Configurable precise motion control method and device based on hardware and medium

Abstract

The invention discloses a configurable precise motion control method and device based on hardware and a medium, and relates to the technical field of motion control, and the method comprises the steps: combining a control operator set and port information into a control structure topology through combining configuration instructions, and issuing the control structure topology to a reconfigurable hardware structure for connection mapping and scheduling among control operators, generating a combined control structure configuration through rapid loading and combination of heterogeneous control logic; the control signal is input to the driving actuator, the motion state of the driving actuator is collected in real time, and actuator feedback data are formed; and receiving and analyzing feedback data of the actuator, comparing a control target with an actual response, and outputting a control performance evaluation result. By extracting motion control operators and operation constraint parameters from an embedded industrial function library, constructing a multi-dimensional parameter mapping space and performing visual analysis and structured combination, rapid indexing, accurate mapping and visual configuration of the control operators are realized, and correct parameter relation and easiness in adjustment are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of motion control technology, and in particular to a hardware-based configurable precision motion control method, device, and medium. Background Technology

[0002] With the development of industrial automation, robotics, and precision manufacturing, motion control technology has been widely applied in many fields. Existing motion control systems typically consist of motion controllers, actuators, and sensors, managing and adjusting motion parameters such as position, velocity, and acceleration through preset control algorithms. In recent years, the introduction of programmable logic devices, system-on-a-chip (SoCs), and high-performance embedded processors has enabled motion control systems to not only achieve high-speed computation but also support, to a certain extent, customized configuration of hardware-level control algorithms.

[0003] While existing modular motion control technologies have seen initial applications in hardware programmability and control algorithm reuse, the loading and configuration of control operators still heavily rely on offline compilation or manual script adjustments, lacking rapid, visual configuration and structured management methods for different control tasks. Furthermore, the connection relationships, port information, and execution timing between control operators are often defined statically, requiring significant redeployment time during task switching, control structure adjustments, or hardware resource reconfiguration, impacting system real-time performance and flexibility. Especially in scenarios involving multi-source sensor data fusion and heterogeneous control logic collaborative execution, existing methods have room for improvement in the dynamics of mapping and scheduling between control operators. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a hardware-based configurable precision motion control method to solve the problem of the lack of fast visual configuration and dynamic mapping scheduling of control operators in reconfigurable hardware.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a hardware-based configurable precision motion control method, which includes loading motion control operators, performing visual configuration and structured operation on the functional parameters of motion control operators, and generating a set of control operators and port information.

[0008] By combining configuration instructions, the set of control operators and port information are combined into a control structure topology, and then sent to the reconfigurable hardware structure for connection mapping and scheduling between control operators. Through the rapid loading and combination of heterogeneous control logic, a combined control structure configuration is generated.

[0009] The system collects raw sensor data and inputs it into the combined control structure configuration. According to the preset timing sequence and control operator connection relationship, it executes control logic at all levels in the reconfigurable hardware structure and outputs control signals.

[0010] The control signal is input to the drive actuator, and the motion status of the drive actuator is collected in real time to form actuator feedback data.

[0011] Receive and analyze actuator feedback data, compare the control target with the actual response, and output control performance evaluation results.

[0012] As a preferred embodiment of the hardware-based configurable precision motion control method of the present invention, the steps of loading motion control operators, visually configuring and structurally operating the functional parameters of the motion control operators, and generating a set of control operators and port information are as follows.

[0013] Motion control operators and running constraint parameters are extracted from the embedded industrial function library, and the operator resource mapping index is established through fast indexing via hash reflection mechanism. Then, multi-dimensional structural parameter tensor construction operation is performed to generate parameter mapping space.

[0014] The parameter mapping space is visualized, parsed, and combined in a structured manner to obtain a structured operator graph. Then, topology parsing and interface mapping operations are performed to generate a set of control operators and port information.

[0015] As a preferred embodiment of the hardware-based configurable precision motion control method of the present invention, the following steps are taken: By combining configuration instructions, the set of control operators and port information are combined into a control structure topology, which is then sent to a reconfigurable hardware structure for connection mapping and scheduling between control operators. Through the rapid loading and combination of heterogeneous control logic, a combined control structure configuration is generated.

[0016] Industrial control software is used to perform structured modeling of the control operator set and port information, graphical connection and scheduling strategy setting, generate combined configuration instructions, and perform syntax and semantic analysis of the combined configuration instructions through a parser to generate a connection scheduling mapping table.

[0017] The system parses the control operator dependencies and priority weights in the connection scheduling map, uses weighted topological sorting to generate the optimal execution sequence, and performs instruction compilation and resource binding operations to generate a hardware execution image.

[0018] The resource allocation, connection relationships and scheduling logic of the control operators in the reconfigurable hardware structure are extracted by hardware execution image extraction, and then unified and formatted to generate a combined control structure configuration.

[0019] As a preferred embodiment of the hardware-based configurable precision motion control method of the present invention, the steps of extracting the resource allocation, connection relationships, and scheduling logic of the control operator in the reconfigurable hardware structure through hardware execution image extraction, and performing unified integration and formatting operations to generate a combined control structure configuration are as follows.

[0020] The control operator resource allocation information, physical connection relationship and scheduling information are extracted from the hardware execution image, and fine-grained resource topology and scheduling data are obtained through parsing and verification operations.

[0021] Resource conflict detection and scheduling consistency analysis are performed on fine-grained resource topology and scheduling data. The conflict identification results are output, and dynamic remapping and scheduling optimization calculations are performed to obtain the resource scheduling scheme.

[0022] The resource scheduling schemes are unified, integrated, and encoded in a structured format to generate a combined control structure configuration.

[0023] As a preferred embodiment of the hardware-based configurable precision motion control method described in this invention, the steps of acquiring raw sensor data and inputting it into a combined control structure configuration, executing control logic at various levels in the reconfigurable hardware structure according to a preset timing sequence and control operator connection relationship, and outputting control signals are as follows:

[0024] Raw sensor data is acquired in real time, timestamped, and multidimensional sensor datasets are generated. Adaptive filtering, denoising, and normalization are then performed to generate multidimensional feature vectors.

[0025] Based on the connection relationship and timing of the control operators in the combined control structure configuration, each control operator is triggered to execute control logic on the multidimensional feature vector to generate an intermediate control instruction set; the intermediate control instruction set is fused based on nonlinear fractional differential operations through a reconfigurable hardware structure to generate control signals.

[0026] As a preferred embodiment of the hardware-based configurable precision motion control method of the present invention, the steps of inputting control signals to the drive actuator, collecting the motion state of the drive actuator in real time, and forming actuator feedback data are as follows:

[0027] The control signal is input to the drive actuator through a high-speed digital interface, the motion state parameters are collected in real time, and the motion state parameters are timestamped using a synchronous clock to generate multi-dimensional motion state data.

[0028] Multidimensional motion state data is fused and anomaly detected to generate feedback data, which is then formatted according to a preset protocol to generate actuator feedback data.

[0029] As a preferred embodiment of the hardware-based configurable precision motion control method of the present invention, the specific steps for receiving and analyzing actuator feedback data, comparing the control target with the actual response, and outputting control performance evaluation results are as follows:

[0030] The system receives and parses the feedback data from the actuator, extracts the motion position, velocity, acceleration, and torque response features, and obtains a multidimensional response feature sequence through timestamp synchronization and sampling point reconstruction.

[0031] Multidimensional error time series is obtained by aligning the multidimensional response feature sequence with the preset control target and obtaining the multidimensional error time series. Then, the time offset is accurately corrected by the nonlinear dynamic time warping algorithm to generate a comprehensive error metric.

[0032] The comprehensive error metric is subjected to nonlinear mapping calculation and normalization aggregation to generate control performance evaluation results.

[0033] As a preferred embodiment of the hardware-based configurable precision motion control method of the present invention, the specific steps for performing nonlinear mapping calculation and normalization aggregation operations on the comprehensive error metric to generate control performance evaluation results are as follows:

[0034] The comprehensive error metric is preprocessed using a nonlinear mapping function to generate a smooth normalized error map.

[0035] The smoothed normalized error map is combined with the multidimensional response feature sequence, and a weighted normalized aggregation calculation is performed to generate a weighted aggregation result. Then, a normalized exponential function mapping is performed to generate a control performance evaluation result.

[0036] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the hardware-based configurable precision motion control method as described in the first aspect of the present invention.

[0037] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the hardware-based configurable precision motion control method as described in the first aspect of the present invention.

[0038] The beneficial effects of this invention are as follows: By extracting motion control operators and operational constraint parameters from embedded industrial function libraries, constructing a multi-dimensional parameter mapping space, and performing visual analysis and structured combination, rapid indexing, accurate mapping, and intuitive configuration of control operators are achieved, ensuring correct parameter relationships and ease of adjustment. Furthermore, by using industrial control software to perform structured modeling, graphical connection, and scheduling strategy setting for the control operator set and port information, and combining weighted topology sorting to generate the optimal execution sequence and distribute it to the reconfigurable hardware structure, the overall optimization of resource allocation, connection paths, and execution order is achieved, thereby shortening the deployment cycle from algorithm design to hardware operation and improving the system's adaptability, real-time performance, and scalability. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a hardware-based, configurable, precision motion control method.

[0041] Figure 2 Flowchart for loading and configuring parameters for motion control operators.

[0042] Figure 3 Flowchart for control structure topology generation and hardware image construction.

[0043] Figure 4 A flowchart for sensor data processing and control signal generation. Detailed Implementation

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0047] Reference Figures 1-4 This is one embodiment of the present invention, which provides a hardware-based configurable precision motion control method, comprising the following steps:

[0048] S1. Load motion control operators, perform visual configuration and structured operations on the function parameters of motion control operators, and generate a set of control operators and port information.

[0049] S1.1 Extract motion control operators and running constraint parameters from the embedded industrial function library, quickly index them using a hash reflection mechanism, establish an operator resource mapping index, and perform multi-dimensional structural parameter tensor construction operations to generate a parameter mapping space.

[0050] Furthermore, motion control operators and operational constraint parameters are extracted one by one from the embedded industrial function library. First, a preset hash reflection mechanism is used to quickly index the motion control operators and operational constraint parameters in the embedded industrial function library. During the indexing process, corresponding hash key-value pairs are generated based on the motion control operator name and operational constraint parameter identifier, and an operator resource mapping index for motion control operators and operational constraint parameters is established according to the key-value order.

[0051] After the operator resource mapping index is established, the motion control operator function parameters in the operator resource mapping index are arranged and combined in multiple dimensions to form a multi-dimensional structural parameter tensor with row, column and hierarchical dimensions. By filling the multi-dimensional structural parameter tensor with the associated values ​​of motion control operator function parameters and corresponding running constraint parameters, the mapping relationship between parameters in each dimension is constructed, and finally the parameter mapping space is generated.

[0052] It should also be noted that the embedded industrial function library is derived from a pre-integrated set of industrial-grade control functions, covering a variety of commonly used motion control operators and their operating constraint parameters.

[0053] Hash reflection is a fast data retrieval technique based on hash tables, commonly used in software engineering to improve search efficiency. It generates unique hash keys by calculating hash values ​​for motion control operator names and runtime constraint parameter identifiers, thereby enabling efficient indexing and mapping of operators and parameters in embedded industrial function libraries.

[0054] S1.2 Perform visualization analysis and structured combination operations on the parameter mapping space to obtain a structured operator graph, and perform topology analysis and interface mapping operations to generate a set of control operators and port information.

[0055] Furthermore, after generating the parameter mapping space, the functional parameters and running constraint parameters of each motion control operator in the parameter mapping space are read in the order of dimension index, and the reading results are mapped to the visualization and analysis interface. By expanding the row, column and hierarchical dimensions of the multidimensional structural parameter tensor one by one, a parameter distribution graphic representation that is easy to observe is generated.

[0056] During the visualization and analysis process, the mapping relationship between the functional parameters of each motion control operator and the running constraint parameters is identified and labeled to form a set of graphical units with identification attributes. Then, a structured combination operation is performed based on the set of graphical units to combine the graphical units with related relationships in an orderly manner according to the dependency order between the functional parameters of the motion control operators and the coordination relationship between the running constraint parameters, thereby generating a structured operator graph.

[0057] After the structured operator graph is generated, a topology parsing operation is performed on the structured operator graph to extract the connection relationships and connection directions between motion control operators. Then, an interface mapping operation is performed to bind the input interface and output interface of each motion control operator to the corresponding interface, and finally, a set of control operators and port information are generated.

[0058] S2. By combining configuration instructions, the set of control operators and port information are combined into a control structure topology, and then sent to the reconfigurable hardware structure for connection mapping and scheduling between control operators. Through the rapid loading and combination of heterogeneous control logic, a combined control structure configuration is generated.

[0059] S2.1 Utilize industrial control software to perform structured modeling of the control operator set and port information, graphical connection and scheduling strategy settings, generate combined configuration instructions, and perform syntax and semantic analysis on the combined configuration instructions through a parser to generate a connection scheduling mapping table.

[0060] Furthermore, after generating the set of control operators and port information, the industrial control software is used to perform structured modeling operations on the set of control operators and port information, dividing the motion control operators into hierarchical levels and defining their structures according to their functions and parameter dependencies. Subsequently, in the industrial control software, through a graphical interface, the nodes of each motion control operator are connected according to the predetermined control logic connection rules, clarifying the signal flow and data transmission path between operators.

[0061] The scheduling strategy is set for the connected motion control operator network, including the configuration of scheduling priority, execution timing and resource consumption constraints. After the above operations are completed, the industrial control software automatically generates a combined configuration instruction, which includes the connection information and scheduling parameters between the motion control operators.

[0062] The generated combined configuration instructions are input into the parser. The parser performs syntax analysis on the combined configuration instructions, identifies the instruction format and constituent elements, and performs semantic analysis to confirm the logical consistency of the motion control operator connection relationship and scheduling strategy. Finally, a connection scheduling mapping table is generated, which describes in detail the connection dependency relationship and scheduling execution order of each motion control operator.

[0063] It should also be noted that the predetermined control logic connection rules first determine the input-output relationship and data flow direction between each control operator based on the functional requirements of the control operators and the specific objectives of the motion control task; based on the port information of the control operators, connection rules are defined, including port type matching, signal transmission methods, and priority ordering; combined with the timing dependencies between control operators, connection timing constraints are established to ensure the order of logic execution and data synchronization; the above connection rules are saved in the form of structured configuration files or graphical models for industrial control software to parse and apply, thereby realizing effective connection and collaborative control between control operators.

[0064] S2.2. Parse the control operator dependencies and priority weights in the connection scheduling map, and use weighted topological sorting to generate the optimal execution sequence. Then, perform instruction compilation and resource binding operations to generate the hardware execution image.

[0065] Furthermore, the dependencies and priority weights of the control operators in the connection scheduling map are parsed. By assigning weight values ​​to the dependent operators of each control operator, the execution order of the control operators is sorted using a weighted topology sorting method to generate the optimal execution sequence that satisfies the dependencies and has the highest priority.

[0066] Based on the optimal execution sequence, the control operator execution instructions are compiled into low-level instruction code that can be recognized by the hardware. Then, the compiled instructions are bound to the resources of the reconfigurable hardware structure to determine the specific allocation and mapping relationship of the control operator in the hardware resources, and finally a hardware execution image containing resource allocation, connection relationship and scheduling information is generated.

[0067] S2.3 Extract control operator resource allocation information, physical connection relationships and scheduling information from the hardware execution image, and obtain fine-grained resource topology and scheduling data through parsing and verification operations.

[0068] Furthermore, control operator resource allocation information, physical connection relationships, and scheduling information are read from the hardware execution image. Through parsing operations, the hardware resource identifier, connection port, and scheduling parameters corresponding to each control operator are extracted item by item. Verification operations are performed on the extracted information to confirm the completeness of resource allocation, the validity of connection relationships, and the correctness of scheduling information. The parsed and verified information is then organized and refined to construct fine-grained resource topology and scheduling data, including control operator resource allocation status, physical connection topology, and scheduling execution sequence, ensuring accurate mapping between resource configuration and connection scheduling.

[0069] S2.4 Perform resource conflict detection and scheduling consistency analysis on fine-grained resource topology and scheduling data, output conflict identification results, and perform dynamic remapping and scheduling optimization calculations to obtain resource scheduling schemes.

[0070] Furthermore, for fine-grained resource topology and scheduling data, resource conflict detection is first performed based on resource allocation identifiers and time scheduling information to identify conflicts such as duplicate hardware resource occupation or overlapping time slices. Simultaneously, scheduling consistency analysis is conducted to verify whether there are contradictions or logical errors between the execution timing of each control operator and the scheduling strategy. Based on the conflict identification results, dynamic remapping operations are performed for conflicts such as duplicate resource occupation or overlapping time slices to adjust the resource allocation and scheduling order of control operators. Through scheduling optimization calculations, a new resource scheduling scheme is generated by comprehensively considering resource utilization and execution efficiency to ensure that the resource allocation of control operators in the reconfigurable hardware structure is reasonable and the scheduling is consistent.

[0071] S2.5. Unify and integrate the resource scheduling scheme and encode it in a structured format to generate a combined control structure configuration.

[0072] Furthermore, based on the resource scheduling scheme, all control operator resource allocation information, scheduling execution order, and connection relationships are uniformly integrated, and the resource allocation status and scheduling information are summarized into a complete configuration structure. Subsequently, according to the predetermined structured format encoding rules, the integrated configuration structure is converted into a standardized encoding format, which includes control operator identifiers, port information, resource mapping, and scheduling sequences, and finally generates a combined control structure configuration that can be directly applied to reconfigurable hardware structures.

[0073] It should also be noted that the predetermined structured format encoding rules first determine the fields and data structures required for encoding based on the allocation of control operator resources, physical connection relationships, and scheduling information, such as operator identifiers, port information, connection topology, and scheduling priorities. Second, a unified data format specification is used to hierarchically encode the operator identifiers and port information to ensure data readability and parsing efficiency. Then, based on the actual application requirements of the control structure configuration, the field length, encoding order, and verification mechanism are set to ensure the completeness and accuracy of the encoding results. Finally, the encoding rules are documented for parsing and loading by the control software and hardware execution units, realizing the standardized transmission and application of combined control structure configurations.

[0074] S3. Collect raw sensor data and input it into the combined control structure configuration. According to the preset timing and control operator connection relationship, execute the control logic at each level in the reconfigurable hardware structure and output control signals.

[0075] S3.1. Real-time acquisition of raw sensor data, timestamp marking, generation of multidimensional sensor dataset, adaptive filtering for noise reduction and normalization processing, and generation of multidimensional feature vectors.

[0076] Furthermore, when collecting motion state data from sensors in real time, a precise timestamp is added to each sensor data point to ensure the temporal order and synchronization of the data; the timestamped sensor data are then aggregated to form a multidimensional sensor dataset containing sensing information of different dimensions and types.

[0077] An adaptive filtering operation is performed on the multidimensional sensor dataset, and the filtering parameters are dynamically adjusted according to the statistical characteristics of the data to achieve noise suppression and signal smoothing. Then, the filtered sensor data is normalized to map the data of each dimension to a uniform numerical range, eliminate the difference in dimensions, and finally generate a multidimensional feature vector for use as input to the subsequent motion control operator.

[0078] S3.2 Based on the connection relationship and timing of the control operators in the combined control structure configuration, each control operator is triggered to execute control logic on the multidimensional feature vector to generate an intermediate control instruction set; the intermediate control instruction set is fused based on nonlinear fractional differential operation through a reconfigurable hardware structure to generate a control signal.

[0079] Furthermore, based on the connection relationships and timing of the control operators in the combined control structure configuration, each control operator is triggered sequentially according to a preset execution order. Using multi-dimensional feature vectors as input, the control logic operations of each control operator are executed to generate the corresponding intermediate control instruction set, the expression of which is:

[0080]

[0081] Among them, I jThis represents the j-th intermediate control instruction, where j represents the index of the intermediate control instruction, n represents the dimension of the multidimensional feature vector, and i represents the index of the multidimensional feature vector. W j，i F represents the adjustment factor corresponding to the i-th dimension feature for the j-th control command. i This represents the eigenvalue of the i-th dimension in a multidimensional eigenvector;

[0082] The generated intermediate control instruction set is input into the reconfigurable hardware structure, and the internal computing unit of the hardware performs fusion processing based on nonlinear fractional differential operations. By integrating the output characteristics of multiple control instructions, the control signal used to drive the actuator is finally generated.

[0083] It should also be noted that the preset execution order is set based on the dependencies and timing requirements between control operators. By analyzing the set of control operators and their port information, and combining the requirements of the motion control task, the execution priority and execution order of each control operator are determined; based on the connection relationships and timing requirements between control operators, the start and end times of each control operator are set to ensure that they are executed in the set order.

[0084] S4. Input the control signal to the drive actuator, collect the motion status of the drive actuator in real time, and form actuator feedback data.

[0085] S4.1 The control signal is input to the drive actuator through a high-speed digital interface, the motion state parameters are collected in real time, and the motion state parameters are timestamped using a synchronous clock to generate multi-dimensional motion state data.

[0086] Furthermore, the control signal is sent to the drive actuator through a high-speed digital interface, while the motion state parameters of the drive actuator are collected in real time. The collected motion state parameters are timestamped using a synchronous clock to ensure the accuracy of the time series of the motion state parameters. The timestamped motion state parameters are then organized according to dimensions to form multidimensional motion state data containing various motion features, providing basic information for subsequent feedback analysis.

[0087] S4.2 Perform multivariate fusion and anomaly detection processing on the multidimensional motion state data to generate feedback data, and format it according to the preset protocol to generate actuator feedback data.

[0088] Furthermore, the multidimensional motion state data is fused according to the characteristics of each dimension. The fusion algorithm integrates the relevant information of each motion parameter to improve the integrity and accuracy of the data. At the same time, anomaly detection is performed on the fused data to identify abnormal fluctuations or error signals in the motion state. The anomaly detection results are combined with the fused data to form feedback data. According to the preset protocol format, the feedback data is encoded and formatted to generate actuator feedback data that conforms to the communication specifications, ensuring the standardized transmission and subsequent parsing of the data.

[0089] It should also be noted that the process of setting the preset protocol format is based on the data exchange requirements between control operators and the hardware execution requirements. According to the input / output parameter types and data structures of the control operators, the data transmission format is defined; combined with real-time requirements, the time synchronization mechanism and data packet structure of the protocol are determined; based on the actuator feedback and control command transmission methods, the data format is set, including timestamps, data types, data lengths, and checksums, to ensure the integrity and correctness of the data during transmission.

[0090] S5. Receive and analyze actuator feedback data, compare the control target with the actual response, and output control performance evaluation results.

[0091] S5.1 Receives and parses the feedback data from the actuator, extracts the motion position, velocity, acceleration and torque response features, and obtains a multidimensional response feature sequence through timestamp synchronization and sampling point reconstruction operations.

[0092] Furthermore, after receiving feedback data from the actuator, the data content is parsed to extract motion position, velocity, acceleration, and torque response characteristic parameters. Based on the timestamp information in the feedback data, each sampling point is synchronized to correct sampling timing differences. Through sampling point reconstruction, the dispersed motion response characteristic parameters are arranged in chronological order to form a complete and continuous multidimensional response characteristic sequence, providing a basis for subsequent error calculation and performance evaluation.

[0093] S5.2 Perform multidimensional error alignment calculation between the multidimensional response feature sequence and the preset control target to obtain the multidimensional error time series, and accurately correct the time offset through the nonlinear dynamic time warping algorithm to generate a comprehensive error metric.

[0094] Furthermore, the multidimensional response feature sequence and the preset control target are compared in their corresponding dimensions to calculate errors, generating a multidimensional error time series reflecting the differences. A nonlinear dynamic time warping algorithm is then used to precisely correct the time offset in the multidimensional error time series, adjusting the time alignment of the sequence to eliminate execution timing differences. Finally, a comprehensive error metric is calculated using the corrected time-aligned error sequence, expressed as:

[0095]

[0096] Where E represents the comprehensive error metric, T represents the time series length, t represents the time index, K represents the number of response feature dimensions, k represents the response feature dimension index, and R... k (t) represents the actual response value of the k-th dimension in the multidimensional response feature sequence at time point t, G k (t) represents the target value of the k-th dimension in the preset control target at time point t;

[0097] It comprehensively reflects the magnitude and trend of the deviation between the control target and the actual response, providing a quantitative basis for control performance evaluation.

[0098] It should also be noted that the process of setting preset control targets is based on the specific requirements and performance indicators of the motion control task. Key parameters of the control target are determined, such as motion accuracy, speed, acceleration, and torque response. Then, based on the relationship between the control target and the actual execution environment, corresponding constraints are set, such as maximum permissible error and time constraints. Based on the requirements of the control algorithm, the optimization direction of the control target is defined to ensure that the control target matches the hardware execution capabilities. These settings are combined with the feedback mechanism to form a complete control target, guiding the execution and adjustment of the motion control process.

[0099] S5.3. Preprocess the comprehensive error metric using a nonlinear mapping function to generate a smoothed normalized error map.

[0100] Furthermore, the comprehensive error metric is preprocessed by inputting it into a nonlinear mapping function. The distribution characteristics of the error values ​​are adjusted through nonlinear transformation to suppress spikes and outliers in error fluctuations and improve the smoothness of the error curve. Subsequently, the mapped error is normalized to convert the error values ​​to a uniform numerical range, ensuring that different error magnitudes are comparable. Finally, a smooth and normalized error graph is generated to intuitively reflect the trend and magnitude of error changes.

[0101] S5.4 Combine the smoothed normalized error map with the multidimensional response feature sequence, perform weighted normalized aggregation calculation to generate weighted aggregation results, and perform normalized exponential function mapping to generate control performance evaluation results.

[0102] Furthermore, the smoothed normalized error map is combined with the multidimensional response feature sequence, and weights are assigned according to the importance of each dimension of the response feature. A weighted normalization method is used to aggregate the two to comprehensively reflect the correlation between error and response features. A normalized exponential function is applied to the weighted aggregation result to adjust the dynamic range and distribution characteristics of the result, thereby enhancing the discriminability and stability of the evaluation result. Finally, control performance evaluation results are generated, providing quantitative indicators for the performance analysis of precision motion control.

[0103] This embodiment also provides a computer device applicable to hardware-based configurable precision motion control methods, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the hardware-based configurable precision motion control method proposed in the above embodiments.

[0104] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0105] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the hardware-based configurable precision motion control method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0106] In summary, this invention achieves rapid indexing, accurate mapping, and intuitive configuration of control operators by extracting motion control operators and operational constraint parameters from embedded industrial function libraries, constructing a multi-dimensional parameter mapping space, and performing visual analysis and structured combination. This ensures correct parameter relationships and ease of adjustment. Furthermore, industrial control software is used to perform structured modeling, graphical connection, and scheduling strategy setting for the control operator set and port information. Combined with weighted topology sorting, the optimal execution sequence is generated and distributed to the reconfigurable hardware structure. This achieves overall optimization of resource allocation, connection paths, and execution order, thereby shortening the deployment cycle from algorithm design to hardware operation and improving the system's adaptability, real-time performance, and scalability.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A hardware-based configurable precision motion control method, characterized in that: include, Load motion control operators, perform visual configuration and structured operations on the function parameters of motion control operators, and generate a set of control operators and port information; By combining configuration instructions, the set of control operators and port information are combined into a control structure topology, and then sent to the reconfigurable hardware structure for connection mapping and scheduling between control operators. Through the rapid loading and combination of heterogeneous control logic, a combined control structure configuration is generated. The system collects raw sensor data and inputs it into the combined control structure configuration. According to the preset timing sequence and control operator connection relationship, it executes control logic at all levels in the reconfigurable hardware structure and outputs control signals. The control signal is input to the drive actuator, and the motion status of the drive actuator is collected in real time to form actuator feedback data. Receive and analyze actuator feedback data, compare the control target with the actual response, and output control performance evaluation results.

2. The hardware-based configurable precision motion control method as described in claim 1, characterized in that: The loading of motion control operators involves visually configuring and structurally manipulating the functional parameters of the motion control operators to generate a set of control operators and port information. The specific steps are as follows: Motion control operators and running constraint parameters are extracted from the embedded industrial function library, and the operator resource mapping index is established through fast indexing via hash reflection mechanism. Then, multi-dimensional structural parameter tensor construction operation is performed to generate parameter mapping space. The parameter mapping space is visualized, parsed, and combined in a structured manner to obtain a structured operator graph. Then, topology parsing and interface mapping operations are performed to generate a set of control operators and port information.

3. The hardware-based configurable precision motion control method as described in claim 2, characterized in that: The process involves combining configuration instructions to assemble a control operator set and port information into a control structure topology, which is then sent to the reconfigurable hardware structure for connection mapping and scheduling between control operators. Through the rapid loading and combination of heterogeneous control logic, a combined control structure configuration is generated. The specific steps are as follows: Industrial control software is used to perform structured modeling of the control operator set and port information, graphical connection and scheduling strategy setting, generate combined configuration instructions, and perform syntax and semantic analysis of the combined configuration instructions through a parser to generate a connection scheduling mapping table. The system parses the control operator dependencies and priority weights in the connection scheduling map, uses weighted topological sorting to generate the optimal execution sequence, and performs instruction compilation and resource binding operations to generate a hardware execution image. The resource allocation, connection relationships and scheduling logic of the control operators in the reconfigurable hardware structure are extracted by hardware execution image extraction, and then unified and formatted to generate a combined control structure configuration.

4. The hardware-based configurable precision motion control method as described in claim 3, characterized in that: The process of extracting the resource allocation, connection relationships, and scheduling logic of the control operators in the reconfigurable hardware structure through hardware execution image extraction, and then performing unified integration and formatting operations to generate a combined control structure configuration, includes the following specific steps: The control operator resource allocation information, physical connection relationship and scheduling information are extracted from the hardware execution image, and fine-grained resource topology and scheduling data are obtained through parsing and verification operations. Resource conflict detection and scheduling consistency analysis are performed on fine-grained resource topology and scheduling data. The conflict identification results are output, and dynamic remapping and scheduling optimization calculations are performed to obtain the resource scheduling scheme. The resource scheduling schemes are unified, integrated, and encoded in a structured format to generate a combined control structure configuration.

5. The hardware-based configurable precision motion control method as described in claim 4, characterized in that: The process involves collecting raw sensor data and inputting it into a combined control structure configuration. Following a preset timing sequence and control operator connection relationships, control logic at various levels is executed within the reconfigurable hardware structure, resulting in the output of control signals. The specific steps are as follows: Raw sensor data is acquired in real time, timestamped, and multidimensional sensor datasets are generated. Adaptive filtering, denoising, and normalization are then performed to generate multidimensional feature vectors. Based on the connection relationship and timing of the control operators in the combined control structure configuration, each control operator is triggered to execute control logic on the multidimensional feature vector to generate an intermediate control instruction set; the intermediate control instruction set is fused based on nonlinear fractional differential operations through a reconfigurable hardware structure to generate control signals.

6. The hardware-based configurable precision motion control method as described in claim 5, characterized in that: The steps for inputting control signals to the drive actuator, collecting the drive actuator's motion state in real time, and generating actuator feedback data are as follows. The control signal is input to the drive actuator through a high-speed digital interface, the motion state parameters are collected in real time, and the motion state parameters are timestamped using a synchronous clock to generate multi-dimensional motion state data. Multidimensional motion state data is fused and anomaly detected to generate feedback data, which is then formatted according to a preset protocol to generate actuator feedback data.

7. The hardware-based configurable precision motion control method as described in claim 6, characterized in that: The specific steps for receiving and analyzing actuator feedback data, comparing the control target with the actual response, and outputting control performance evaluation results are as follows. The system receives and parses the feedback data from the actuator, extracts the motion position, velocity, acceleration, and torque response features, and obtains a multidimensional response feature sequence through timestamp synchronization and sampling point reconstruction. Multidimensional error time series is obtained by aligning the multidimensional response feature sequence with the preset control target and obtaining the multidimensional error time series. Then, the time offset is accurately corrected by the nonlinear dynamic time warping algorithm to generate a comprehensive error metric. The comprehensive error metric is subjected to nonlinear mapping calculation and normalization aggregation to generate control performance evaluation results.

8. The hardware-based configurable precision motion control method as described in claim 7, characterized in that: The specific steps for performing nonlinear mapping calculations and normalization aggregation operations on the comprehensive error metric to generate control performance evaluation results are as follows. The comprehensive error metric is preprocessed using a nonlinear mapping function to generate a smooth normalized error map. The smoothed normalized error map is combined with the multidimensional response feature sequence, and a weighted normalized aggregation calculation is performed to generate a weighted aggregation result. Then, a normalized exponential function mapping is performed to generate a control performance evaluation result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the hardware-based configurable precision motion control method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the hardware-based configurable precision motion control method according to any one of claims 1 to 8.

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