Stage mechanical equipment synchronous control method for dynamically optimizing state parameters

Abstract

The invention discloses a stage mechanical equipment synchronous control method with dynamically optimized state parameters, which comprises the following steps of: based on obtained motion state parameters of all equipment in synchronous linkage, carrying out improved state space mathematical modeling on all the equipment in synchronous motion to obtain a coupled mathematical state model; time-varying parameters in the coupling mathematical state model are updated online, and the state space of the equipment is predicted; meanwhile, a global synchronization index is defined based on the position synchronization error and the speed synchronization error of the equipment so as to construct a target function; defining a cost function of the whole state space of the equipment based on the predicted state space and a target function; and acquiring a control strategy of the equipment based on the cost function of the whole state space of the equipment, and adaptively and dynamically adjusting control parameters of the equipment by using the control strategy. The method has the advantages that the problem of asynchronous equipment linkage caused by position and speed deviation is reduced, the synchronous control precision among multiple pieces of equipment is improved, and the fluency and comfort of performance are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of stage equipment motion control optimization, and in particular to a stage mechanical equipment synchronization control method with dynamic optimization of state parameters. Background Art

[0002] With the rapid development of the economy and the iterative upgrade of residents' cultural consumption needs, the performance market is ushering in an unprecedented period of high-quality development. The overall scale of the national performance market has increased compared to before the epidemic. Among them, emerging formats such as immersive performances and real-life landscape performances have experienced explosive growth, giving rise to new forms of performing arts spaces that are completely different from traditional theaters, and building a new industrial development pattern driven by "traditional theater performances + new space performances". Looking at the existing performance consumer groups, their entertainment spending is also gradually increasing, and the attention they pay to live music festivals, shows, etc. has increased significantly, showing a trend of upgrading the experience from superficial viewing to in-depth participation. In the field of technological innovation, the heavy application of digital technologies such as three-dimensional simulation and twin virtual production has enabled cultural and tourism integration projects to achieve more amazing artistic effects.

[0003] As we all know, perfect coordination between stage machinery and performers is essential for creating exceptional artistic effects. The precise coordination of multiple stage machinery devices is a key element in elevating these effects. Precise dynamic coordination of stage equipment not only enhances performance creativity and artistic appeal, but also, through the interaction between devices, creates an overall aesthetic and heightens the visual impact of the stage.

[0004] Currently, the synchronous linkage of numerous devices has become a very common form of performance in exciting stage performances. Whether it is a large-scale performance or other types of artistic presentation, it is inseparable from the synchronous collaboration between devices. However, the precise and accurate realization of the synchronous movement of multiple devices is often subject to a number of factors. First, different devices have their own independent communication and properties, making the linkage between devices more difficult; second, the stage space, on-site environment and load conditions will also affect the accuracy and stability of the synchronous movement of multiple devices. In particular, for the formation of devices formed at any time during the performance, synchronous linkage must be achieved to coordinate with the lighting or the combination of virtual and real artistic effects. Slight synchronization errors or received communication timing will cause the actual movement position to deviate from the target position, reducing the synchronization control effect and damaging the overall viewing experience. Even if devices of the same type are configured with the same motion parameters and use the same control process, they generally experience good synchronization and coordination during the initial period of linkage. However, common factors such as sudden load changes, network congestion, slow response, or control system disturbances can cause lags or misalignments in the linkage of multiple devices during the subsequent period. For example, devices that should have reached their target positions simultaneously may experience linkage errors, while devices that should have initiated opposite directions simultaneously may no longer have the markers to initiate simultaneously. Due to the sensitivity of performances to position synchronization, these situations can seriously impact the overall artistic expression of the performance. Summary of the Invention

[0005] The purpose of the present invention is to provide a synchronous control method for stage mechanical equipment with dynamic optimization of state parameters, thereby solving the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A synchronous control method for stage mechanical equipment with dynamic optimization of state parameters comprises the following steps:

[0008] S1. Obtain the motion state parameters of all synchronized devices;

[0009] S2. performing improved state space mathematical modeling for all devices in synchronous motion based on motion state parameters to obtain a coupled mathematical state model;

[0010] S3. Update the time-varying parameters in the coupled mathematical state model online and predict the state space of the device. At the same time, define a global synchronization index based on the position synchronization error and velocity synchronization error of the device to construct the objective function.

[0011] S4. Based on the predicted device state space and the objective function, a cost function of the entire device state space is defined; a control strategy of the device is obtained according to the cost function, and the control parameters of the device are adaptively and dynamically adjusted using the control strategy.

[0012] Preferably, in step S1, the motion parameters of each device include a description of the sum of position and speed information;

[0013]

[0014] Among them, MPa Dev is the set of motion parameters of n synchronous linkage devices; is a subset of location data; is the speed data subset; is the position parameter of the i-th device; is the speed parameter of the i-th device; i≤n∈N + , N + is a set of positive integers.

[0015] Preferably, step S2 specifically includes introducing coupling relationships between devices and adaptive parameters according to the characteristics of each device to adapt to the dynamic changes of the devices, so as to construct a coupled mathematical state model; the coupled mathematical state model is expressed as follows:

[0016]

[0017] in, is x i The first derivative with respect to time; x i and x j are the state vectors of the i-th and j-th devices respectively; A i (θ k ) is the state transfer matrix of the i-th device; B i (θ k ) is the time-varying control input matrix of the i-th device; K s,i is the equivalent stiffness of the i-th device; K t,i is the torque of the i-th device; k is the discrete time step; θ k is the time-varying parameter at time k, and the influencing factor of this parameter is J is the moment of inertia of the equipment; K s is the equivalent stiffness of the equipment; K t is the torque of the equipment; τ com is the communication delay; u i is the control state input of the i-th device; H ij (x j -x i ) is the coupling term that coordinates the relative motion between the i-th and j-th devices; H ijis the coupling matrix between the i-th and j-th devices; h pos is the position coupling coefficient; h vel is the velocity coupling coefficient.

[0018] Preferably, step S3 specifically includes the following contents:

[0019] S31, according to the dynamic changes of the control system, the time-varying parameters θ of the model k Perform online updates;

[0020]

[0021] in, is the time-varying parameter of the updated model; θ (i′) is the parameter vector of the i′th application scenario; is the probability that the control system at time k is in the i′th application scenario; is the probability that the control system is in the i′th application scenario at time k-1; M is the total number of application scenarios; σ is the sensor observation noise, and y(k) is the actual sensor feedback value;

[0022] S32, using the updated time-varying parameter θ k Predict the state space of the device;

[0023]

[0024]

[0025] in, is the predicted state space of the device at time k+1; x(k) is the state space of the device at time k; u(k) is the control input of the device at time k; is the coupling state matrix; B i Updated predictive control input matrix; is the basic stiffness; h p0 is the stiffness of the initial state; J i and J j are the moments of inertia of the i-th and j-th devices respectively; H 12 is the coupling coefficient between device 1 and device 2; H 21 is the coupling coefficient between device 2 and device 1;

[0026] S33. Determine a global synchronization index based on the position synchronization error and the speed synchronization error of the device to construct an objective function;

[0027]

[0028] Among them, e posis the position synchronization error; e vel is the speed synchronization error; Φ is the global synchronization index; is the objective function; λ is the speed weight; ε is the device connection topology; δ lev is the parameter threshold of the global synchronization indicator.

[0029] Preferably, step S4 specifically includes the following contents:

[0030] S41. Based on the predicted device state space and the objective function, define a cost function for the entire state space;

[0031]

[0032] α(t)=α0+βΦ(t)

[0033] in, is the cost function of the entire state space; α(t) is the dynamic weight used to adjust the position deviation between devices; X(t) and X ref are the current state and expected state of the equipment respectively; U(t) is the control input index; N p is the prediction step size; α0 is the synchronization weight; β is the adaptive coefficient;

[0034] The constraints of the cost function of the entire state space are:

[0035]

[0036] Among them, v max It’s the speed limit; is the global position synchronization of the linkage devices; Q and R are the weight matrices of state and control, respectively, used to balance the tracking error and control input;

[0037] S42. Based on the cost function of the entire state space, obtain a control strategy capable of adaptively and dynamically adjusting control parameters, and dynamically adjust the control parameters of the corresponding device using the control strategy;

[0038]

[0039] u i (k)=U i * (0)

[0040] Among them, U * (k) is the adaptive control input; U i * (0) is the adaptive input initialization value; u i (k) is the control input of the i-th device at time k.

[0041] The beneficial effects of the present invention are: 1. The method of the present invention utilizes the mutual influence between synchronous linkage devices to establish an improved state space mathematical model, introduces the multi-device coupling matrix relationship, and solves the spatial coordination problem of motion equipment. 2. The method of the present invention takes into account the interference of communication time lag and other external factors, and can identify the control parameters of the equipment in different application scenarios in real time, iteratively update the control input and optimize the control instructions, and minimize the problem of equipment linkage asynchrony caused by position and speed deviations, thereby improving the synchronization control accuracy between multiple devices and ensuring the smoothness and comfort of the performance. 3. The method of the present invention has the characteristics of low cost, high precision, easy implementation, adaptability to different equipment formations, and application to different performances. It can reduce the work pressure of operators due to equipment asynchrony, and at the same time enhance the real-time synchronization artistic effect of the performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a technical roadmap of the synchronous control method in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] In order to solve the problem of asynchronous linkage of stage equipment, a synchronous control method for stage mechanical equipment with dynamic optimization of state parameters is provided, which specifically includes the following contents:

[0045] 1. Motion state parameter collection

[0046] Get the motion status parameters of all synchronized devices.

[0047] Specifically: take the position and speed parameters of n devices that are synchronously linked to construct a motion data set, which is expressed as:

[0048] Dev={D1、D2、D i …D n}

[0049] Among them, the motion parameters of each device include the sum of position and speed information description, which can be expressed as:

[0050]

[0051] Among them, MPa Dev is the set of motion parameters of n synchronous linkage devices; is a subset of location data; is the speed data subset; is the position parameter of the i-th device; is the speed parameter of the i-th device; i≤n∈N + , N + is a set of positive integers.

[0052] 2. Establishment of coupled mathematical state model

[0053] An improved state space mathematical modeling is performed for all devices moving synchronously based on the motion state parameters to obtain a coupled mathematical state model.

[0054] Specifically, based on the physical characteristics of the system, an improved state space mathematical model is performed for the n synchronously running formation devices. According to the characteristics of each device, the coupling relationship between devices and adaptive parameters are introduced to adapt to the dynamic changes of the devices. It can be expressed as:

[0055]

[0056]

[0057] in, is x i The first derivative with respect to time; x i and x j are the state vectors of the i-th and j-th devices respectively; A i (θ k ) is the state transfer matrix of the i-th device; B i (θ k ) is the time-varying control input matrix of the i-th device; K s,i is the equivalent stiffness of the i-th device; K t,i is the torque of the i-th device; k is the discrete time step; θ k is the time-varying parameter at time k, and the influencing factor of this parameter is J is the moment of inertia of the equipment; K s is the equivalent stiffness of the equipment; K t is the torque of the equipment; τ com is the communication delay; u i is the control state input of the i-th device; H ij (x j -x i ) is the coupling term that coordinates the relative motion between the i-th and j-th devices; H ij is the coupling matrix between the i-th and j-th devices; h pos is the position coupling coefficient; h vel is the velocity coupling coefficient.

[0058] In traditional state spaces, parameters are acquired statically and used dynamically. This results in the inability of devices to make timely adjustments during operation and obtain state values ​​that are closer to the real environment. This will further increase the synchronization deviation between devices and cause asynchronous operation of device position and speed.

[0059] 3. Parameter Update, State Prediction and Objective Function Construction

[0060] The time-varying parameters in the coupled mathematical state model are updated online to predict the state space of the device. At the same time, a global synchronization index is defined based on the position synchronization error and speed synchronization error of the device to construct the objective function.

[0061] 3.1. In order to adapt to different application scenarios when devices are running synchronously, the time-varying parameter θ of the coupled mathematical state model k To perform online updates based on system dynamic changes, the preset model set is: {θ (1) ,…θ (N)}, the model probability is updated to,

[0062]

[0063] The time-varying parameter update formula is:

[0064]

[0065] in, is the time-varying parameter of the updated model; θ (i′) is the parameter vector of the i′th application scenario; is the probability that the control system at time k is in the i′th application scenario; is the probability that the control system is in the i′th application scenario at time k-1; M is the total number of application scenarios; σ is the sensor observation noise, and y(k) is the actual sensor feedback value.

[0066] 3.2. Then, the updated time-varying parameters are used to predict the state space of the device, which is expressed as:

[0067]

[0068] in, is the predicted state space of the device at time k+1; x(k) is the state space of the device at time k; u(k) is the control input of the device at time k; is the coupling state matrix; B i Updated predictive control input matrix; is the basic stiffness; h p0 is the stiffness of the initial state; J iand J j are the moments of inertia of the i-th and j-th devices respectively; H 12 is the coupling coefficient between device 1 and device 2; H 21 is the coupling coefficient between device 2 and device 1.

[0069] 3.3. Define the global synchronization index based on the position synchronization error and speed synchronization error of the device and construct the objective function.

[0070] Let the position synchronization error be,

[0071]

[0072] The speed synchronization error is,

[0073]

[0074] The global synchronization indicator is,

[0075]

[0076] Then the objective function is constructed as:

[0077]

[0078] Among them, e pos is the position synchronization error; e vel is the speed synchronization error; Φ is the global synchronization index; is the objective function; λ is the speed weight; ε is the device connection topology; δ lev is the parameter threshold of the global synchronization indicator.

[0079] 4. Control Strategy Acquisition

[0080] Based on the predicted state space and the objective function, a cost function of the entire state space of the device is defined; based on the cost function of the entire state space of the device, a control strategy of the device is obtained, and the control parameters of the device are adaptively and dynamically adjusted using the control strategy.

[0081] 4.1. Based on the predicted device state space and the objective function, the cost function of the entire state space is defined as:

[0082]

[0083] α(t)=α0+βΦ(t)

[0084] in, is the cost function of the entire state space; α(t) is the dynamic weight used to adjust the position deviation between devices; X(t) and X ref are the current state and expected state of the equipment respectively; U(t) is the control input index; Np is the prediction step size; α0 is the synchronization weight; β is the adaptive coefficient;

[0085] The constraints of the cost function are

[0086]

[0087] Among them, N p is the prediction step size, α0 is the synchronization weight, β is the adaptive coefficient, v max is the speed limit, u max Enter limits for the control state, It refers to the global position synchronization of the linked devices. Q and R are the weight matrices of state and control, which are used to balance the tracking error and control input.

[0088] 3.3. The control strategy can be obtained based on the cost function of the entire state space, which is expressed as,

[0089]

[0090] After that, the control strategy can be used to adaptively and dynamically adjust the control parameters of the corresponding equipment to ensure system stability. At the same time, it also solves problems such as errors in synchronization linkage.

[0091] u i (k)=U i * (0)

[0092] Among them, U * (k) is the adaptive control input; U i * (0) is the adaptive input initialization value; u i (k) is the control input of the i-th device at time k.

[0093] In this embodiment, through the above steps, the system uses this method to solve the position and speed errors caused by coupling between devices and factor interference, improves the accuracy and stability of synchronous linkage, and maximizes the artistic effect of stage performances.

[0094] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0095] The present invention provides a method for synchronous control of stage mechanical equipment with dynamic optimization of state parameters. The method of the present invention utilizes the mutual influence between synchronous linkage devices to establish an improved state space mathematical model, introduces a multi-device coupling matrix relationship, and solves the spatial coordination problem of motion equipment. The method of the present invention takes into account the interference of communication time lag and other external factors, can identify the control parameters of the equipment in different application scenarios in real time, iteratively update the control input and optimize the control instructions, and minimize the problem of equipment linkage asynchrony caused by position and speed deviations, thereby improving the synchronization control accuracy between multiple devices and ensuring the smoothness and comfort of the performance. The method of the present invention has the characteristics of low cost, high precision, easy implementation, adaptability to different equipment formations, and application to different performances. It can reduce the work pressure of operators due to equipment asynchrony, and at the same time enhance the real-time synchronization artistic effect of the performance.

[0096] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A synchronous control method for stage mechanical equipment with dynamic optimization of state parameters, characterized by: The following steps are included: S1. Obtain the motion state parameters of all synchronized devices; S2. performing improved state space mathematical modeling for all devices in synchronous motion based on motion state parameters to obtain a coupled mathematical state model; S3. Update the time-varying parameters in the coupled mathematical state model online and predict the state space of the device. At the same time, define a global synchronization index based on the position synchronization error and velocity synchronization error of the device to construct the objective function. S4. Based on the predicted device state space and the objective function, a cost function of the entire device state space is defined; a control strategy of the device is obtained according to the cost function, and the control parameters of the device are adaptively and dynamically adjusted using the control strategy.

2. The synchronous control method for stage mechanical equipment with dynamic optimization of state parameters according to claim 1 is characterized in that: In step S1, the motion parameters of each device include the sum of position and velocity information description; Among them, MPa Dev is the set of motion parameters of n synchronous linkage devices; is a subset of location data; is the speed data subset; is the position parameter of the i-th device; is the speed parameter of the i-th device; i≤n∈N + , N + is a set of positive integers.

3. The synchronous control method for stage mechanical equipment with dynamic optimization of state parameters according to claim 2 is characterized in that: Step S2 specifically involves introducing coupling relationships between devices and adaptive parameters based on the characteristics of each device to adapt to the dynamic changes of the devices, so as to construct a coupled mathematical state model; the coupled mathematical state model is expressed as follows: in, is x i The first derivative with respect to time; x i and x j are the state vectors of the i-th and j-th devices respectively; A i (θ k ) is the state transfer matrix of the i-th device; B i (θ k ) is the time-varying control input matrix of the i-th device; K s,i is the equivalent stiffness of the i-th device; K t,i is the torque of the i-th device; k is the discrete time step; θ k is the time-varying parameter at time k, and the influencing factor of this parameter is J is the moment of inertia of the equipment; K s is the equivalent stiffness of the equipment; K t is the torque of the equipment; τ com is the communication delay; u i is the control state input of the i-th device; H ij (x j -x i ) is the coupling term that coordinates the relative motion between the i-th and j-th devices; H ij is the coupling matrix between the i-th and j-th devices; h pos is the position coupling coefficient; h vel is the velocity coupling coefficient.

4. The method for synchronous control of stage mechanical equipment with dynamic optimization of state parameters according to claim 2, characterized in that: Step S3 specifically includes the following contents: S31, according to the dynamic changes of the control system, the time-varying parameters θ of the model k Perform online updates; in, is the time-varying parameter of the updated model; θ (i′) is the parameter vector of the i′th application scenario; is the probability that the control system at time k is in the i′th application scenario; is the probability that the control system is in the i′th application scenario at time k-1; M is the total number of application scenarios; σ is the sensor observation noise, and y(k) is the actual sensor feedback value; S32, using the updated time-varying parameter θ k Predict the state space of the device; in, is the predicted state space of the device at time k+1; x(k) is the state space of the device at time k; u(k) is the control input of the device at time k; is the coupling state matrix; B i Updated predictive control input matrix; is the basic stiffness; h p0 is the stiffness of the initial state; J i and J j are the moments of inertia of the i-th and j-th devices respectively; H 12 is the coupling coefficient between device 1 and device 2; H 21 is the coupling coefficient between device 2 and device 1; S33. Determine a global synchronization index based on the position synchronization error and the speed synchronization error of the device to construct an objective function; Among them, e pos is the position synchronization error; e vel is the speed synchronization error; Φ is the global synchronization index; is the objective function; λ is the speed weight; ε is the device connection topology; δ lev is the parameter threshold of the global synchronization indicator.

5. The synchronous control method for stage mechanical equipment with dynamic optimization of state parameters according to claim 2 is characterized in that: Step S4 specifically includes the following contents: S41. Based on the predicted device state space and the objective function, define a cost function for the entire state space; α(t)=α0+βΦ(t) in, is the cost function of the entire state space; α(t) is the dynamic weight used to adjust the position deviation between devices; X(t) and X ref are the current state and expected state of the equipment respectively; U(t) is the control input index; N p is the prediction step size; α0 is the synchronization weight; β is the adaptive coefficient; The constraints of the cost function of the entire state space are: Among them, v max It’s the speed limit; is the global position synchronization of the linkage devices; Q and R are the weight matrices of state and control, respectively, used to balance the tracking error and control input; S42. Based on the cost function of the entire state space, obtain a control strategy capable of adaptively and dynamically adjusting control parameters, and dynamically adjust the control parameters of the corresponding device using the control strategy; in i (k)=U i * (0) Among them, U * (k) is the adaptive control input; U i * (0) is the adaptive input initialization value; u i (k) is the control input of the i-th device at time k.

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