtaining a difference between the temperature of the chip and a preset operating temperature; the preset operating temperature is a rated maximum operating temperature of the chip; A fractional-order PID controller is constructed, and a multi-objective constrained fitness function and a single-objective fitness function are constructed based on the control time and driving power respectively; Based on the fitness function, the parameters of the fractional-order PID controller are optimized by a multi-objective adaptive Hippo algorithm; the improved Hippo algorithm classifies the populations and assigns corresponding fitness functions to different populations for position optimization, thereby achieving global optimization of the controller parameters; The optimization of the fractional-order PID controller parameters by the multi-objective adaptive Hippo algorithm includes: S1: Constructing a corresponding hippo population based on the controller parameters of the fractional-order PID, and randomly initializing the positions of the hippopotamuses in the hippo population, wherein the hippo positions are possible solutions for the corresponding controller parameters; S2: randomly dividing the hippo population into a male hippo population and an immature hippo population, and selecting a dominant male hippo from the male hippo population based on a multi-objective constrained fitness function, and selecting a temporary leader from the immature hippo population based on a single-objective fitness function; S3: updating the position of the male hippo population based on the multi-objective constrained fitness function; and updating the position of the immature hippo population based on the single-objective fitness function; S4: exchanging the temporary leader with the non-dominant male hippopotamus after the position update; S5: Repeat S3-S4, iteratively optimize the position of each hippopotamus, and obtain the optimal solution of the male hippopotamus position corresponding to the minimized multi-objective constrained fitness function value, which is the optimal fractional-order PID controller parameter; The multi-objective constrained fitness function is expressed as: ； The single-objective fitness function is expressed as: ； in, is the difference between the chip temperature at time t and the preset operating temperature, and The weight coefficient for regulating time and driving power; is the control quantity at each moment; is power, is the maximum power limit; is the supply voltage; It is the adjustment time; It is the stabilization time required by chip design; is the steady-state error, is the maximum steady-state error required by chip design; The overshoot of the system, It is the maximum overshoot that the chip can accept; Using a fractional-order PID controller with optimized parameters, a control parameter is generated based on the difference between the temperature of the chip and the preset operating temperature; and based on the control parameter, the chip is heated to the preset operating temperature to complete the chip temperature stability control.

2. The chip temperature stability control method according to claim 1, characterized in that: Based on the multi-objective constrained fitness function, the position of the male hippo population is updated, including: Based on the current position of each hippopotamus in the male hippopotamus population, the predicted position of each hippopotamus is generated by sequentially using the exploration phase position update strategy, the defense position update strategy, and the escape position update strategy in the hippopotamus algorithm; Based on the multi-objective fitness function value corresponding to each predicted position, the optimal solution for the position of each hippopotamus is obtained; among them, the optimal solution obtained by the previous position update strategy is the initial position of the next update strategy. After each update strategy obtains the optimal solution for the position of each hippopotamus, the position of each hippopotamus is updated based on the optimal solution.

3. The chip temperature stability control method according to claim 1, characterized in that: The method of updating the position of the immature hippopotamus population based on the single-objective fitness function includes: Generating a predicted exploration phase position of the immature hippopotamus population based on the temporary leader position, the dominant male hippopotamus position, and a preset adaptive factor, and updating the exploration phase position based on the corresponding single-target fitness value; Based on the updated exploration phase position, each hippopotamus position is updated in turn through the defense position update strategy and the escape position update strategy of the hippopotamus algorithm and the fitness function.

4. The chip temperature stability control method according to claim 3, characterized in that: The temporary leader position in the immature hippo population is obtained by the following formula: ； The predicted location of the immature hippo population during the exploration phase is generated using the following formula: ； ； in, is the position of the temporary leader, For the exploration stage position of immature hippos, is the optimal fitness value of the immature hippopotamus obtained at the current number of iterations, is the global optimal fitness value of the immature hippo population before this update; is the current position of the i-th hippopotamus in the j-th parameter dimension, is the dominant hippo position in the current iteration, is the gathering factor, is the adaptive factor, is the average position of multiple randomly selected hippos; is a random number between [0,2]; is a random number in the interval [1, 2]; is the maximum number of iterations.

5. The chip temperature stability control method according to claim 1, characterized in that: The pair of the temporary leader and the non-dominant male hippopotamus exchange populations, expressed as: + ； ； in, To exchange the position of non-dominant male hippopotamus to the immature hippo population, For the non-dominant male hippopotamus to exchange positions before the population, To exchange for the position of temporary leader of the male hippopotamus group, 、 are the number of hippos in the immature hippo population and the male hippo population, respectively; the location of the temporary leader; is the global optimal hippo position; 、 is a random disturbance factor with equal probability in the interval [0,1].

6. The chip temperature stability control method according to claim 1, characterized in that: The controller parameters include parameters 、 、 , λ and μ; the fractional-order PID is expressed as: ； in, is the output of the fractional-order PID, is the difference between the chip temperature at time t and the preset operating temperature, is the proportionality coefficient, is the integral coefficient, is the differential coefficient, s is the complex variable in the Laplace transform, and λ and μ are non-integer orders with respect to s.

7. The chip temperature stability control method according to claim 1, characterized in that: Obtaining the temperature of the chip through a temperature measuring resistor, and heating the chip through a heating resistor; The temperature measuring resistor and the heating resistor are both arranged on the surface of the chip in the form of metal films through secondary packaging.

8. The chip temperature stability control method according to claim 7, characterized in that: Based on the temperature measuring resistor and three low temperature coefficient resistors, a temperature measuring bridge is constructed to detect the chip temperature; Based on the control parameters, the heating resistor is heated by a driving element to achieve heating of the chip.

Images