Method for controlling a six-degree-of-freedom platform

Abstract

The application discloses a control method of a six-degree-of-freedom platform, comprising the following steps: step one, six control systems are constructed, the control systems are one-to-one corresponding to six driving joints of the six-degree-of-freedom platform; wherein the control system comprises a CMAC model and a PID controller; step two, an overshoot, a damping ratio and a damped oscillation period of the control system at a current time are acquired and input into the CMAC model, and the CMAC model outputs a PID controller parameter change amount; wherein the parameter change amount comprises a proportional change amount, an integral change amount and an integral gain change amount; step three, the parameter change amount is input into the PID controller, and the PID controller outputs a driving joint control signal at a next time according to the adjusted parameters.

Description

Technical Field

[0001] This invention belongs to the field of six-degree-of-freedom platform technology, and specifically relates to a control method for a six-degree-of-freedom platform. Background Technology

[0002] Six-degree-of-freedom (DOF) parallel platforms are widely used in various motion simulation simulators. Currently, parallel six-DOF platforms commonly employ PID controllers for closed-loop control. However, traditional PID control suffers from significant errors, making it difficult to achieve satisfactory control results. While neural networks such as BP and RBP can be used for control, their large computational load and slow learning speed make real-time control difficult to achieve.

[0003] CMAC is a neural network model that simulates the function of the cerebellum. CMAC is an associative network where only a small subset of neurons (determined by the input) are associated with each output. Its associations exhibit local generalization ability, meaning similar inputs will produce similar outputs, while distant inputs will produce almost independent outputs. CMAC can be viewed as a tabular system for representing nonlinear mappings. Because its adaptive adjustment occurs in the linear mapping part, its learning algorithm is simple, converges much faster than backpropagation (BP), and avoids local minima. Summary of the Invention

[0004] The purpose of this invention is to provide a control method for a six-degree-of-freedom platform, which uses a CMAC model to adjust the parameters of the PID controller in real time, thereby improving the control accuracy of the six-degree-of-freedom platform.

[0005] Another objective of this invention is to determine the necessity of adjusting the PID controller parameters based on the error value and error change of the control system, which can improve the operating efficiency of the control system and reduce tracking delay.

[0006] The technical solution provided by this invention is as follows: A control method for a six-degree-of-freedom platform includes the following steps: Step 1: Construct six control systems, each corresponding one-to-one with one of the six drive joints of the six-degree-of-freedom platform; The control system includes a CMAC model and a PID controller; Step 2: Obtain the overshoot, damping ratio, and damped oscillation period of the control system at the current moment, and input them into the CMAC model. The CMAC model outputs the changes in PID controller parameters. The parameter changes include: proportional changes, integral changes, and integral gain changes; Step 3: Input the parameter change into the PID controller, and the PID controller outputs the drive joint control signal for the next moment based on the adjusted parameters.

[0007] Preferably, the control method for the six-degree-of-freedom platform further includes adjusting the proportional gain coefficient using the following formula, and using the adjusted proportional gain coefficient as the proportional gain coefficient of the PID controller. ; in, This represents the adjusted proportional gain coefficient. The proportional change output by the CMAC model The obtained proportional gain coefficient, This represents the proportional gain coefficient of the current PID controller. This indicates the overshoot of the control system at the current moment. The reference value represents the overshoot.

[0008] Preferably, before step two, the following steps are also included: Obtain the error value and error change of the control system at the current moment, and determine whether the parameters of the PID controller need to be adjusted based on the error value and error change. If parameter adjustment is required, continue to steps two and three. If parameter adjustment is not required, the PID controller directly uses the current parameters to output the drive joint control signal for the next moment.

[0009] Preferably, when or and At this time, the parameters of the PID controller need to be adjusted; when and At this time, there is no need to adjust the parameters of the PID controller; in, This represents the error value of the control system at the current moment. Indicates the allowable error threshold; This represents the change in error of the control system at the current moment. This indicates the threshold for the allowable error variation.

[0010] Preferably, before inputting the CMAC model, the following steps are also included: quantifying the overshoot, damping ratio, and damped oscillation period of the control system, respectively, using the following formulas: ; In the formula, Indicates input parameters , The times represent overshoot, damping ratio, and damped oscillation period, respectively. Indicates input parameters Quantization value, and These represent the input parameters respectively. The maximum and minimum values, Describe the input parameters The quantization series.

[0011] Preferably, the quantization level of the overshoot is 30 levels, the quantization level of the damping ratio is 16 levels, and the quantization level of the damped oscillation period is 22 levels.

[0012] Preferably, the overshoot, damping ratio, and damped oscillation period are determined by the system error curve.

[0013] The beneficial effects of this invention are: The control method for a six-degree-of-freedom platform provided by this invention improves the control accuracy of the six-degree-of-freedom platform by adjusting the parameters of the PID controller in real time using a CMAC model.

[0014] This invention also determines the necessity of adjusting PID controller parameters by analyzing the error value and error change of the control system. This can improve the operating efficiency of the control system and reduce tracking delay while ensuring the control accuracy of the six-degree-of-freedom platform, thereby further improving the tracking accuracy of the six-degree-of-freedom platform. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the control method for the six-degree-of-freedom platform described in this invention. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0017] like Figure 1 As shown, the present invention provides a control method for a six-degree-of-freedom platform, and the specific implementation method is as follows.

[0018] I. Constructing a Control System Since this invention targets the control of a parallel six-degree-of-freedom (6DOF) platform, which comprises six structurally identical and independent drive joints, controlling the platform requires controlling each of the six drive joints individually. Therefore, this invention constructs six control systems, each corresponding one-to-one with a drive joint of the platform; that is, each control system controls one drive joint. The control systems include a CMAC model and a PID controller. The input parameters of the CMAC model are set as the overshoot, damping ratio, and damped oscillation period of the control system, and the output parameter is the change in the PID controller parameters, i.e., the proportional gain. integral change and integral gain change .

[0019] II. Training the CMAC Model The generalization constant of the CMAC model was set to 32. The CMAC model was trained using an error correction method with a learning rate of 0.6. The spline function was used instead of the traditional ALBUS function as the basis function of the CMAC neural network. The ALBUS function outputs only 0 and 1, resulting in a segmented continuous curve that is continuous only between internal nodes, often discontinuous at the boundaries of internal nodes. The spline function effectively addresses this problem.

[0020] Before inputting the CMAC model, the overshoot, damping ratio, and damped oscillation period of the control system need to be quantized separately.

[0021] In this embodiment, the input parameters of the CMAC model are quantized using the following formula: ; In the formula, Indicates input parameters , The times represent overshoot, damping ratio, and damped oscillation period, respectively. Indicates input parameters Quantization value, and These represent the input parameters respectively. The maximum and minimum values, Describe the input parameters The quantization series.

[0022] In one embodiment, the quantization level of the overshoot is set to 30 levels, the quantization level of the damping ratio is set to 16 levels, and the quantization level of the damped oscillation period is set to 22 levels.

[0023] In one embodiment, a training step is performed at the end of each control cycle. The overshoot, damping ratio, and damped oscillation period observed in the previous control cycle are quantized and used as inputs to the CMAC model. The CMAC model is continuously trained until the training error meets the set requirements, at which point the training ends. The trained CMAC model can then be used for parameter tuning of the PID controller.

[0024] 3. Obtain the overshoot, damping ratio, and damped oscillation period of the control system at the current moment, and input them into the trained CMAC model. The trained CMAC model outputs the changes in PID controller parameters, including: proportional gain. integral change and integral gain change .

[0025] To reduce the control response delay of a six-degree-of-freedom platform, one embodiment further includes adjusting the proportional gain coefficient using the following formula, and using the adjusted proportional gain coefficient as the proportional gain coefficient of the PID controller. ; in, This represents the adjusted proportional gain coefficient. The proportional change output by the CMAC model The obtained proportional gain coefficient, This represents the proportional gain coefficient of the current PID controller. This indicates the overshoot of the control system at the current moment. The reference value represents the overshoot.

[0026] When the overshoot is small, the system stability is good. In this case, appropriately increasing the proportional gain can further improve the system's response speed, thereby reducing response delay and improving the tracking accuracy of the six-degree-of-freedom platform control. Conversely, when the overshoot is large, the system error is large and the system stability is poor. In this case, appropriately decreasing the proportional gain can further improve the system stability. In other words, by further adjusting the PID controller parameter changes output by the CMAC model according to the magnitude of the overshoot using the above formula, the real-time performance of the six-degree-of-freedom platform control can be further improved while ensuring the platform's stability. This allows the system to follow commands flexibly and robustly, thus improving the overall performance of the control system.

[0027] Fourth, the parameter changes output by the CMAC model are input into the PID controller, and the PID controller outputs the drive joint control signal for the next moment based on the adjusted parameters.

[0028] Each control system performs the above control process, thus realizing the control of the six-degree-of-freedom platform.

[0029] The overshoot, damping ratio, and damped oscillation period are determined by the system error curve. The system error curve can be obtained through pattern recognition using the time characteristics of the system error. Overshoot Damping ratio and decaying oscillation period Calculate using the following formulas respectively.

[0030] ; ; ; In the formula, , , These represent the first, second, and third peaks of the system error curve, respectively. and These represent the first peak value. and the third peak The moment it appears.

[0031] During control experiments, it was found that some cycles required very small adjustments. However, the adjustment process requires a complete calculation, which introduces a time delay compared to using a PID controller alone. This accumulated control delay over multiple control cycles can negatively impact tracking accuracy. To avoid tracking accuracy errors caused by pursuing high control precision, in one embodiment, the control method for the six-degree-of-freedom platform further includes: acquiring the current error value and error change of the control system, and determining whether the PID controller parameters need adjustment in the next cycle based on the error value and error change; if parameter adjustment is required, proceeding to steps two and three; if no parameter adjustment is required, the PID controller directly uses the current parameters to output the drive joint control signal for the next moment.

[0032] As an option, the rules for determining whether the parameters of the PID controller need to be adjusted are set as follows: when or and When this happens, the parameters of the PID controller need to be adjusted; when and At this time, it is not necessary to adjust the parameters of the PID controller; among them, This represents the error value of the control system at the current moment. Indicates the allowable error threshold; This represents the change in error of the control system at the current moment. This indicates the threshold for the allowable error variation.

[0033] This error value itself is the error after the control system has been adjusted, and it is within an acceptable range. However, research has found that if the error value at the current moment... A larger value indicates a certain risk of fluctuation in the control system, and the error may be even greater in the next moment. Therefore, choosing [a specific value]... As the most important factor in judgment, if If the set allowable error threshold is exceeded, to avoid fluctuations in control accuracy in the next control cycle, the parameters of the PID controller are adjusted using the CMAC model, and control is then performed using the PID controller with adjusted parameters. However, further research revealed that if only from... In its design, the impact of error fluctuations might be overlooked. Therefore, this invention also considers the error variation. As a judgment parameter, that is, when passed If the requirements are met, further judgment is made. The value, if The parameters are relatively large, and the risk of fluctuations still needs to be considered. Therefore, it is still necessary to adjust the parameters of the PID controller using the CMAC model.

[0034] Allowable error threshold and allowable error variation threshold The settings can be made based on the actual control accuracy requirements and in conjunction with experiments.

[0035] In one embodiment, the following is set , ;in, This represents the maximum acceptable error for the control system. Verification has shown that it achieves good control performance.

[0036] Example 1 This embodiment uses a pneumatic parallel six-degree-of-freedom platform as the controlled object for simulation control experiments. Each drive joint of the pneumatic parallel six-degree-of-freedom platform is a valve-controlled cylinder system with identical structures; therefore, only one set of drive joints, i.e., one set of pneumatic valve-controlled cylinder systems, is simulated. The mathematical model of each drive joint in the pneumatic parallel six-degree-of-freedom platform is as follows: ; in, This indicates the displacement output by the cylinder. , These are the piston areas of the rodless chamber and the rod chamber, respectively; The gas constant is This is the viscous damping coefficient of the piston and load. For the load and piston mass, Let be the isentropic exponent, and take... =1.4, It is the acceleration due to gravity. , These are the control voltages for the rodless chamber and rod chamber pressure control proportional valves, respectively. Standard temperature For standard volume, This indicates the volume of the rodless cavity. , These represent the spring constants of the internal springs in the rodless and rod-type chambers, respectively. , These represent the pressures in the rodless chamber and the rod chamber, respectively. This represents static friction or Coulomb friction. It is a complex variable.

[0037] The structural parameters of the pneumatic parallel six-degree-of-freedom platform are set as follows: upper platform diameter 0.5 m, lower platform diameter 0.7 m, and drive cylinder center length 1.024 m.

[0038] In each control cycle, the actual output of the control system at the current moment is collected in real time. And calculate the expected output. Compared with actual output The difference is used as the error value of the control system at the current moment. And calculate the error value at the current time (period). Error value compared to the previous time (period) The difference is used as the change in error of the controller system at the current moment. Determine whether it is necessary to adjust the parameters of the PID controller based on the established rules.

[0039] when or and When this happens, the parameters of the PID controller need to be adjusted; when and At this time, it is not necessary to adjust the parameters of the PID controller; setting , .

[0040] Simultaneously, the system error curve is acquired in each control cycle, and the peak value and time of the system error curve are obtained by eigenvalue identification. Based on the peak value and time, the overshoot, damping ratio and damped oscillation period of the control system are calculated.

[0041] If adjustments to the PID controller parameters are required, the overshoot, damping ratio, and damped oscillation period of the control system are quantized separately, and the quantized results are used as inputs to the CMAC model. The parameter changes output by the CMAC model are... , , The input is given to the PID controller, which outputs the drive joint control signal for the next moment based on the adjusted parameters.

[0042] The quantification formula is as follows: ; In the formula, Indicates input parameters , The times represent overshoot, damping ratio, and damped oscillation period, respectively. Indicates input parameters Quantization value, and These represent the input parameters respectively. The maximum and minimum values, Describe the input parameters The quantization series.

[0043] The quantization levels for overshoot are set to 30, damping ratio to 16, and damped oscillation period to 22.

[0044] Simultaneously, a control system employing only a PID controller and not a CMAC model was set up for comparative simulation experiments. The sampling period for both the comparative experiment and the example was set to 0.02s.

[0045] Verification showed that the control error range of using a PID controller alone was -0.01m to 0.01m, while the error range of using a combination of a CMAC model and a PID controller remained within -0.005m to 0.005m. This fully demonstrates that the control method for the six-degree-of-freedom platform provided by this invention, by adjusting the parameters of the PID controller in real time using a CMAC model, can improve the control accuracy of the six-degree-of-freedom platform. Even after a control time of 30 minutes, the control system used in this embodiment still maintained high tracking accuracy, with a time delay essentially equal to that of the control group using a PID controller alone. This indicates that the method provided by this invention can improve the operating efficiency of the control system and reduce tracking delay while ensuring the control accuracy of the six-degree-of-freedom platform.

[0046] Example 2 This embodiment uses a hydraulic parallel six-degree-of-freedom platform as the controlled object for simulation control experiments. The parameter setting process is basically the same as in Embodiment 1, and will not be repeated here.

[0047] Meanwhile, in this embodiment, the proportional gain coefficient is adjusted using the following formula; .

[0048] Verification showed that the error range of the hydraulic six-degree-of-freedom platform using a combination of the CMAC model and PID controller could be maintained within -0.005m to 0.005m. Furthermore, after adjusting the proportional gain coefficient using the aforementioned formula, the error range of the CMAC model combined with the PID controller could be maintained within -0.004m to 0.004m. The adjustment using the formula further improved the control accuracy of the six-degree-of-freedom platform.

[0049] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A control method for a six-degree-of-freedom platform, characterized in that, Includes the following steps: Step 1: Construct six control systems, each corresponding one-to-one with one of the six drive joints of the six-degree-of-freedom platform; The control system includes a CMAC model and a PID controller; Step 2: Obtain the overshoot, damping ratio, and damped oscillation period of the control system at the current moment, and input them into the CMAC model. The CMAC model outputs the changes in PID controller parameters. The parameter changes include: proportional changes. integral change and integral gain change ; Step 3: Input the parameter change into the PID controller, and the PID controller outputs the drive joint control signal for the next moment based on the adjusted parameters; It also includes adjusting the proportional gain coefficient using the following formula, and using the adjusted proportional gain coefficient as the proportional gain coefficient of the PID controller; ； in, This represents the adjusted proportional gain coefficient. The proportional change output by the CMAC model The obtained proportional gain coefficient, This represents the proportional gain coefficient of the current PID controller. This indicates the overshoot of the control system at the current moment. The reference value representing the overshoot; Before step two, the following is also included: Obtain the error value and error change of the control system at the current moment, and determine whether the parameters of the PID controller need to be adjusted based on the error value and error change. If parameter adjustment is required, continue to steps two and three. If parameter adjustment is not required, the PID controller directly uses the current parameters to output the drive joint control signal for the next moment.

2. The control method for a six-degree-of-freedom platform according to claim 1, characterized in that, when or and At this time, the parameters of the PID controller need to be adjusted; when and At this time, there is no need to adjust the parameters of the PID controller; in, This represents the error value of the control system at the current moment. Indicates the allowable error threshold; This represents the change in error of the control system at the current moment. This indicates the threshold for the allowable error variation.

3. The control method for a six-degree-of-freedom platform according to claim 2, characterized in that, Before inputting the CMAC model, the following steps are also included: quantifying the overshoot, damping ratio, and damped oscillation period of the control system, using the following formulas: ； In the formula, Indicates input parameters , The times represent overshoot, damping ratio, and damped oscillation period, respectively. Indicates input parameters Quantization value, and These represent the input parameters respectively. The maximum and minimum values, Describe the input parameters The quantization series.

4. The control method for a six-degree-of-freedom platform according to claim 3, characterized in that, The quantization levels for the overshoot are 30, the damping ratio is 16, and the decaying oscillation period is 22.

5. The control method for a six-degree-of-freedom platform according to claim 1, characterized in that, The overshoot, damping ratio, and damped oscillation period are determined by the system error curve.

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