Reconfigurable vehicle cooperative control method of double predictive controllers

Abstract

The invention is suitable for the field of vehicle automatic control, and provides a reconfigurable vehicle cooperative control method of a dual-predictive controller, and the method comprises the steps: designing a reconfigurable vehicle observer based on extended Kalman filtering, and dynamically adjusting a state vector and a system matrix dimension to adapt to the increase and decrease of a module; establishing a distributed vehicle dynamics model and a model library, dynamically configuring the degree of freedom according to the number of modules, and realizing configuration mapping through a parameterized interface; and a layered coupling controller is constructed, an upper-layer model predicts, controls and outputs total longitudinal force and additional yawing moment, and a lower-layer quadratic programming dynamically distributes driving moment to each hub motor. According to the method, a state perception-model reconstruction-control optimization closed loop is formed, cooperative control with small state error and multi-target dynamic balance is realized, and the method is suitable for reconfigurable vehicles in emergency rescue, polar region exploration and other scenes.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle automatic control, and in particular relates to a reconfigurable vehicle cooperative control method of a dual-predictive controller. Background Art

[0002] With the increasing demand for adaptability to complex environments in civilian scenarios such as emergency rescue, polar exploration, and logistics and transportation, traditional fixed-configuration vehicles are unable to meet the challenges of dynamic mission switching and terrain diversity. Multi-configuration switching intelligent mobility platforms achieve flexible adaptation of functions and performance by dynamically adjusting the mechanical structure (such as modular reconfiguration, wheel / track switching, and wheelbase extension), becoming a core technical direction for improving cross-domain operational capabilities. Among them, reconfigurable vehicles, due to their modular autonomous reconfiguration capabilities, can break through the bottleneck of adaptability to complex terrain and heterogeneous tasks, have broad application prospects, and have become a current research hotspot. Reconfigurable vehicles need to cope with sudden changes in mass distribution, inertia parameters, and drive characteristics caused by dynamic reconfiguration. Adaptive state observation, dynamic modeling, and control strategies need to be designed to achieve efficient collaborative control.

[0003] When applied to reconfigurable vehicles, existing technologies have the following technical problems: in terms of state observation, fixed-dimensional observers cannot adapt to the changes in state vector dimensions caused by the dynamic increase or decrease of modules, and have problems such as insufficient adaptability to parameter mutations and poor real-time performance; in terms of dynamic modeling, there is a lack of distributed parametric modeling mechanism, and the model's degrees of freedom cannot be dynamically adjusted according to the number of modules and connection status, resulting in a mismatch between the model and the real-time topology structure; in terms of control strategy, existing hierarchical control schemes have problems such as rigid static rule bases and lack of hard constraints. They cannot dynamically coordinate multi-objective optimization such as trajectory tracking, stability, and energy efficiency when the number of modules is adjusted, and there are situations where control objectives conflict. Summary of the Invention

[0004] The purpose of the present invention is to provide a reconfigurable vehicle cooperative control method of a dual predictive controller, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is implemented as follows: a reconfigurable vehicle cooperative control method with a dual predictive controller, the method comprising: Step 1: 1. Construct a four-dimensional state vector containing the dynamic state, driving state, energy state, and reconstruction state: 2. Simultaneously construct a nonlinear state space model: 3. Use extended Kalman filter for nonlinear state estimation; 4. Dynamically adjust the state vector and system matrix when adding or removing modules and dimension.

[0006] Step 2: 1. Define the physical parameters of each module, including the number of modules, the wheelbase, the connection type (symmetric / asymmetric), and the task cabin. 2. Establish the single-module dynamics equation and the inter-module coupling dynamics model. 3. Determine the system degrees of freedom based on the number of modules. 4. Through the parameterized interface, input the number of modules n, the wheelbase , the connection type (symmetric / asymmetric), and output the automatically generated topology structure, the assigned inter-module constraints, and activate the corresponding degrees of freedom.

[0007] Step 3: Construct a hierarchical coupling controller: 1. Upper controller based on model predictive control 2. Lower distributor based on quadratic programming Through the lower distributor based on quadratic programming, dynamically distribute the driving torque to each hub motor, output the optimal torque sequence that adapts to the module configuration and energy state, and ensure the driving performance and system safety of the vehicle.

[0008] The beneficial effects of the present application are: The present application proposes a collaborative control method based on state and model dual predictive controller for reconfigurable vehicles. By using an extensible observer to perceive the module connection state in real time, the dimension of the observation matrix is dynamically adjusted when the number of modules changes, solving the problem that the traditional fixed dimension observer cannot adapt to the change of state vector dimension. Based on the real-time topology state driven distributed dynamics modeling, the degrees of freedom configuration and the mechanical constraints of the connection mechanism that match the number of modules are automatically generated, breaking the bottleneck that the fixed model cannot be updated online to adapt to the topology change. A hierarchical controller is constructed, the upper model predictive control embeds the configuration consistency optimization goal and the mechanical hard constraints of the connection mechanism, and the lower quadratic programming dynamically reconstructs the torque distribution boundary according to the module connection state, solving the multi-control objective conflict problem. Finally, a closed loop of "state perception - model reconstruction - control optimization" is formed, breaking the response delay, model mismatch and control failure bottleneck of the traditional scheme when the number of modules changes, achieving state error small, multi-objective dynamic balance collaborative control, and providing an effective method for adaptive collaborative control of reconfigurable vehicles. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical scheme and advantages of the present application clearer, the following embodiments are used to further illustrate the present application. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0010] A reconfigurable vehicle collaborative control method based on a dual predictive controller, the method comprising: Step 1: 1. Construct a four-dimensional state vector comprising a dynamics state, a driving state, an energy state, and a reconfiguration state: ; where: dynamics state is: ; is the longitudinal velocity, is the lateral velocity, is the yaw angular velocity, is the longitudinal acceleration, is the lateral acceleration; driving state is: ; is the wheel hub motor speed, is the torque; energy state is: ; is the battery state of charge, is the voltage, is the current, is the range extender power; reconfiguration state is: ; is the module connection state, is the type.

[0011] 2. Simultaneously construct a nonlinear state space model: ; where is the state vector, is the input vector, is the nonlinear state transition function, is the disturbance vector.

[0012] 3. Perform nonlinear state estimation using extended Kalman filtering, including: prediction stage: ; where is the process noise; Jacobian matrix calculation: , ; The nonlinear state transition function and measurement function are locally linearized by first-order Taylor expansion of the nonlinear function through Jacobian matrix, which transforms the nonlinear system into an approximate linear model, so that the EKF can use linear filtering theory to estimate the state.

[0013] Measurement update stage: ; The measurement noise is Kalman gain and state update: .

[0014] 4. And dynamically adjust the state vector and system matrix when the module is added or removed and dimension: Module access processing: detect the locking signal , initialize the energy state, expand the dimensions of the state vector and system matrix , ; Module removal processing: detect the locking signal , remove the corresponding module from the state vector, and shrink the dimensions of the system matrix , .

[0015] Step 2: 1. Decompose the reconfigurable vehicle into independent mobile modules, connection modules, and task cabins, and define the physical parameters of each module; 2. Establish single-module dynamics equations and inter-module coupling dynamics models, Establishing single-module dynamics equations is specifically: For the first module, establish translational and rotational dynamics equations: Translational equation: ; Rotational equation: ; Establishing inter-module coupling dynamics models is specifically: Establishing inter-module force transmission model: Axial traction force transmission: ; Lateral shear force constraint: .

[0016] 3. Determine the system degrees of freedom according to the number of modules, including: Overall degrees of freedom: 3 translations (X / Y / Z) + 3 rotations (yaw / pitch / roll), a total of 6 global degrees of freedom; Module degrees of freedom: Each mobile module contains the rotational degrees of freedom of two wheels (around the axle); Connection freedom: inter-module connection restricts X / Y / Z translation and pitch / roll rotation, only leaving a small elastic deformation in yaw direction (1 degree of freedom); Total freedom: 7+2n (n: number of modules).

[0017] 4. Input module number n, wheelbase, connection type (symmetric / asymmetric) through parameterized interface, output automatically generated topology, assign inter-module constraints, and activate corresponding degrees of freedom; Step 3: Construct a hierarchical coupling controller: 1. Upper controller based on model predictive control Design objective function: ; Where: is the trajectory tracking error term, is the ride stability term, is the energy optimization term, is the configuration consistency term, 、 、 、 is the weight coefficient, the weight coefficient is based on the reconstruction state and energy state Real-time adjustment, realize self-adaptive adjustment in all working conditions through dynamic weight matrix; Design constraint conditions: The equality constraint is: ; State vector , including dynamics state, driving state, energy state and reconstruction state; input vector , total longitudinal force and additional yaw moment; constant vector

[0018] The inequality constraint is: ; ​Inequality constraints include hardware physical constraints, inter-module force transmission constraints, configuration dynamic constraints, energy and drive constraints, etc. Hardware physical constraints limit the safe range of total longitudinal force, additional yaw moment, and vehicle motion state (vehicle speed, yaw angular velocity, etc.), as well as wheel hub motor torque and battery power limit. Inter-module force transmission constraints ensure connection stability through axial traction force and transverse shear force constraints. Configuration dynamic constraints regulate the dynamic matching of force loading rate and degree of freedom when modules are added or removed. Energy and drive constraints limit battery output power and define torque in the high efficiency zone based on the wheel hub motor efficiency MAP. These constraints are adapted in real time through dynamic adjustment mechanism, with module connection state, road adhesion coefficient, etc., to ensure system safety and stability.

[0019] The above constraints are combined into a unified form: ; The upper MPC outputs the total longitudinal force Additional yaw moment And torque distribution range Provide optimization target and constraint boundary for lower QP algorithm.

[0020] 2. Lower distributor based on quadratic programming Objective function design: ; Where: Used to balance the torque of each wheel hub motor to avoid overload of single wheel hub motor, Used to optimize energy utilization based on wheel hub motor efficiency MAP, Ensure that the torque does not exceed the upper limit of hardware. Dynamic weight coefficient Real-time adjustment according to module connection state, battery SOC, etc., to achieve collaborative optimization of driving performance and safety.

[0021] Design constraint conditions: Driving force balance constraint is: ; Ensure that the total driving force and resistance are balanced, and adapt to the dynamic distribution of torque when modules are added or removed; Yaw moment balance constraint is: ; Based on the installation coordinates of the wheel hub motor, torque is distributed, and the wheelbase weight correction deviation is introduced for asymmetric configuration; Wheel hub motor torque limiting constraint is: ; Combined with the dynamic derating of wheel hub motor temperature to ensure safety; Inter-module torque difference constraint is: ; Prevent the connection mechanism overload, weight lifting when configuration switching; Battery power constraints are: .

[0022] Dynamic derating with SOC and prefer high efficiency region for hub motor.

[0023] Each constraint is adapted to different configurations and working conditions of reconfigurable vehicle through dynamic adjustment mechanism, such as weight correction when module connection state changes, power derating driven by energy state, etc., to ensure the feasibility of torque distribution and system safety.

[0024] The driving torque is dynamically distributed to each hub motor through the lower-level distributor based on quadratic programming, and the optimal torque sequence adapting to the module configuration and energy state is output, to ensure the driving performance of the vehicle and the safety of the system.

[0025] The technical features of the above-described embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above-described embodiments are not described, but as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0026] The above-described embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the present application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

[0027] The above-described only the preferred embodiments of the present application, and not to limit the present application, any modification, equivalent replacement and improvement within the spirit and principles of the present application, should be included in the scope of protection of the present application.

Claims

1. A reconfigurable vehicle cooperative control method based on a dual predictive controller, characterized in that: The method comprises: Construct a four-dimensional state vector including dynamic state, drive state, energy state and reconstruction state, and at the same time build a nonlinear state space model, use extended Kalman filtering for nonlinear state estimation, and dynamically adjust the state vector and system matrix when adding or removing modules and Dimensions; The reconfiguration state is a state composed of the connection state and type information of each module of the reconfigurable vehicle; The reconfigurable vehicle is decomposed into independent maneuvering modules, connection modules, and mission modules. The single-module dynamic equations and inter-module coupling dynamics models are established. The system degrees of freedom are determined based on the number of modules. Through the parameterized interface, the number of modules, wheelbase, and connection type are input, and the automatically generated topology structure, inter-module constraints, and corresponding activated degrees of freedom are output. A hierarchical coupling controller is constructed: the upper layer adopts model predictive control to design the objective function, sets equality constraints and inequality constraints, and outputs the total longitudinal force and additional yaw moment; the lower layer adopts quadratic programming to design the objective function, and sets driving force balance constraints, yaw moment balance constraints, hub motor torque limit constraints, inter-module torque difference constraints and battery power constraints, and dynamically distributes the driving torque used in the driving force balance constraints to each hub motor.

2. The method according to claim 1, characterized in that The construction includes a four-dimensional state vector of dynamic state, driving state, energy state and reconstruction state: ； in: Dynamic state for: ； is the longitudinal velocity, is the lateral velocity, is the yaw angular velocity, is the longitudinal acceleration, is the lateral acceleration; Drive Status for: ； is the speed of each hub motor, is the torque; Energy status for: ； is the battery charge state, For voltage, For current, is the range extender power; Reconstruction status for: ； For module connection status, For type.

3. The method according to claim 1, characterized in that The nonlinear state space model is constructed as follows: ； in is the state vector, is the input vector, is the nonlinear state transfer function, is the perturbation vector.

4. The method according to claim 1, wherein The nonlinear state estimation using extended Kalman filtering includes: Prediction stage: ； in is the process noise, is the control input vector, which represents the system at time step Drive control instructions; Jacobian matrix calculation: ， ； Where, is the Jacobian matrix of the state transfer function; is the Jacobian matrix of the observation function; Measurement update phase: ； Where, is the observation vector; is the observation function; is the state vector; is the observation noise; Kalman gain and state update: ； Where, is the Kalman gain, which is used to weigh the confidence of the predicted value and the measured value; is the state covariance matrix, which represents the uncertainty of state prediction; is the Jacobian matrix of the observation function, which maps the state space to the observation space; is the measurement noise covariance matrix, representing the sensor error; is the time step The predicted value of the state; is the observation vector; is the observation function; is the time step The status update value of is the updated state covariance matrix; is the identity matrix.

5. The method according to claim 1, wherein The dynamic adjustment of state vector and system matrix when modules are added or removed and The dimensions are: Module access processing: Lock signal detected , initialize the energy state, expand the state vector and system matrix 、 Dimensions; Module removal processing: Lock signal detected , remove the corresponding module from the state vector and shrink the system matrix 、 dimension.

6. The method according to claim 1, characterized in that The single module dynamic equation and the inter-module coupling dynamic model are established, wherein: The specific single module dynamic equation is established as follows: For the first Modules to establish translational and rotational dynamics equations: Translation equation: ; Where, For the The quality of each module; For the The acceleration vector of each module; For modules For modules The force of and is the module index, , is the total number of modules; For the The driving force of each module; For the Rolling resistance of each module; Rotation equation: ; Where, For the The moment of inertia tensor of each module; For the The angular velocity vector of each module; For the The angular acceleration vector of each module; For modules For modules The torque; For the Aerodynamic drag moment of each module; The specific steps to establish the inter-module coupling dynamic model are as follows: Establish inter-module force transmission model: Axial traction force transmission: ; Where, For modules and Axial traction between is the axial stiffness coefficient of the connection mechanism; For modules and Axial displacement difference; is the axial damping coefficient of the connection mechanism; is the derivative of the axial displacement difference; is the preload force of the connection mechanism; Lateral shear restraint: ; Where, is the lateral shear force between modules, is the vertical shear force between modules; is the friction coefficient of the connecting surface.

7. The method according to claim 1, characterized in that The system degrees of freedom are determined based on the number of modules, where the total degrees of freedom is 7+2n, where n is the number of modules, and specifically includes: There are 6 global degrees of freedom, including 3 translations and 3 rotations, 2 rotational degrees of freedom for each mobility module, and 1 connection degree of freedom for elastic deformation in the yaw direction between modules.

8. The method according to claim 1, characterized in that The upper layer adopts model predictive control to design the objective function: ； in: is the trajectory tracking error term, is the driving stability item, is the energy optimization term, is the configuration consistency term, 、 、 、 is the weight coefficient; The lower layer adopts quadratic programming to design the objective function: ； in: is the wheel hub motor torque balance item; This is the efficiency optimization item for the hub motor; It is a hardware security protection item; It is a dynamic weight coefficient, which is adjusted according to the module connection status and battery SOC.

9. The method according to claim 1, characterized in that The equality constraints are: ； Where, is the coefficient matrix of the state vector; is the state vector at time k, is the coefficient matrix of the input vector; for The input vector at time instant; is a constant vector; The inequality constraints are: ； Where, is the inequality coefficient matrix of the state vector; Inequality coefficient matrix of input vector; is the time-varying constraint boundary vector.

10. The method according to claim 1, characterized in that The driving force balance constraint is: ； Where, For the Output torque of each wheel hub motor; is the effective radius of the wheel; is the total longitudinal force output by the upper MPC; is the vehicle running resistance; The yaw moment balance constraint is: ； Where, Indicates the coordinates of the hub motor in the vehicle body coordinate system; is the additional yaw moment output by the upper MPC; The torque limit constraint of the hub motor is: ； Where, is the lower limit of the hub motor torque, is the upper limit of the hub motor torque; The torque difference constraint between modules is: ； Where, is the friction coefficient of the connection mechanism; It is the pre-tightening force of the connection mechanism; The battery power constraint is: ； Where, For the The speed of the wheel hub motor; The maximum allowable output power of the battery.

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