Automatic control method and system based on optimal iterative learning control and feedforward control

Abstract

The invention belongs to the technical field of industrial automatic control, and provides an automatic control method and system based on optimal iterative learning control and feedforward control, and the automatic control method comprises the steps: constructing an automatic control model through employing an optimal iterative learning control algorithm and a model-based feedforward control algorithm; the automatic control model is optimized; and implementing automatic control based on the automatic control model. The automatic control method based on the optimal iterative learning control and the feed-forward control is utilized, so that the problems that the performance of the optimal iterative learning control algorithm is reduced during track change and the model-based feed-forward control algorithm is possibly degraded under the constraint of an actuator are solved; the method can realize high-precision tracking and reliable control under repeated and non-repeated tasks, and is suitable for an industrial motion system of high-precision trajectory tracking and executor constraint management.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation control, and in particular to an automation control method and system based on optimal iterative learning control and feedforward control. Background Art

[0002] In modern industrial automation, robotic navigation, aerospace and other fields, the demand for high-precision positioning of unknown targets is increasing. Traditional target positioning methods mainly rely on a single control strategy, such as model-based feedforward control (MFC) or iterative learning control (ILC). However, these methods have many limitations when facing complex environments and unknown targets. MFC requires an accurate system model and its performance will significantly degrade when the trajectory changes. Although iterative learning control can optimize the control input by learning historical trajectory errors, thereby achieving better trajectory tracking performance in repetitive tasks, it has a strong dependence on initial conditions and its performance will significantly deteriorate when the task trajectory changes. In addition, existing systems generally lack effective handling of actuator saturation constraints, which makes it difficult to achieve high-precision positioning in practical applications.

[0003] In recent years, the optimal iterative learning control (OILC) algorithm has attracted attention due to its excellent trajectory tracking performance in repetitive tasks. The optimal iterative learning control algorithm can effectively compensate for tracking errors by continuously optimizing the control input during the iteration process, while taking into account the size and changes of the system control force, thereby avoiding actuator saturation; however, the optimal iterative learning control algorithm still has the problem of performance degradation when processing non-repetitive tasks. At the same time, the model-based feedforward control method provides a new idea for solving the feedforward parameter adjustment problem in non-repetitive tasks. Through online bootstrapping and recursive update algorithms, unbiased parameter estimation can be achieved in a closed-loop setting, thereby achieving good tracking performance in non-repetitive tasks. This provides the possibility of developing a feedforward control algorithm that can adapt to both repetitive and non-repetitive tasks.

[0004] Therefore, an automated control method and system based on optimal iterative learning control and feedforward control is needed to achieve high-precision positioning of unknown targets, while meeting the actuator saturation constraints and trajectory change adaptability in complex environments, providing strong support for achieving high-precision and high-reliability target positioning. Summary of the Invention

[0005] The present invention provides an automated control method and system based on optimal iterative learning control and feedforward control, which solves the problems of performance degradation of the optimal iterative learning control algorithm when the trajectory changes and the possible degradation of the model-based feedforward control algorithm under actuator constraints. It can achieve high-precision tracking and reliable control under repetitive and non-repetitive tasks, and is suitable for industrial motion systems with high-precision trajectory tracking and actuator constraint management.

[0006] The present invention provides an automation control method based on optimal iterative learning control and feedforward control, which is applied to automation control equipment and includes the following steps: Step 1: Build an automated control model using the optimal iterative learning control algorithm and the model-based feedforward control algorithm; including: S201: Constructing a first automated control sub-model based on an optimal iterative learning control algorithm; the first automated control sub-model is configured to implement the following functions: initializing and adjusting the amplitude and rate of change of a control signal; limiting the magnitude and change of a control force; and optimizing the control signal through iterative learning of historical tracking errors extracted from historical data. Limits on the size and variability of control forces, including: A weight matrix is introduced to limit the size and rate of change of the control signal. Specifically, a mathematical representation of the physical limits of the actuator is established to define the control signal amplitude constraint and the control signal change rate constraint. A positive definite weight matrix is introduced into the objective function to construct a quadratic cost function. The quadratic cost function is: in, represents the tracking error, represents the square of the Euclidean norm of the tracking error, represents the control signal vector; A positive definite weight matrix representing the control signal vector; represents the control signal change rate vector; A positive definite weight matrix representing the control signal rate of change vector; represents the quadratic cost function; Represents the total execution time; t represents the specific time in the time interval [0, T]; and is a diagonal matrix used to adjust the size and change rate of the control signal; The control signal vector is constrained between the maximum output force of the actuator and the lower limit of the output force of the actuator, and the control signal change rate vector is constrained between the maximum change rate allowed by the actuator and the lower limit of the change rate of the actuator to prevent the actuator from saturating; S202: Constructing a second automation control sub-model using a model-based feedforward control algorithm; S203: Connect the first automation control sub-model and the second automation control sub-model to construct an automation control model; Step 2: Optimize the automation control model; Step 3: Implement automated control based on the automated control model.

[0007] Furthermore, the adjustment of the amplitude and rate of change of the control signal is achieved through an iterative learning gain matrix, which satisfies the following formula: Where k represents the number of iterations, represents the control signal vector for the k+1th iteration, represents the control signal vector for the kth iteration, represents the tracking error of the kth iteration; Represents the proportional gain matrix, which is used to weight the correction of the tracking error amplitude value; represents the differential gain matrix, which is used to weight the correction of the rate of change of the tracking error; Represents the rate of change of tracking error at the kth iteration.

[0008] Furthermore, the second automation control sub-model is used to implement the following functions: A feedforward controller designed based on a target mathematical model is used to calculate the control signal compensation when the tracked trajectory changes. The target mathematical model is constructed as follows: based on the basic mathematical model, basis functions and parameter vectors are introduced. The basic mathematical model includes one or more combinations of transfer function models, state space models or neural network models. The basis functions are one or more combinations of polynomial basis functions, Fourier basis functions or wavelet basis functions. The parameter vector is updated by the least squares method or the maximum likelihood estimation method. The control parameters of the target mathematical model are dynamically adjusted according to the task requirements and system characteristics.

[0009] Furthermore, the automation control model is optimized, including: A feedback controller is introduced to obtain feedback signals of the automated control model during the training and execution process; the feedback controller is one of a proportional-integral-derivative controller, a robust controller, or a sliding mode controller; An adaptive search algorithm is introduced to dynamically adjust the search strategy according to the feedback signal to search for the optimal control parameters of the feedforward controller, and the optimal control parameters are used as the control parameters in the actual use of the automation control model.

[0010] Furthermore, the adaptive search algorithm is one or more combinations of recursive least squares method, particle swarm optimization algorithm or gradient descent algorithm.

[0011] Furthermore, optimizing the automation control model further includes: implementing optimal control of iterative updates of control signals according to the set event triggering conditions for executing iterative updates, the specific steps of which are: Set the event trigger conditions for iterative updates. First, define the trigger threshold for tracking error changes, and then set the trigger conditions based on the fluctuation of the control signal. The trigger threshold is: in, is the trigger threshold at the kth iteration and time t; k is the number of iterations; is the initial threshold, is the adaptive coefficient, is the maximum output control signal of the actuator; is the control signal at the kth iteration and time t; is the norm of the control signal at the kth iteration and time t; is the norm of the maximum output control signal of the actuator; The trigger condition is: when or When , iterative update is performed; among them, is the weight coefficient of control signal change; is the tracking error at the kth iteration and time t; is the tracking error at the k-1th iteration and time t; represent and The norm of the difference; is the control signal at the kth iteration and time t; is the control signal at the k-1th iteration and time t; represent and The norm of the difference; is the trigger threshold at the kth iteration and time t; Iterative updates are performed based on event trigger conditions. Specifically, if the iteration time is within the non-triggering time interval, the control signal is obtained based on the previous iteration result. If the iteration time is within the triggering time interval, the control signal is incrementally updated according to the set increment amplitude. A multi-objective cost function is constructed. Within the constraints of the control signal vector and the control signal change rate vector, the multi-objective cost function is evaluated for minimum cost. The optimal target of the iterative update of the control signal is obtained based on the evaluation result. The iterative update is stopped when the optimal target is reached. The multi-objective cost function is expressed as: in, represents the multi-objective cost function, is the weight coefficient of tracking error, is the square of the norm of the tracking error; Express Integral from 0 to T; is the total execution time; is the weight coefficient of the control signal, is the square of the norm of the control signal; Express From 0 to 's points; is the weight coefficient of the trigger frequency; is the trigger moment indicator function. At the trigger moment, is 1, at the non-triggering moment, is 0; Represents the total number of trigger events; Represents the index value of the iterative trigger event, The value range is 1,2,..., ; Indicates the The time when the trigger event occurs.

[0012] An automated control system based on optimal iterative learning control and feedforward control is applied to automated control equipment and is used to implement automated control using an automated control method based on optimal iterative learning control and feedforward control, including: An automated control model building module, used to build an automated control model using an optimal iterative learning control algorithm and a model-based feedforward control algorithm; Automation control model optimization module, used to optimize the automation control model; The automation control implementation module is used to implement automation control based on the automation control model.

[0013] Compared with the existing technology, the present invention has the following advantages and beneficial effects: by combining the advantages of optimal iterative learning control and model-based feedforward control, the tracking error is significantly reduced, the control accuracy and response speed are improved, and stable control performance can be maintained in the case of external interference or system parameter changes; the change process of the control signal is optimized, and the saturation phenomenon of the actuator due to excessive signal fluctuations is avoided, thereby extending the service life of the equipment; the event trigger mechanism is introduced to reduce the frequency of control signal updates, reduce the consumption of computing resources, and at the same time ensure the dynamic performance of the system; multi-objective optimization is achieved, and a balance is achieved between tracking error, control signal amplitude and trigger frequency by constructing a comprehensive cost function, thereby improving the overall control effect; it has strong adaptability, can dynamically adjust control parameters according to specific task requirements and system characteristics, and is suitable for automated control tasks in a variety of complex scenarios.

[0014] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 Schematic diagram of the steps of the automated control method based on optimal iterative learning control and feedforward control; Figure 2 A schematic diagram of the steps of a method for optimizing an automation control model; Figure 3 Schematic diagram of the structure of the automatic control system based on optimal iterative learning control and feedforward control. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0018] The present invention provides an automatic control method based on optimal iterative learning control and feedforward control, such as Figure 1 As shown, it is applied to automatic control equipment and includes the following steps: Step 1: Build an automated control model using the optimal iterative learning control algorithm and the model-based feedforward control algorithm; including: S201: Constructing a first automated control sub-model based on an optimal iterative learning control algorithm; the first automated control sub-model is configured to implement the following functions: initializing and adjusting the amplitude and rate of change of a control signal; limiting the magnitude and change of a control force; and optimizing the control signal through iterative learning of historical tracking errors extracted from historical data. Limits on the size and variability of control forces, including: A weight matrix is introduced to limit the size and rate of change of the control signal. Specifically, a mathematical representation of the physical limits of the actuator is established to define the control signal amplitude constraint and the control signal change rate constraint. A positive definite weight matrix is introduced into the objective function to construct a quadratic cost function. The quadratic cost function is: in, represents the tracking error, represents the square of the Euclidean norm of the tracking error, represents the control signal vector; A positive definite weight matrix representing the control signal vector; represents the control signal change rate vector; A positive definite weight matrix representing the control signal rate of change vector; represents the quadratic cost function; Represents the total execution time; t represents the specific time in the time interval [0, T]; and is a diagonal matrix used to adjust the size and change rate of the control signal; The control signal vector is constrained between the maximum output force of the actuator and the lower limit of the output force of the actuator, and the control signal change rate vector is constrained between the maximum change rate allowed by the actuator and the lower limit of the change rate of the actuator to prevent the actuator from saturating; S202: Constructing a second automation control sub-model using a model-based feedforward control algorithm; S203: Connect the first automation control sub-model and the second automation control sub-model to construct an automation control model; Step 2: Optimize the automation control model; Step 3: Implement automated control based on the automated control model.

[0019] The working principle of the above technical solution is: in order to realize the automation control method based on optimal iterative learning control and feedforward control, which is applied to automation control equipment, the present invention proposes to first use the optimal iterative learning control algorithm and the model-based feedforward control algorithm to construct an automation control model; the optimal iterative learning control algorithm uses historical operation data to improve the control algorithm of the next iteration. The historical operation data can be an error or a control input. For example, for high-precision positioning of robotic arms and conveyor belts in industrial automation, the historical operation data includes the expected trajectory of the previous iteration, specifically the spatial path coordinate sequence that the end effector of the robotic arm needs to follow accurately, and the precise position sequence that a certain point on the conveyor belt needs to reach at a specific time; the actual output of the previous iteration, specifically the actual position coordinates of the end effector of the robotic arm at each sampling moment in k operations, and the conveyor belt marking point at k operations. The actual position at each sampling moment in the ; k represents the number of iterative runs; the control input of the previous iteration, specifically the torque instruction sequence for driving the robot arm joint motor, the speed or position instruction sequence for driving the conveyor belt motor; the tracking error of the previous iteration, specifically the position deviation vector of the robot arm end at each moment in the k runs, the position deviation of the conveyor belt mark point at each moment in the k runs and the iteration number identifier; k represents the number of iterative runs; this algorithm continuously adjusts and optimizes the control signal through historical data and iterative learning, so that it gradually approaches the ideal output; the model-based feedforward control algorithm uses a mathematical model to calculate the impact of the disturbance on the output, and completes compensation before the disturbance actually affects the output to directly offset the impact; the automated control model constructed by combining the two algorithms can not only use mathematical models for accurate predictions, but also dynamically adapt to complex environmental changes through iterative learning; In order to realize the construction of the automatic control model, the first automatic control sub-model is first constructed based on the optimal iterative learning control algorithm. The optimal iterative learning control algorithm gradually optimizes the generation process of the control signal by continuously accumulating historical operation data, thereby improving the dynamic response capability of the system; then the second automatic control sub-model is constructed using the model-based feedforward control algorithm, and the model-based feedforward control algorithm uses precise mathematical modeling to predict possible disturbances in advance and implement compensation; finally, the first automatic control sub-model is connected with the second automatic control sub-model. The connection is based on the use of the first automatic control sub-model to perform control, and then the first automatic control sub-model is used to perform control. The first automatic control sub-model is to control the reduction of the trajectory error, and the second automatic control sub-model is to respond in a very short time when the trajectory changes, so as to achieve accurate and rapid tracking of the trajectory. Then the automatic control model is generated. The constructed automatic control model can fully utilize the advantages of the two algorithms and lay a solid foundation for subsequent optimization and implementation. During the initialization phase, the first automated control sub-model initializes and adjusts the amplitude and rate of change of the control signal. Simultaneously, during operation, through in-depth analysis of historical data, it extracts key tracking error information and uses this information for iterative optimization, thereby gradually reducing deviations. By limiting the magnitude and rate of change of the control force, the stability and security of the system are further enhanced, avoiding unnecessary fluctuations caused by sudden changes or over-adjustments. To limit the magnitude and variation of the control force and prevent actuator saturation, the present invention first introduces a weight matrix and constructs a quadratic cost function. Through the regulation of the diagonal matrices Q and R, the relationship between the magnitude and rate of change of the control signal can be flexibly balanced to meet the needs of different application scenarios. At the same time, the control signal is constrained within the physical limits of the actuator. By embedding the constraints into the optimization process, the adaptability and robustness of the algorithm are further enhanced, enabling it to maintain high performance in complex dynamic environments. Then, the automation control model is optimized. The optimization includes: introducing a feedback controller and an adaptive search algorithm, using the obtained optimal control parameters as the control parameters in the actual use of the automation control model; and implementing optimal control of iterative updates of control signals based on set event trigger conditions for executing iterative updates; Finally, automated control is implemented based on the automated control model. For example, when the robotic arm in industrial automation repeats the welding path, the joint friction will cause the trajectory error to be greater than 0.5mm. To solve this problem, the optimal iterative learning control algorithm in the automated control model is first used to correct the control signal through the first three iterative learning to reduce the tracking error to less than 0.1mm; when the welding path changes, the model-based feedforward control algorithm in the automated control model is used to calculate the feedforward signal in real time based on the inverse model of the robotic arm dynamics, quickly reducing the trajectory switching time, thereby achieving precise automated control.

[0020] The beneficial effects of the above technical solution are as follows: adopting the solution provided by this embodiment, by constructing an automated control model using the optimal iterative learning control algorithm and the model-based feedforward control algorithm, and applying it to automated control in automated control equipment, it can reduce control errors, improve control accuracy, stability, and adaptability, and provide reliable technical support for actual high-precision control applications; by combining the optimal iterative learning control algorithm and the model-based feedforward control algorithm to construct an automated control model, it helps to improve the performance of the automated control model and provide reliable technical support for high-precision control scenarios; the first automated control sub-model can significantly improve the control accuracy and response speed of the system, while reducing the risk of instability caused by external interference or internal fluctuations; by limiting the size and change of the control force, it can effectively avoid the performance degradation of the actuator caused by overload or excessive rate of change, thereby extending the service life of the equipment and improving system stability; by introducing the diagonal matrix adjustment mechanism, not only the flexibility of the control signal is improved, but also precise control can be achieved in a variety of application scenarios; the design of embedding constraints into the optimization process further enhances the practicality of the algorithm.

[0021] In one embodiment, the adjustment of the amplitude and the rate of change of the control signal is achieved by an iterative learning gain matrix, which satisfies the following formula: Where k represents the number of iterations, represents the control signal vector for the k+1th iteration, represents the control signal vector for the kth iteration, represents the tracking error of the kth iteration; Represents the proportional gain matrix, which is used to weight the correction of the tracking error amplitude value; represents the differential gain matrix, which is used to weight the correction of the rate of change of the tracking error; Represents the rate of change of tracking error at the kth iteration.

[0022] The working principle of the above technical solution is as follows: the amplitude of the control signal refers to the numerical size of the control signal, such as the amplitude of the motor driving force, which directly affects the dynamic range of the system output; the rate of change of the control signal refers to the speed of change of the control signal over time, such as the rate of increase and decrease of the driving force, which affects the smoothness of the system response and the mechanical stress; the adjustment of the amplitude and rate of change of the control signal is achieved in order to make the error between the system output trajectory and the expected trajectory converge to zero within a limited time, such as a single operation cycle of the robot arm, that is, to achieve complete tracking; the present invention can gradually optimize the control signal according to the error information in the historical data by introducing the iterative learning gain matrix, and During the first iteration, the proportional gain matrix and the differential gain matrix respectively perform weighted corrections on the amplitude and rate of change of the tracking error, thereby realizing dynamic adjustment of the control signal. For example, in the application scenario of precise positioning of a semiconductor wafer stage, the implementation process is as follows: During the first iteration, the amplitude of the control signal is set according to the empirical value, and the rate of change of the control signal is limited according to the maximum acceleration of the motor. In the subsequent iterations, the driving force amplitude is increased through the proportional gain matrix to reduce the positioning deviation, and the rate of change in the acceleration stage is reduced through the differential gain matrix to suppress mechanical vibration. After 3 iterations, the nanometer-level positioning error can converge from ±50nm to ±10nm.

[0023] The beneficial effects of the above technical solution are: the solution provided by this embodiment not only improves the system's adaptability, but also enhances its robustness in complex environments, and can quickly converge to the target value within a limited time interval, while avoiding instability caused by excessive or insufficient control force.

[0024] In one embodiment, the second automation control sub-model is used to implement the following functions: A feedforward controller designed based on a target mathematical model is used to calculate the control signal compensation when the tracked trajectory changes. The target mathematical model is constructed as follows: based on the basic mathematical model, basis functions and parameter vectors are introduced. The basic mathematical model includes one or more combinations of transfer function models, state space models or neural network models. The basis functions are one or more combinations of polynomial basis functions, Fourier basis functions or wavelet basis functions. The parameter vector is updated by the least squares method or the maximum likelihood estimation method. The control parameters of the target mathematical model are dynamically adjusted according to the task requirements and system characteristics.

[0025] The working principle of the above technical solution is: in order to achieve precise control of complex systems, this embodiment combines the design of a feedforward controller with the target mathematical model to effectively cope with the challenges brought by trajectory changes; in the specific implementation process, the selection of basis functions and the update mechanism of parameter vectors work together to ensure the model's adaptability to dynamic environments; by introducing polynomial basis functions, Fourier basis functions or wavelet basis functions, the nonlinear relationship between system input and output can be flexibly captured; at the same time, the parameter vector is iteratively optimized using the least squares method or maximum likelihood estimation method, further improving the prediction accuracy and robustness of the model; this design can not only dynamically adjust control parameters according to task requirements, but also maintain stable performance in a variety of application scenarios.

[0026] The beneficial effects of the above technical solution are: by adopting the solution provided in this embodiment, the response speed and accuracy of the control system can be significantly improved. By dynamically adjusting the control parameters, the system can maintain efficient operation under different working conditions, thereby reducing the need for manual intervention.

[0027] In one embodiment, the automation control model is optimized, such as Figure 2 Shown, including: A feedback controller is introduced to obtain feedback signals of the automated control model during the training and execution process; the feedback controller is one of a proportional-integral-derivative controller, a robust controller, or a sliding mode controller; An adaptive search algorithm is introduced to dynamically adjust the search strategy according to the feedback signal to search for the optimal control parameters of the feedforward controller, and the optimal control parameters are used as the control parameters in the actual use of the automation control model.

[0028] The working principle of the above technical solution is: first, the system operation data is collected in real time through the feedback controller, and this data is compared with the target value to generate a feedback signal; the adaptive search algorithm analyzes the performance of the current control parameters based on the feedback signal, and dynamically adjusts the search direction and step size to approach the optimal solution more quickly. This process ensures that the feedforward controller can always maintain optimal performance under different working conditions by continuously evaluating the impact of the control parameters on the system output; at the same time, the adaptive search algorithm has strong robustness and can effectively cope with the challenges brought by the system's nonlinearity, time-varying characteristics and external interference, thereby further improving the stability and accuracy of the overall control system.

[0029] The beneficial effect of the above technical solution is: by adopting the solution provided by this embodiment, through the synergistic effect of the feedback controller and the adaptive search algorithm, the system can achieve rapid response and precise control under complex and changeable working conditions.

[0030] In one embodiment, the adaptive search algorithm is a combination of one or more of a recursive least squares method, a particle swarm optimization algorithm, or a gradient descent algorithm.

[0031] The working principle of the above technical solution is as follows: the recursive least squares method continuously updates the parameter estimates and uses historical data and current feedback signals for iterative optimization to quickly approach the optimal solution; the particle swarm optimization algorithm simulates group behavior and searches for the optimal control parameter combination in the solution space through collaboration and information sharing between individuals, and has the characteristic of strong global search capability; the gradient descent algorithm is based on the gradient information of the objective function and gradually adjusts the parameters in the descent direction to achieve the local optimal solution; when these algorithms are used alone or in combination, they can give full play to their respective advantages, adapt to the needs under different working conditions, and further improve the performance of the control system.

[0032] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, through the flexible combination of recursive least squares method, particle swarm optimization algorithm and gradient descent algorithm, the system can dynamically switch or fuse algorithm strategies according to different working conditions, thereby maintaining efficient operation in complex environments.

[0033] In one embodiment, optimizing the automation control model further includes: implementing optimal control of iterative updates of control signals according to set event triggering conditions for executing iterative updates, specifically the following steps: Set the event trigger conditions for iterative updates. First, define the trigger threshold for tracking error changes, and then set the trigger conditions based on the fluctuation of the control signal. The trigger threshold is: in, is the trigger threshold at the kth iteration and time t; k is the number of iterations; is the initial threshold, is the adaptive coefficient, is the maximum output control signal of the actuator; is the control signal at the kth iteration and time t; is the norm of the control signal at the kth iteration and time t; is the norm of the maximum output control signal of the actuator; The trigger condition is: when or When , iterative update is performed; among them, is the weight coefficient of control signal change; is the tracking error at the kth iteration and time t; is the tracking error at the k-1th iteration and time t; represent and The norm of the difference; is the control signal at the kth iteration and time t; is the control signal at the k-1th iteration and time t; represent and The norm of the difference; is the trigger threshold at the kth iteration and time t; Iterative updates are performed based on event trigger conditions. Specifically, if the iteration time is within the non-triggering time interval, the control signal is obtained based on the previous iteration result. If the iteration time is within the triggering time interval, the control signal is incrementally updated according to the set increment amplitude. A multi-objective cost function is constructed. Within the constraints of the control signal vector and the control signal change rate vector, the multi-objective cost function is evaluated for minimum cost. The optimal target of the iterative update of the control signal is obtained based on the evaluation result. The iterative update is stopped when the optimal target is reached. The multi-objective cost function is expressed as: in, represents the multi-objective cost function, is the weight coefficient of tracking error, is the square of the norm of the tracking error; Express Integral from 0 to T; is the total execution time; is the weight coefficient of the control signal, is the square of the norm of the control signal; Express From 0 to 's points; is the weight coefficient of the trigger frequency; is the trigger moment indicator function. At the trigger moment, is 1, at the non-triggering moment, is 0; Represents the total number of trigger events; Represents the index value of the iterative trigger event, The value range is 1,2,..., ; Indicates the The time when the trigger event occurs.

[0034] The working principle of the above technical solution is as follows: the traditional time-driven control method often updates the control signal at each fixed time interval. This method may lead to unnecessary iterative operations when the system state changes relatively smoothly, thereby wasting computing resources; the event trigger mechanism adopted by the present invention can avoid this problem, and the control signal will be iteratively updated only when specific trigger conditions are met, greatly reducing the number of unnecessary iterations; the use of adaptive thresholds is to dynamically adjust the trigger conditions according to the actual operating status. In actual applications, the operating status of the system will be affected by many factors, such as changes in the external environment, aging of internal components of the system, etc. The adaptive threshold can be adjusted in real time according to these changes, thereby ensuring that the system can operate stably under various complex situations, which can improve the robustness and adaptability of the system; In the specific implementation process, the incremental update strategy of the control signal updates the control signal according to the set incremental amplitude at the triggering moment, making full use of the advantages of feedforward control, so that the desired control effect can be achieved more quickly; at the same time, the three optimization objectives of tracking error, control signal energy and trigger frequency in the multi-objective cost function are used to measure the update target of the control signal; in actual control implementation, different performance indicators often restrict each other; for example, in order to reduce the tracking error, it may be necessary to increase the energy of the control signal; and too many control signal updates will lead to an increase in the triggering frequency, thereby increasing the computational burden of the system; the multi-objective cost function achieves balanced optimization of different performance indicators by setting weight coefficients. By adjusting the weight coefficient of the tracking error, the weight coefficient of the control signal, and the weight coefficient of the triggering frequency, the control signal update target can be flexibly adjusted according to specific application requirements.

[0035] The beneficial effects of the above technical solution are: by adopting the solution provided in this embodiment, by setting event trigger conditions and multi-objective cost functions, it is possible to significantly reduce the consumption of computing resources while effectively ensuring control accuracy; through flexible configuration of trigger conditions and cost function parameters, customized design can be carried out for different application scenarios.

[0036] Automatic control systems based on optimal iterative learning control and feedforward control, such as Figure 3 As shown, it is applied to automatic control equipment and is used to implement automatic control using an automatic control method based on optimal iterative learning control and feedforward control, including: An automated control model building module, used to build an automated control model using an optimal iterative learning control algorithm and a model-based feedforward control algorithm; Automation control model optimization module, used to optimize the automation control model; An automation control implementation module, used for implementing automation control based on the automation control model; Automation control systems based on optimal iterative learning control and feedforward control. Applications of automation control equipment include, but are not limited to, high-precision positioning of robotic arms and conveyor belts in industrial automation, path tracking in robot navigation, and aircraft attitude control in the aerospace field. The specific implementation is: Applying automated control in the semiconductor wafer handling robot system: In repetitive handling tasks performed by the automated control equipment of a semiconductor wafer handling robot, the optimal iterative learning control algorithm in the automated control model is first used to correct the control signal through the first three iterative learning cycles, reducing the tracking error from an initial 0.1mm to 0.02mm. When the wafer placement position changes, the model-based feedforward control algorithm in the automated control model is used to calculate the feedforward signal in real time based on the robot's inverse dynamics model, shortening the trajectory switching time from 1.2s to 0.6s. A weight matrix is set to limit the motor drive force amplitude to ≤50N and the rate of change to ≤10N / s to avoid motor overload alarms. Finally, the feedforward parameters are optimized using an online recursive least squares method to improve positioning accuracy. Applying automated control in UAV path planning: First, in the drone's repetitive tracking tasks, the optimal iterative learning control algorithm in the automated control model is used to reduce the track tracking error to less than 0.5m. When encountering sudden obstacles, the model-based feedforward control algorithm in the automated control model is used to quickly generate obstacle avoidance feedforward signals based on the drone's dynamics model, which can reduce the response time to less than 200ms. At the same time, setting the weight matrix to constrain the motor output power within the rated range can improve the continuous operation stability of the drone's path planning system by 75%.

[0037] The working principle of the above technical solution is as follows: in order to realize an automated control system based on optimal iterative learning control and feedforward control, the present invention proposes an automated control model construction module, which combines the optimal iterative learning control algorithm and the model-based feedforward control algorithm to model the automated control model; Then, the automation control model optimization module optimizes the automation control model; Finally, the automation control implementation module implements automation control according to the optimized control model.

[0038] The beneficial effects of the above technical solution are: the solution provided by this embodiment can effectively improve the accuracy and efficiency of automatic control, is applicable to different types of automatic control equipment, and provides a more flexible and reliable solution for the field of industrial automation.

[0039] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An automated control method based on optimal iterative learning control and feedforward control, characterized in that: Applied to automated control equipment, the method comprises the following steps: Step 1: Build an automated control model using the optimal iterative learning control algorithm and the model-based feedforward control algorithm; including: S201: Constructing a first automated control sub-model based on an optimal iterative learning control algorithm; the first automated control sub-model is configured to implement the following functions: initializing and adjusting the amplitude and rate of change of a control signal; limiting the magnitude and change of a control force; and optimizing the control signal through iterative learning of historical tracking errors extracted from historical data. Limits on the size and variability of control forces, including: A weight matrix is introduced to limit the size and rate of change of the control signal. Specifically, a mathematical representation of the physical limits of the actuator is established to define the control signal amplitude constraint and the control signal change rate constraint. A positive definite weight matrix is introduced into the objective function to construct a quadratic cost function. The quadratic cost function is: in, represents the tracking error, represents the square of the Euclidean norm of the tracking error, represents the control signal vector; A positive definite weight matrix representing the control signal vector; represents the control signal change rate vector; A positive definite weight matrix representing the control signal rate of change vector; represents the quadratic cost function; Represents the total execution time; t represents the specific time in the time interval [0, T]; and is a diagonal matrix used to adjust the size and change rate of the control signal; The control signal vector is constrained between the maximum output force of the actuator and the lower limit of the output force of the actuator, and the control signal change rate vector is constrained between the maximum change rate allowed by the actuator and the lower limit of the change rate of the actuator to prevent the actuator from saturating; S202: Constructing a second automation control sub-model using a model-based feedforward control algorithm; S203: Connect the first automation control sub-model and the second automation control sub-model to construct an automation control model; Step 2: Optimize the automation control model; Step 3: Implement automated control based on the automated control model.

2. The automated control method based on optimal iterative learning control and feedforward control according to claim 1, characterized in that: The adjustment of the amplitude and rate of change of the control signal is achieved through the iterative learning gain matrix, which satisfies the following formula: Where k represents the number of iterations, represents the control signal vector for the k+1th iteration, represents the control signal vector for the kth iteration, represents the tracking error of the kth iteration; Represents the proportional gain matrix, which is used to weight the correction of the tracking error amplitude value; represents the differential gain matrix, which is used to weight the correction of the rate of change of the tracking error; Represents the rate of change of tracking error at the kth iteration.

3. The automated control method based on optimal iterative learning control and feedforward control according to claim 1, characterized in that: The second automation control sub-model is used to implement the following functions: A feedforward controller designed based on a target mathematical model is used to calculate the control signal compensation when the tracked trajectory changes. The target mathematical model is constructed as follows: based on the basic mathematical model, basis functions and parameter vectors are introduced. The basic mathematical model includes one or more combinations of transfer function models, state space models or neural network models. The basis functions are one or more combinations of polynomial basis functions, Fourier basis functions or wavelet basis functions. The parameter vector is updated by the least squares method or the maximum likelihood estimation method. The control parameters of the target mathematical model are dynamically adjusted according to the task requirements and system characteristics.

4. The automated control method based on optimal iterative learning control and feedforward control according to claim 3, characterized in that: Optimize the automation control model, including: A feedback controller is introduced to obtain feedback signals of the automated control model during the training and execution process; the feedback controller is one of a proportional-integral-derivative controller, a robust controller, or a sliding mode controller; An adaptive search algorithm is introduced to dynamically adjust the search strategy according to the feedback signal to search for the optimal control parameters of the feedforward controller, and the optimal control parameters are used as the control parameters in the actual use of the automation control model.

5. The automated control method based on optimal iterative learning control and feedforward control according to claim 4, characterized in that: The adaptive search algorithm is one or more combinations of recursive least squares method, particle swarm optimization algorithm or gradient descent algorithm.

6. The automated control method based on optimal iterative learning control and feedforward control according to claim 1, characterized in that: Optimizing the automation control model also includes: implementing optimal control of the iterative update of the control signal according to the set event triggering conditions for executing the iterative update. The specific steps are as follows: Set the event trigger conditions for iterative updates. First, define the trigger threshold for tracking error changes, and then set the trigger conditions based on the fluctuation of the control signal. The trigger threshold is: in, is the trigger threshold at the kth iteration and time t; k is the number of iterations; is the initial threshold, is the adaptive coefficient, is the maximum output control signal of the actuator; is the control signal at the kth iteration and time t; is the norm of the control signal at the kth iteration and time t; is the norm of the maximum output control signal of the actuator; The trigger condition is: when or When , iterative update is performed; among them, is the weight coefficient of control signal change; is the tracking error at the kth iteration and time t; is the tracking error at the k-1th iteration and time t; represent and The norm of the difference; is the control signal at the kth iteration and time t; is the control signal at the k-1th iteration and time t; represent and The norm of the difference; is the trigger threshold at the kth iteration and time t; Iterative updates are performed based on event trigger conditions. Specifically, if the iteration time is within the non-triggering time interval, the control signal is obtained based on the previous iteration result. If the iteration time is within the triggering time interval, the control signal is incrementally updated according to the set increment amplitude. A multi-objective cost function is constructed. Within the constraints of the control signal vector and the control signal change rate vector, the multi-objective cost function is evaluated for minimum cost. The optimal target of the iterative update of the control signal is obtained based on the evaluation result. The iterative update is stopped when the optimal target is reached. The multi-objective cost function is expressed as: in, represents the multi-objective cost function, is the weight coefficient of tracking error, is the square of the norm of the tracking error; Express Integral from 0 to T; is the total execution time; is the weight coefficient of the control signal, is the square of the norm of the control signal; Express From 0 to 's points; is the weight coefficient of the trigger frequency; is the trigger moment indicator function. At the trigger moment, is 1, at the non-triggering moment, is 0; Represents the total number of trigger events; Represents the index value of the iterative trigger event, The value range is 1,2,..., ; Indicates the The time when the trigger event occurs.

7. An automated control system based on optimal iterative learning control and feedforward control, characterized in that: Applied to an automation control device, for implementing automation control using the automation control method based on optimal iterative learning control and feedforward control as claimed in any one of claims 1 to 6, comprising: An automated control model building module, used to build an automated control model using an optimal iterative learning control algorithm and a model-based feedforward control algorithm; Automation control model optimization module, used to optimize the automation control model; The automation control implementation module is used to implement automation control based on the automation control model.

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