Layered servo control method for traveling tracked vehicles

Abstract

The invention discloses a layered servo control method for a traveling tracked vehicle. A layered control framework is divided into a planning layer and a control layer; the planning layer receives the target position and speed and the current position and speed of the artillery through a dynamic planning model, plans a group of artillery position and speed sequences with shortest time consumption by taking the speed and acceleration of the artillery as constraints, and outputs the artillery position and speed sequences to the control layer; and the control layer receives the artillery position, the speed sequence and the design control law output by the planning layer through a model prediction controller, outputs a voltage control signal to a servo control system of the tracked vehicle, obtains a prediction error through feedback control, and obtains the voltage control signal through rolling optimization. According to the method, the servo system of the tracked vehicle during running is controlled by utilizing a dynamic planning algorithm and model prediction control, a planning layer provides a good signal for a control layer in hierarchical control, the control layer and the planning layer form a virtual closed loop, planning of the planning layer is influenced, a good control effect on the servo system of the tracked vehicle is achieved, and the control efficiency of the tracked vehicle is improved. And the convergence speed and the stability precision of the servo system are improved to a certain extent.

Description

Technical Field

[0001] The invention relates to the field of automation, and in particular to a layered servo control method for a moving tracked vehicle. Background Art

[0002] Tracked vehicles have good protection, mobility and passability. Their superior performance makes tracked vehicles play a variety of roles in modern society. They can not only protect the safety of passengers and equipment in the vehicle, but also quickly adjust the formation and deploy quickly. In the information age, tracked vehicles can obtain more information through advanced sensors and better perceive the environment. In addition, tracked vehicles can also be equipped with artillery and other equipment to provide powerful fire support. The artillery on the tracked vehicle requires a servo system to stabilize it. The servo control on the move requires high precision, fast convergence speed, and small oscillation when stable, so that the artillery can obtain a better stability effect similar to that in a static state. The servo stability control of tracked vehicles on the move has a higher technical difficulty. With the development of modern advanced sensor equipment and information equipment, the servo stability control of tracked vehicles on the move has become possible.

[0003] The main functions of the servo system of a tracked vehicle on the move include target tracking, aiming and aiming stability. In the face of higher requirements in modern society, the servo system needs to have a faster response capability. It needs to be able to respond quickly to the changing position and speed of the target and improve the tracking rate. Tracked vehicles are often in a moving state, which requires the servo system to have a higher response speed and stability accuracy. However, tracked vehicles will be subject to more interference when moving, and the servo control of artillery is more difficult. In addition, the most commonly used three-ring PID controller in the servo system of tracked vehicles on the move is currently, but the use of pre-designed parameters cannot guarantee that the servo system can meet the requirements of shooting accuracy and response speed, and the PID controller is prone to overshoot and slow convergence. At present, how to use a new control algorithm to control the servo system in order to obtain better control effects is an urgent problem to be solved. Summary of the invention

[0004] The object of the present invention is to provide a hierarchical control method for a servo control system of a moving tracked vehicle, with dynamic programming as a planning layer and a model predictive controller as a control layer.

[0005] The technical solution to realize the present invention is: a layered servo control method for a moving tracked vehicle, comprising the following steps:

[0006] Step 1: Hierarchical control architecture design: The hierarchical control architecture is divided into planning layer and control layer;

[0007] Step 2, planning layer design: receive the target position and speed, as well as the current position and speed of the gun through the dynamic programming model, and plan a set of gun position and speed sequences with the shortest time consumption based on the gun speed and acceleration, and output them to the control layer;

[0008] Step 3, control layer design: receive the gun position and speed sequence output by the planning layer through the model predictive controller, design the control law, output the voltage control signal to the servo control system of the tracked vehicle, obtain the prediction error through feedback control, and obtain the voltage control signal through rolling optimization.

[0009] Further, step 2, planning layer design: receive the target position, speed, and the current position and speed of the gun through the dynamic programming model, and plan a set of gun position and speed sequences with the shortest time consumption with the speed and acceleration of the gun as constraints, and output them to the control layer. The specific method is as follows:

[0010] Establish a geodetic coordinate system, with the ground plane as the reference, the vertical projection of the tracked vehicle's center of mass on the ground as the origin, the positive x-axis due west of the origin, the positive y-axis due above the origin, and finally determine the z-axis using the right-hand rule;

[0011] Assume that the angle of the gun relative to the geodetic coordinate system is θ, the angular velocity of the gun is ω, the angular acceleration of the gun is a, the angle of the target relative to the vehicle coordinate system is θ_t, the angular velocity is ω_t, the start time of the gun adjustment is t0, and the end time of the gun adjustment is t1. According to the position and velocity of the gun in the geodetic coordinate system, the dynamic equation of the gun changing with time is determined as:

[0012]

[0013] Among them, x(t) is the state variable of the gun, θ(t), ω(t), and a(t) are the changes of the gun's angle, angular velocity, and angular acceleration over time, respectively. is the second-order derivative of the gun angle with respect to time;

[0014] Taking the shortest gun adjustment time as the performance index, the performance index J cost Defined as:

[0015]

[0016] The angle and angular velocity of the gun are discretized and analyzed, and divided into discrete states with intervals of Δθ and Δω respectively. In a control cycle, the positions of the starting and ending points are fixed, and the state change path with the shortest time from the starting state to the ending state is found through the dynamic programming algorithm; in the discretization process, it is assumed that the acceleration of the gun remains unchanged between the two levels, and the average speed, time, and acceleration of the gun between two adjacent levels are:

[0017]

[0018] In the formula, k represents the number of stages, ω[k] is the angular velocity of the kth stage, ω avg [k→k+1] is the average angular velocity from the kth level to the k+1th level, Δt[k→k+1] is the time consumed for the state transition from the kth level to the k+1th level, a[k→k+1] is the angular acceleration from the kth level to the k+1th level, and the process points that do not meet the upper and lower limits of acceleration are eliminated through the acceleration, and J is used to calculate the acceleration. cost As a performance indicator, keep the group with the smallest performance indicator as the remaining cost. When performing the next level of calculation, only the remaining cost of the previous level needs to be considered. Similarly, reverse the process from the end point to the starting point to determine the optimal path for dynamic planning.

[0019] The calculation formula for the remaining cost from Ni level to N level is:

[0020]

[0021] By finding the state point with the minimum cost function at each level, that is, finding the state point that makes the cost J 1→N The minimum path between (t0, t1) is x1(t0)→x2(j2)→x3(j3)…→x N (t1), plan the gun position and angular velocity sequence.

[0022] Further, step 3, control layer design: receive the gun position and speed sequence output by the planning layer through the model predictive controller, design the control law, output the voltage control signal to the servo control system of the tracked vehicle, obtain the prediction error through feedback control, and obtain the voltage control signal through rolling optimization. The specific method is:

[0023] Taking the permanent magnet synchronous motor as the controlled object of the model predictive controller, the prediction model is:

[0024]

[0025] In the formula, i d 、i q Indicates d and q axis current; u d 、u q Indicates d and q axis voltage; L d , L q Represents the d and q axis inductance; R s represents the coil resistance; ψ m is the permanent magnet flux; ρ is the number of motor pole pairs; T1 is the load torque; J is the motor moment of inertia; ω e is the electromagnetic angular velocity of the motor; ω r is the motor rotor angular velocity; f m is the viscous friction coefficient;

[0026] A surface-mounted permanent magnet synchronous motor is used as the drive motor of the artillery. d and the corresponding differential term is equal to 0, then the prediction model becomes:

[0027]

[0028] Select i q ,ω r As a state variable, u q As the control quantity, the state space equation is obtained:

[0029]

[0030] Discretization, the state space equation at time k+1 is:

[0031]

[0032] Where x PMSM (k) is the state variable of the permanent magnet synchronous motor at time k, C=

[01] T , T is the sampling time;

[0033] The state equation at time k-1 is:

[0034]

[0035] Subtract the state equation at time k+1 from the state equation at time k-1, and we get:

[0036]

[0037] In the formula, is the derivative of the q-axis current change at time k+1, is the derivative of the motor rotor speed change at time k+1, Δu q (k-1) is the change of the control quantity at time k-1, let x MPC =[Δi q Δω r ω r ] T , C MPC =[0 0 1] T , and the prediction model is:

[0038]

[0039] In the formula, x MPC (k) is the state variable of the model predictive controller at time k, yMPC (k) is the output of the model predictive controller at time k;

[0040] By analogy, the predicted state at time k+N is:

[0041]

[0042] Where N P is the prediction step length; N C is the control step size, and N C ≤N P .

[0043] Expand the model predictive controller output equation:

[0044] Y=Y0+Y U ΔU

[0045] in

[0046] Through the prediction model, the predicted speed at certain moments in the future is obtained. The predicted speed is subtracted from the actual speed to obtain the prediction error:

[0047] e(k)=y s (k)-y(k)

[0048] In the formula, y s is the actual rotation speed of the gun;

[0049] Assume that the error after time k is consistent with the error at time k, that is:

[0050] e(k+N P )=e(k+N P -1)=...=e(k)

[0051] Written in matrix form:

[0052] E=[1 1 … 1] T e(k)

[0053] Where, E=[e(k) e(k+1) ... e(k+N P )]

[0054] In order to solve the optimal control sequence, the quadratic cost function is assumed to be:

[0055]

[0056] Among them, J MPC is the quadratic cost function of the model predictive controller, y r (k+1) is the predicted reference value of the model predictive controller at time k+1; qi and r i is the weight coefficient, and J MPC Convert to matrix form:

[0057] J MPC =(Y r -YE) T Q(Y r -YE)+ΔU T RΔU

[0058] in, is the output error weight matrix; represents the input control weight matrix, ΔU=[Δu q (k) Δu q (k+1) … Δu q (k+N C -1)] T ;

[0059] To minimize the cost J, the necessary and sufficient condition needs to be The voltage input increment is obtained as:

[0060] Δu q (k)=(R 2 +Y U T QY U ) -1 Y U T Q(Y r -YE)

[0061] Therefore, the voltage control signal at time k, that is, the controller output signal u q (k) = Δu q (k)+u q (k-1).

[0062] A layered servo control system for a moving tracked vehicle implements the layered servo control method for a moving tracked vehicle to achieve layered servo control for the moving tracked vehicle.

[0063] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for layered servo control of a moving tracked vehicle is implemented to realize layered servo control of a moving tracked vehicle.

[0064] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the layered servo control method for a moving tracked vehicle is implemented to achieve layered servo control for a moving tracked vehicle.

[0065] Compared with the prior art, the present invention has the following significant advantages: when controlling the servo system of a moving tracked vehicle, the model prediction hierarchical control method based on the dynamic programming algorithm has different degrees of improvement in the convergence speed and stability accuracy of the servo system compared with the traditional PID control algorithm. This method well meets the stability requirements of the moving tracked servo control system, making it possible to apply the algorithm in the engineering application of future land warfare fire control systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the overall process of the moving tracked vehicle of the present invention.

[0067] Figure 2 It is a schematic diagram of the hierarchical control architecture.

[0068] Figure 3 It is a schematic diagram of a discrete control system.

[0069] Figure 4 This is a schematic diagram of the path cost from the second-to-last level to the penultimate level in dynamic programming.

[0070] Figure 5 This is a schematic diagram of the path cost from level N-2 to level N-1.

[0071] Figure 6 This is a schematic diagram of the path cost from level 1 to level 2.

[0072] Figure 7 It is a schematic diagram of model predictive control.

[0073] Figure 8 Comparison of hierarchical control and traditional three-loop control simulation high and low output

[0074] Fig. 9 Comparison of hierarchical control and traditional three-loop control simulation horizontal output

[0075] Fig.10 Planning layer planning high and low speed sequence diagram

[0076] Fig.11 Planning layer planning horizontal speed sequence diagram DETAILED DESCRIPTION

[0077] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0078] Combination Figure 1The present invention is based on a gun model prediction control method of a dynamic programming algorithm. The position and speed of the gun on the tracked vehicle are discretized, and the path of the state transfer is solved with the shortest time consumed by the state transfer as the planning goal. The output is a set of gun position and speed sequences, which are used as the input of the control layer model prediction controller. The servo control system of the tracked vehicle is controlled by the model prediction controller, thereby improving the stability accuracy and convergence speed of the servo control system. The specific steps are as follows:

[0079] Step 1: Hierarchical control architecture design

[0080] Combination Figure 2 The hierarchical control architecture is divided into a planning layer and a control layer. The planning layer receives the target position and speed, as well as the current position and speed of the gun, and plans a set of planned speed sequences with the shortest time consumption based on the speed and acceleration of the gun, and outputs it to the control layer. The control layer receives the planned gun position and speed sequence, designs the control law, and outputs the control quantity to the servo control system; the gun feeds back its own position and speed information in real time.

[0081] Step 2: Design of dynamic programming algorithm for planning layer

[0082] First, establish a geodetic coordinate system, with the ground plane as the reference, the vertical projection of the tracked vehicle's center of mass on the ground as the origin, the positive x-axis due west of the origin, the positive y-axis due above the origin, and finally determine the z-axis using the right-hand rule.

[0083] For the moving servo control system, assume that the angle of the gun relative to the geodetic coordinate system is θ, the angular velocity of the gun is ω, the angular acceleration of the gun is a, the angle of the target relative to the vehicle coordinate system is θ_t, the angular velocity is ω_t, the start time of gun adjustment is t0, and the end time of gun adjustment is t1.

[0084] According to the position and speed of the gun in the geodetic coordinate system, the dynamic equation for determining the gun's change over time is:

[0085]

[0086] Among them, x(t) is the state variable of the gun, θ(t), ω(t), and a(t) are the changes of the gun's angle, angular velocity, and angular acceleration over time, respectively. is the second-order derivative of the gun angle with respect to time.

[0087] Taking the shortest gun adjustment time as the performance index, the performance index J cost Defined as:

[0088]

[0089] like Figure 3As shown in the figure, the angle and angular velocity of the gun are discretized and analyzed, and the angle and angular velocity are divided into discrete states with intervals of Δθ and Δω respectively. In a control cycle, the positions of the starting and ending points are fixed. The following dynamic programming algorithm is used to find the state change path with the shortest time from the starting state to the ending state. In the discretization process, it is assumed that the acceleration of the gun remains unchanged between the two levels, so the average speed, time, and acceleration of the gun between the two adjacent levels are:

[0090]

[0091] In the formula, k represents the number of stages, ω[k] is the angular velocity of the kth stage, ω avg [k→k+1] is the average angular velocity from the kth level to the k+1th level, Δt[k→k+1] is the time consumed for the state transition from the kth level to the k+1th level, a[k→k+1] is the angular acceleration from the kth level to the k+1th level, and the process points that do not meet the upper and lower limits of acceleration are eliminated through the acceleration, and J is used to calculate the acceleration. cost As a performance indicator, retain the group with the smallest performance indicator as the remaining cost. When performing the next level of calculation, only the remaining cost of the previous level needs to be considered. Similarly, by working backwards from the end point to the starting point, the optimal path of dynamic planning can be determined.

[0092] The possible paths from the last level to the second-to-last level are as follows Figure 4 As shown, x N (t1), x N-1 (1), x N-1 (2)…x N-1 (M) is all possible discrete states of level N-1, and we can get the level N-1 state x N-1 (i) The cost to reach the final state can be expressed as:

[0093]

[0094] In the formula, i can be any integer from 1 to M, J N-1→N (i,t1) represents the state x N-1 (i) The cost to reach the final state, Δt N-1→N (i, t1) means that the cost from any state in the N-1th level to the final state can be obtained.

[0095] The cost of calculating the N-2th level to the Nth level can be considered in two steps. Figure 5 As shown, first calculate the state node x of level N-2 N-2 (i) The cost J of all paths to level N-1 N-2→N-1 (i,j). According to the cost of any state in level N-1 to the final state, then find the N-2→N(i, t1) The smallest value of J can tell the node x N-2 (i) The shortest path to the final state is x N-2 (i)→x N-1 (j)→x N (t1).

[0096]

[0097] Through the above analysis, we can know the shortest path from each state node of level N-2 to the target state. When considering the cost of the next level, we can use J N-2→N (i, t1) is substituted into the calculation as the remaining cost. At this point, no matter what the state of the next level is, the shortest path from each state node of level N-2 to the target state will not be affected.

[0098] like Figure 6 The state transition path from level 1 to level 2 is analyzed as shown, and the total cost from level 1 to level N is:

[0099]

[0100] At this point, the state transition path with the minimum cost is obtained. Through the state transition path with the minimum cost, the planned reference angular position and angular velocity sequence can be obtained and passed to the model predictive controller of the control layer as input.

[0101] Step 3: Control layer model predictive control design

[0102] The gun reference angular position and angular velocity sequence output by the planning layer are obtained from step 2 and used as the input of the model predictive controller. The schematic diagram of the model predictive control is shown in Figure 7 As shown. Taking the permanent magnet synchronous motor as the controlled object of the model predictive controller, the prediction model is:

[0103]

[0104] In the formula, i d 、i q Indicates d and q axis current; u d 、u q Indicates d and q axis voltage; L d , L q Represents the d and q axis inductance; R s represents the coil resistance; ψ m is the permanent magnet flux; ρ is the number of motor pole pairs; T1 is the load torque; J is the motor moment of inertia; ω e is the electromagnetic angular velocity of the motor; ω r is the motor rotor angular velocity; f m is the viscous friction coefficient.

[0105] A surface-mounted permanent magnet synchronous motor is used as the drive motor of the gun. d =0 control. Therefore, it can be approximately considered that i d and its derivative term is equal to 0, then the prediction model becomes:

[0106]

[0107] Select i q ,ω r As a state variable, u q As the control quantity, the following state space equation can be obtained:

[0108]

[0109] Discretizing it, we can get the state space equation at time k+1:

[0110]

[0111] Where x PMSM (k) is the state variable of the permanent magnet synchronous motor at time k, C = [0 1] T T is the sampling time.

[0112] The state equation at time k-1 is:

[0113]

[0114] Subtracting the state equation at time k+1 from the state equation at time k-1 yields:

[0115]

[0116] In the formula, is the derivative of the q-axis current change at time k+1, is the derivative of the motor rotor speed change at time k+1, Δu q (k-1) is the change of the control quantity at time k-1, let x MPC =[Δi q Δω r ω r ] T , C MPC =[0 0 1] T The prediction model is:

[0117]

[0118] In the formula, x MPC(k) is the state variable of the model predictive controller at time k, y MPC (k) is the output of the model predictive controller at time k.

[0119] By analogy, the predicted state at time k+N is:

[0120]

[0121] Where N P is the prediction step length; N C is the control step size, and N C ≤N P .

[0122] Expanding the model predictive controller output equation yields:

[0123] Y=Y0+Y U ΔU

[0124] in

[0125] Through the prediction model, the predicted speed at certain moments in the future can be obtained. The prediction error can be obtained by subtracting the predicted speed from the actual speed:

[0126] e(k)=y s (k)-y(k)

[0127] In the formula, y s is the actual rotation speed of the gun.

[0128] Since the actual speed output of the gun after time k cannot be obtained, in order to simplify the analysis, it can be assumed that the error after time k is consistent with the error at time k, that is,

[0129] e(k+N P )=e(k+N P -1)=...=e(k)

[0130] Written in matrix form:

[0131] E=[1 1 … 1] T e(k)

[0132] In the formula, E=[e(k) e(k+1) ... e(k+N P )]

[0133] In order to solve the optimal control sequence, the quadratic cost function can be set as:

[0134]

[0135] Among them, J MPCis the quadratic cost function of the model predictive controller, y r (k+1) is the predicted reference value of the model predictive controller at time k+1; q i and r i is the weight coefficient. MPC Converted into matrix form, we get:

[0136] J MPC =(Y r -YE) T Q(Y r -YE)+ΔU T RΔU

[0137] in, is the output error weight matrix; represents the input control weight matrix, ΔU=[Δu q (k) Δu q (k+1) … Δu q (k+N C -1)] T .

[0138] To minimize the cost J, the necessary and sufficient condition needs to be Combining the above formula, the voltage input increment can be obtained as:

[0139] Δu q (k)=(R 2 +Y U T QY U ) -1 Y U T Q(Y r -YE)

[0140] Therefore, the voltage control signal at time k, that is, the controller output signal u q (k) = Δu q (k)+u q (k-1).

[0141] In summary, the present invention is based on a dynamic programming algorithm, and plans the angle and angular velocity sequence with the shortest consumption time according to the current angle and angular velocity of the target, as well as the angle, angular velocity, system angular acceleration, and upper and lower limits of the angular velocity of the self-stabilizing system, to prevent the system from overshooting due to too fast changes in the expected angle. The model predictive control accepts the angle and angular velocity sequence output by the dynamic programming, and uses the model predictive controller to perform rolling optimization on the artillery servo system. The two algorithms complement each other and are organically combined to improve the convergence speed and stability accuracy of the servo system.

[0142] Example

[0143] The effect of the present invention can be further illustrated by the following simulation experiment.

[0144] 1. Simulation conditions

[0145] The simulation time is 20s. The vertical reference input is 4πrad, the horizontal reference input is 3πrad, the simulation step is set to a fixed step of 2e-7s, and the simulated road surface is divided into three levels: C, E, and F. The vehicles pass through different levels of road surface at speeds of 20km / h and 40km / h, respectively.

[0146] The rated torque of the high and low motors is 8.4Nm, and the load torque is set to 2Nm. The upper limit of the acceleration of the planning layer is set to 11428.57rad / s2. Since the load torque can help the motor decelerate during the deceleration process, the minimum acceleration is set to -17571.42rad / s2, and the upper limit of the speed is set to 240rad / s. The single-loop model predictive control of the control layer, the prediction step is set to 10, and the control step is set to 1. The output error weight coefficient is set to 1, and the input control weight coefficient is set to 0.1.

[0147] The rated torque of the horizontal motor is 28.6Nm, and the load torque is set to 10Nm. The upper limit of the planning layer acceleration is set to 4670.66rad / s2, the minimum acceleration is set to -5857.88rad / s2, and the upper limit of the speed is set to 143rad / s. The prediction step of the model predictive control of the horizontal motor is set to 10, and the control step is set to 1. The output error weight coefficient is set to 1, and the input control weight coefficient is set to 0.1.

[0148] 2. Simulation content and result analysis

[0149] The simulation results are as follows Figure 8 High and low output to the motor, Fig. 9 Horizontal motor output, Fig.10 High and low speed sequence, Fig.11 Horizontal velocity sequence,Table 1 Steady-state accuracy table for different grades of road surfaces and different travel speeds.

[0150] from Figure 8 , Fig. 9It can be seen that the three-loop PID control method is an error-based control method. It rises faster at the initial moment, but converges slower. As the distance to the set target value gets closer, the rising speed tends to decrease. The DP-MPC hierarchical control algorithm adopted in this article uses a planning layer to replace the role of the position loop in the traditional three-loop control, and retains some performance during planning to ensure that it can handle emergencies. At the beginning, the rising speed of the hierarchical control method is lower than that of the traditional PID three-loop. In the final stage, since the hierarchical control still follows the planned speed to reach the target value, it can still maintain a faster approach speed. Therefore, although the hierarchical control is not as fast as the traditional three-loop control in the early response speed, it can still reach the set target position faster. From Fig.10 , Fig.11 It can be seen that the speed sequence planned by the planning layer is approximately a trapezoid, and the next optimal speed change sequence is calculated according to the current speed and position in each control cycle.

[0151] Table 1 is a table of steady-state accuracy for different grades of road surfaces and different travel speeds.

[0152] Table 1 Steady-state accuracy of tracked vehicle stability system

[0153]

[0154]

[0155] In summary, the error in the elevation direction is generally higher than that in the horizontal direction. The horizontal direction is mainly the error between the azimuth and the target value. Since the left and right swing amplitude of the control object when the tracked vehicle moves forward is mainly affected by the posture of the vehicle body, the error in the horizontal direction is generally smaller than that in the elevation direction. In addition, it is not difficult to see from the figure that as the speed increases, the output error becomes larger and larger, and the frequency of error change also increases with the speed. It can be seen that this control method can still stabilize the artillery loaded on the moving tracked vehicle.

Claims

1. A layered servo control method for a moving tracked vehicle, characterized in that: The following steps are included Step 1: Hierarchical control architecture design: The hierarchical control architecture is divided into planning layer and control layer; Step 2, planning layer design: receive the target position and speed, as well as the current position and speed of the gun through the dynamic programming model, and plan a set of gun position and speed sequences with the shortest time consumption based on the gun speed and acceleration, and output them to the control layer; Step 3, control layer design: receive the gun position and speed sequence output by the planning layer through the model predictive controller, design the control law, output the voltage control signal to the servo control system of the tracked vehicle, obtain the prediction error through feedback control, and obtain the voltage control signal through rolling optimization.

2. The layered servo control method for a moving tracked vehicle according to claim 1, characterized in that: Step 2, planning layer design: receive the target position and speed, as well as the current position and speed of the gun through the dynamic programming model, and plan a set of gun position and speed sequences with the shortest time consumption based on the gun speed and acceleration, and output them to the control layer. The specific method is as follows: Establish a geodetic coordinate system, with the ground plane as the reference, the vertical projection of the tracked vehicle's center of mass on the ground as the origin, the positive x-axis due west of the origin, the positive y-axis due above the origin, and finally determine the z-axis using the right-hand rule; Assume that the angle of the gun relative to the geodetic coordinate system is θ, the angular velocity of the gun is ω, the angular acceleration of the gun is a, the angle of the target relative to the vehicle coordinate system is θ_t, the angular velocity is ω_t, the start time of the gun adjustment is t0, and the end time of the gun adjustment is t1. According to the position and velocity of the gun in the geodetic coordinate system, the dynamic equation of the gun changing with time is determined as: Among them, x(t) is the state variable of the gun, θ(t), ω(t), and a(t) are the changes of the gun's angle, angular velocity, and angular acceleration over time, respectively. is the second-order derivative of the gun angle with respect to time; Taking the shortest gun adjustment time as the performance index, the performance index J cost Defined as: The angle and angular velocity of the gun are discretized and analyzed, and divided into discrete states with intervals of Δθ and Δω respectively. In a control cycle, the positions of the starting and ending points are fixed, and the state change path with the shortest time from the starting state to the ending state is found through the dynamic programming algorithm; in the discretization process, it is assumed that the acceleration of the gun remains unchanged between the two levels, and the average speed, time, and acceleration of the gun between two adjacent levels are: In the formula, k represents the number of stages, ω[k] is the angular velocity of the kth stage, ω avg [k→k+1] is the average angular velocity from the kth level to the k+1th level, Δt[k→k+1] is the time consumed for the state transition from the kth level to the k+1th level, a[k→k+1] is the angular acceleration from the kth level to the k+1th level, and the process points that do not meet the upper and lower limits of acceleration are eliminated through the acceleration, and J is used to calculate the acceleration. cost As a performance indicator, keep the group with the smallest performance indicator as the remaining cost. When performing the next level of calculation, only the remaining cost of the previous level needs to be considered. Similarly, reverse the process from the end point to the starting point to determine the optimal path for dynamic planning. The calculation formula for the remaining cost from Ni level to N level is: By finding the state point with the minimum cost function at each level, that is, finding the state point that makes the cost J 1→N The shortest path between (t0, t1) is x1(t0)→x2(j2)→x3(j3)…→x N (t1), plan the gun position and angular velocity sequence.

3. The layered servo control method for a moving tracked vehicle according to claim 1, characterized in that: Step 3, control layer design: receive the gun position and speed sequence output by the planning layer through the model predictive controller, design the control law, output the voltage control signal to the servo control system of the tracked vehicle, obtain the prediction error through feedback control, and obtain the voltage control signal through rolling optimization. The specific method is as follows: Taking the permanent magnet synchronous motor as the controlled object of the model predictive controller, the prediction model is: In the formula, i d 、i q Indicates d and q axis current; u d 、u q Indicates d and q axis voltage; L d , L q Represents the d and q axis inductance; R s represents the coil resistance; ψ m is the permanent magnet flux; ρ is the number of motor pole pairs; T1 is the load torque; J is the motor moment of inertia; ω e is the electromagnetic angular velocity of the motor; ω r is the motor rotor angular velocity; f m is the viscous friction coefficient; A surface-mounted permanent magnet synchronous motor is used as the drive motor of the gun. d and the corresponding differential term is equal to 0, then the prediction model becomes: Select i q ,ω r As a state variable, u q As the control quantity, the state space equation is obtained: Discretization, the state space equation at time k+1 is: Where x PMSM (k) is the state variable of the permanent magnet synchronous motor at time k, C=[01] T , T is the sampling time; The state equation at time k-1 is: Subtract the state equation at time k+1 from the state equation at time k-1, and we get: In the formula, is the derivative of the q-axis current change at time k+1, is the derivative of the motor rotor speed change at time k+1, Δu q (k-1) is the change of the control quantity at time k-1, let x MPC =[Δi q Δω r ω r ] T , C MPC =[0 0 1] T , and the prediction model is: In the formula, x MPC (k) is the state variable of the model predictive controller at time k, y MPC (k) is the output of the model predictive controller at time k; By analogy, the predicted state at time k+N is: Where N P is the prediction step length; N C is the control step size, and N C ≤N P . Expand the model predictive controller output equation: Y=Y0+Y U ΔU in Through the prediction model, the predicted speed at certain moments in the future is obtained. The predicted speed is subtracted from the actual speed to obtain the prediction error: e(k)=y s (k)-y(k) In the formula, y s is the actual rotation speed of the gun; Assume that the error after time k is consistent with the error at time k, that is: e(k+N P )=e(k+N P -1)=...=e(k) Written in matrix form: E=[1 1 … 1] T e(k) Where, E=[e(k) e(k+1) ... e(k+N P )] In order to solve the optimal control sequence, the quadratic cost function is assumed to be: Among them, J MPC is the quadratic cost function of the model predictive controller, y r (k+1) is the predicted reference value of the model predictive controller at time k+1; q i and r i is the weight coefficient, and J MPC Convert to matrix form: J MPC =(Y r -Y-E) T Q(Y r -V-E)+ΔU T RΔU in, is the output error weight matrix; represents the input control weight matrix, ΔU=[Δu q (k) Δu q (k+1) … Δu q (k+N C -1)] T ; To minimize the generation J, the necessary and sufficient condition needs to be The voltage input increment is obtained as: Δu q (k)=(R 2 +Y U T QY U ) -1 Y U T Q(Y r -Y-E) Therefore, the voltage control signal at time k, that is, the controller output signal u q (k) = Δu q (k)+u q (k-1).

4. A layered servo control system for a moving tracked vehicle, characterized in that: The layered servo control method for a moving tracked vehicle as described in any one of claims 1 to 3 is implemented to realize the layered servo control of a moving tracked vehicle.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the layered servo control method for a moving tracked vehicle according to any one of claims 1 to 3 is implemented to realize the layered servo control of a moving tracked vehicle.

6. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the layered servo control method for a moving tracked vehicle according to any one of claims 1 to 3 is implemented to realize the layered servo control of a moving tracked vehicle.

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