Energy-saving torque control method of five-axis transfer robot based on dimension reduction display MPC

Abstract

The present application belongs to the technical field of torque control, and particularly relates to a five-axis transfer robot energy-saving torque control method based on dimension reduction display MPC. The method comprises the following steps: collecting current vehicle state information in real time; based on the current vehicle state information, obtaining an optimal torque distribution scheme and an optimal torque instruction from a pre-generated explicit control strategy through quick table lookup; and delivering the obtained optimal torque instruction to a bottom layer drive to control each wheel motor to execute a driving control task under energy consumption optimization.

Description

Technical Field

[0001] This invention belongs to the field of torque control technology, specifically relating to an energy-saving torque control method for a five-axis transport robot based on a dimension-reduced display MPC. Background Technology

[0002] With the rapid development of smart ports and industrial logistics systems, transfer robots have been widely used in complex environments such as enclosed spaces, high-density loading and unloading, and narrow passageways to complete high-precision and high-efficiency cargo handling tasks. To meet the dynamic control requirements under complex paths and heavy-load conditions, some high-performance transfer robots adopt a multi-wheel independent drive structure to improve the overall mobility, load capacity, and terrain adaptability. However, in actual operation, robots often face conditions such as ramp crossings, frequent starts and stops, and complex paths, which places higher demands on the energy consumption control, dynamic response, and execution coordination of the drive system.

[0003] Existing drive control methods still have significant shortcomings in energy consumption optimization. On the one hand, traditional control strategies often adopt fixed ratio or average torque scheduling methods, failing to fully consider the efficiency differences of each drive motor under different operating conditions, resulting in low overall energy utilization. On the other hand, although control methods based on online optimization theoretically have better regulation performance, they suffer from heavy computational burden and slow response speed in high-dimensional multi-cycle systems, making it difficult to meet the real-time requirements under high-frequency control cycles, thus affecting path tracking accuracy and system stability. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an energy-saving torque vector control method for a transport robot based on explicit model predictive control, aiming to solve the problems of low energy consumption control accuracy, poor real-time performance, insufficient response to slope and load changes, heavy computational burden on the controller, and rigid energy efficiency scheduling strategy in existing multi-wheel drive systems.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] This invention provides an energy-saving torque control method for a five-axis transport robot based on dimension-reduced display MPC, comprising the following steps:

[0007] Step 100: Collect current vehicle status information in real time;

[0008] Step 200: Based on the current vehicle status information, quickly look up the optimal torque distribution scheme from the pre-generated explicit control strategy and obtain the optimal torque command;

[0009] Step 300: The obtained optimal torque command is transmitted to the underlying drive to control each wheel motor to perform drive control tasks under energy consumption optimization.

[0010] Furthermore, before quickly looking up the optimal torque distribution scheme from the pre-generated explicit control strategy based on the current vehicle state information, the process also includes:

[0011] The five axes of the five-axis transport robot are simplified into two three-degree-of-freedom dynamic models and one two-degree-of-freedom dynamic model. The vehicle dynamic state equation is established, with longitudinal velocity, lateral velocity and yaw rate as the core vehicle state variables.

[0012] Furthermore, based on the current vehicle state information, the optimal torque distribution scheme is quickly obtained by looking up a table from the pre-generated explicit control strategy, including:

[0013] The original high-dimensional vehicle state vector is subjected to state dimensionality reduction processing to obtain an equivalent low-dimensional vehicle state vector.

[0014] A control equation is introduced, in which the control quantity is the control torque of each wheel. By applying different control torques to different wheels, the vehicle state information can be controlled.

[0015] The energy consumption per unit time of each wheel motor is constructed and the total energy consumption is derived accordingly. Based on the equivalent low-dimensional vehicle state vector, the reference vehicle state vector, the control torque and the total energy consumption, a cost function is constructed to achieve a multi-objective unified optimization of path tracking accuracy and energy minimization.

[0016] Based on the current vehicle state information and control torque, a discrete dynamics model of the vehicle is constructed to describe the vehicle state information at the next moment.

[0017] Based on the discrete dynamics model of the vehicle, a model predictive control problem is constructed, taking into account multiple constraints.

[0018] The model predictive control problem is solved offline by dividing and optimizing the multi-parameter region, generating a mapping relationship between the vehicle state information space and the optimal torque control command, and deploying it to the controller in the form of a lookup table;

[0019] Real-time vehicle status information is acquired and used as input for table lookup to retrieve the optimal torque distribution result.

[0020] Furthermore, the energy consumption per unit time of each wheel motor is constructed, and the total energy consumption is derived from this, including:

[0021] Construct energy consumption models for each wheel motor, and determine its energy consumption per unit time. for:

[0022] ;

[0023] in, Indicates time k wheels wheel speed, Indicates time k wheels The control torque, For the first Efficiency function of an electric motor under different operating conditions;

[0024] According to the above formula, it is defined at time... Total energy consumption for:

[0025] ;

[0026] Where n represents the number of motors.

[0027] Furthermore, based on the equivalent low-dimensional vehicle state vector, the reference vehicle state vector, the control torque, and the total energy consumption, a cost function is constructed to achieve a unified optimization of multiple objectives, including path tracking accuracy and energy minimization.

[0028] ;

[0029] Where Q and R are the weight matrices of the state error and the control input, respectively; For a moment k The reference vehicle state vector; For a moment k The actual state vector, that is, the equivalent low-dimensional vehicle state vector obtained after state dimensionality reduction; N p The length of the time range to be calculated.

[0030] Furthermore, several constraints include upper and lower limits for control inputs, vehicle state boundaries, tire adhesion limits, and motor efficiency operating range constraints.

[0031] Furthermore, the vehicle status information includes longitudinal speed, lateral speed, yaw rate, and wheel speeds.

[0032] Compared with the prior art, the present invention has the following technical effects:

[0033] This invention proposes an energy-efficient torque vector control method based on explicit model predictive control (eMPC). By constructing a dynamics and energy consumption model of a multi-wheeled transport robot system offline, the optimal control law is solved in advance and deployed in the online control system using a lookup table, achieving a high-efficiency energy consumption control strategy without real-time iteration. Considering the high state dimensionality and large lookup table complexity inherent in multi-wheeled systems, a state dimensionality reduction mechanism is further introduced. Multiple wheel-end variables are compressed into a small number of equivalent representative states. While retaining the main dynamic characteristics, this effectively reduces the controller input dimension and the number of lookup table regions, thereby improving the controller's computational efficiency and deployment adaptability, and meeting the high-performance energy-saving control requirements under complex operating conditions. Attached Figure Description

[0034] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the transfer robot of the present invention;

[0036] Figure 2 This is the overall architecture diagram of the energy-saving control system;

[0037] Figure 3 It is a three-degree-of-freedom dynamic model;

[0038] Figure 4 It is a two-degree-of-freedom dynamic model;

[0039] Figure 5 The control flowchart is shown for the energy-saving torque vector control method based on eMPC.

[0040] Figure 6 This is a schematic diagram illustrating the execution process of the eMPC lookup-based control strategy. Detailed Implementation

[0041] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solutions proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. Specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] Figure 1This is a schematic diagram of the transfer robot of the present invention, as shown below. Figure 1 As shown, the transport robot adopts a five-axis, ten-wheel independent drive structure, where each of the ten wheels is independently controlled by a hub motor to output torque. To simplify the control modeling process, considering the actual structural characteristics of the vehicle: each of the two rear axles is fixedly mounted on the front and rear sections respectively, exhibiting similar motion characteristics; therefore, this invention simplifies it into two three-degree-of-freedom dynamic subsystems. The first axle possesses independent drive and steering functions, with its two wheels working together to achieve lateral and longitudinal control of the vehicle, thus it can be simplified into a two-degree-of-freedom dynamic model. Through the above model division, the complexity of overall system modeling and control solution is effectively reduced.

[0043] Figure 2 The overall architecture diagram of the energy-saving control system is as follows: Figure 2 As shown, the energy-saving torque control system for a five-axis transport robot based on dimension-reduced display MPC includes four core functional modules: path planning module, status acquisition module, controller, and actuator.

[0044] Based on the target trajectory of the vehicle generated by the path planning module, the status acquisition module is used to collect the current vehicle status information in real time, including longitudinal speed. lateral velocity yaw rate Wheel speed of each wheel Road surface slope The state variables required by the vehicle are collected and processed by the IMU, wheel speed sensors, and other onboard equipment, forming the vehicle's current state vector. This vector serves as the basic input for subsequent eMPC controller optimization calculations, ultimately forming a complete system state vector for subsequent eMPC optimization calculations.

[0045] The controller is used to quickly look up the optimal torque distribution scheme from a pre-generated explicit control strategy based on the current vehicle status information, and output the calculated control torque commands for each wheel to the underlying actuator.

[0046] The actuators receive control torque commands from each wheel and drive the motors of each wheel to operate in coordination, enabling the vehicle to meet path tracking accuracy and stability requirements while minimizing overall energy consumption. This process is executed in real time in a high-frequency closed-loop manner, ensuring the feasibility, real-time performance, and optimal energy efficiency of the control strategy in the multi-wheel drive transport robot system.

[0047] In one embodiment of the present invention, an energy-saving torque control method for a five-axis transport robot based on dimension-reduced display MPC is provided, comprising the following steps:

[0048] Step 100: Collect current vehicle status information in real time. The required vehicle status variables include longitudinal speed, lateral speed, yaw rate, wheel speed of each wheel, and road slope.

[0049] Step 200: Based on the current vehicle status information, quickly look up the optimal torque distribution scheme from the pre-generated explicit control strategy and obtain the optimal torque command;

[0050] Step 300: The obtained optimal torque command is transmitted to the underlying drive to control each wheel motor to perform drive control tasks under energy consumption optimization.

[0051] The following is a detailed explanation of each of the above steps:

[0052] Step 100: Collect current vehicle status information in real time, including longitudinal acceleration, lateral acceleration, vehicle yaw rate, and wheel rotation speed.

[0053] The aforementioned state information is collected and processed by the IMU, wheel speed sensors, and other on-board equipment, and constitutes the current system state vector of the vehicle. This vector serves as the basic input for subsequent eMPC controller optimization calculations, ultimately forming a complete system state vector for subsequent eMPC controller optimization calculations.

[0054] Step 200: Based on the current vehicle status information, quickly look up the optimal torque distribution scheme and optimal torque command from the pre-generated explicit control strategy.

[0055] In step 200, the specific process is as follows:

[0056] Step 210: Simplify the five axes of the five-axis transport robot into two three-degree-of-freedom dynamic models and one two-degree-of-freedom dynamic model, establish the vehicle dynamic state equation, and use longitudinal velocity, lateral velocity and yaw angular velocity as the core vehicle state variables to describe the dynamic behavior of the vehicle in complex scenarios.

[0057] Figure 3 For a three-degree-of-freedom dynamic model, such as Figure 3 As shown, the five-axis transport robot model is simplified into a three-degree-of-freedom dynamic model, and the dynamic equations are established based on the three-degree-of-freedom dynamic model:

[0058] ;

[0059] in, It is the total mass of the vehicle. It is air density. It is the aerodynamic drag coefficient. B is the frontal area of ​​the vehicle, and A is the wheelbase. It is the longitudinal velocity of the vehicle's center of gravity. It is the first derivative of the longitudinal velocity of the vehicle's center of mass with respect to time. It is the lateral velocity of the vehicle's center of gravity. It is the first derivative of the lateral velocity of the vehicle's center of mass with respect to time. It is the vehicle's yaw rate. It is the first derivative of the vehicle's yaw rate with respect to time. It is the moment of inertia of the vehicle about its vertical axis. and It is the distance from the vehicle's center of gravity to the front and rear axles. and These are the steering angles of the left and right front wheels, respectively. These are the longitudinal forces on the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. These are the lateral forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively.

[0060] Figure 4 For a two-degree-of-freedom dynamic model, such as Figure 4 As shown. The front axis of the five-axis transport robot is simplified into a two-degree-of-freedom dynamic model, and the dynamic equations are established based on the two-degree-of-freedom dynamic model:

[0061] ;

[0062] in, It's the steering angle.

[0063] Step 220: Based on the current vehicle status information, quickly look up the optimal torque distribution scheme and optimal torque command from the pre-generated explicit control strategy.

[0064] Figure 5 This is a schematic diagram illustrating the execution process of a lookup-based control strategy, such as... Figure 5 As shown. The lookup table control strategy proposed in this invention employs a dimension-reduced explicit model predictive control (eMPC) method, aiming to achieve dynamic optimal control between path tracking accuracy and energy efficiency for a multi-wheel drive transport robot. The specific steps are as follows:

[0065] Step 221: Perform state dimensionality reduction processing on the original high-dimensional vehicle state vector to obtain an equivalent low-dimensional vehicle state vector, which is used as the input state of the controller.

[0066] Based on vehicle state information, the vehicle dynamics state equation is defined as follows:

[0067] ;

[0068] Where X(t) represents the original high-dimensional vehicle state vector. a x For the longitudinal acceleration of the vehicle, ay For the vehicle's lateral acceleration, The vehicle's yaw rate. For wheels i The rotational speed, i = 1~10.

[0069] By designing a linear mapping matrix T The original high-dimensional state vector X ( t Mapped to an equivalent low-dimensional vehicle state vector X r Here, the average speed of the 10 wheels is used as part of the state variables after dimensionality reduction, simplifying the representation of information about wheel speed. After dimensionality reduction, it becomes:

[0070] ;

[0071] ;

[0072] In the above formula, Indicates wheel i The average rotational speed; T represents the linear mapping matrix.

[0073] Wherein, the mapping matrix T is:

[0074] ;

[0075] The above equivalent low-dimensional vehicle state vector It is used as the lookup input for the display controller during the control law matching stage.

[0076] Step 222: Introduce the control equation, where the control quantity is the control torque of each wheel, and the control torque vector is composed of the control torques of each wheel of the vehicle.

[0077] After obtaining the low-dimensional state vector, the control variables need to be defined to control the vehicle's motion. The control equations specify that the control variables are the control torques of each wheel. By applying different torques to different wheels, control objectives such as steering, acceleration, and deceleration can be achieved, making them key operational variables for vehicle dynamics control. The control equations are as follows:

[0078] ;

[0079] In the above formula, T i For wheels i Control torque; This represents the control torque vector composed of the control torques of each wheel of the vehicle at a discrete time point k. It is the control torque used to adjust the vehicle's motion state and achieve the control objective.

[0080] Step 223: Construct the energy consumption per unit time of each wheel motor and derive the total energy consumption accordingly. Based on the vehicle state vector, reference vehicle state vector, control torque and total energy consumption, construct a cost function to achieve multi-objective unified optimization of path tracking accuracy and energy minimization.

[0081] To establish a unified objective for path tracking and energy consumption optimization, we first construct an energy consumption model for each wheel motor, measuring its energy consumption per unit time. for:

[0082] ;

[0083] in, Indicates time k wheels wheel speed, Indicates time k wheels The control torque, For the first Efficiency function of a motor under different operating conditions.

[0084] According to the above formula, it is defined at time... The total energy consumption is:

[0085] ;

[0086] in, n This indicates the number of motors.

[0087] To achieve a unified optimization of multiple objectives, including path tracking accuracy and energy minimization, a cost function is defined. as follows:

[0088] ;

[0089] Where Q and R are the weight matrices of the state error and the control input, respectively; For a moment k The reference vehicle state vector; For a moment k The actual state vector, that is, the equivalent low-dimensional vehicle state vector obtained after state dimensionality reduction; N p The length of the time range to be calculated.

[0090] The optimization framework unifies path tracking accuracy (reflected in the state error term) and energy consumption (reflected in the control input term). During optimization, both objectives need to be considered comprehensively to find an optimal control strategy that allows the system to track the reference path as accurately as possible while operating with low energy consumption.

[0091] Step 224: Based on the current vehicle state and control torque, construct a discrete dynamics model of the vehicle to describe the vehicle state at the next moment.

[0092] In order to predict the future state of a vehicle in order to design and optimize control strategies, a dynamic model of the vehicle needs to be established.

[0093] Discrete dynamics models are used to calculate the vehicle state at the next moment based on the current vehicle state and control torque. They describe the discrete changes in vehicle state over time and are the foundation for predicting future vehicle states and implementing control. For example, based on the current longitudinal acceleration, lateral acceleration, yaw rate, average wheel speed, and control torque of each wheel, the values ​​of these state variables at the next moment can be calculated using a discrete dynamics model, thereby enabling dynamic tracking and control of the vehicle's motion.

[0094] The discrete dynamics model of the vehicle is as follows:

[0095] ;

[0096] exist k The state at time +1 It is by k state of time and k Moment The function is jointly determined. f It is the state transition function.

[0097] Step 225: Based on the vehicle discrete dynamics model, construct the model predictive control problem, taking into account multiple constraints such as energy minimization, path tracking accuracy, and motor operating range.

[0098] A series of physical and performance constraints are introduced, including upper and lower limits of control input, vehicle state boundaries, tire adhesion limits, and motor efficiency operating range constraints, to ensure that the generated control commands are executable and energy-efficient.

[0099] ;

[0100] in, These are the lower and upper limits of the control input, respectively, which restrict the physical boundaries of the motor torque. These are the lower and upper limits of the state variables, respectively, constraining the vehicle's dynamic state within a safe range; Indicates the first Each drive wheel at any time The longitudinal tire force corresponds to the driving force or braking force during acceleration and deceleration. For the first The adhesion limit zone of a tire under current working conditions is usually represented by a friction circle or friction ellipsoid, which is used to ensure that the force on the tire does not exceed the ground adhesion capacity. This represents the acceptable range of motor operating efficiency, used to avoid entering the low-efficiency or overheating zone.

[0101] Step 226: In the control strategy design stage, the model predictive control problem is divided into multiple parameter regions and optimized offline to generate the mapping relationship between the state space and the optimal torque control command, and deployed to the controller in the form of a lookup table.

[0102] First, the state space is divided into each region For each partitioned region, an explicit control law is derived using a complex optimization algorithm. Assume the state vector is... x The explicit control law in the j-th region can be expressed as:

[0103] ;

[0104] In the above formula, Indicates the state x Belongs to the region The optimal control torque vector at that time; Representation and region The corresponding state feedback gain matrix determines the weights of the linear relationship between the control command and the state vector; It is a bias term used to further adjust the control command to better match the optimal control requirements of the region; This represents the j-th state space region.

[0105] This process generates a precise mapping between the state space and the optimal torque control command. This mapping is then deployed to the controller in the form of a lookup table, forming an offline-built explicit policy library.

[0106] During actual vehicle operation, online control is required. First, the region to which the current vehicle state belongs needs to be quickly located within the offline-built explicit policy library. This relies on the real-time vehicle state information provided by the state acquisition module, including key state parameters such as the vehicle's position, speed, acceleration, and yaw angle.

[0107] After receiving the current state information, the controller quickly determines the state space region it belongs to. Once the region to which the state belongs is determined, the controller can retrieve the corresponding control law from the explicit policy library.

[0108] At the same time, the controller also combines the acquired reference speed and desired heading angle information. The reference speed is an ideal speed preset based on factors such as the vehicle's driving task and road conditions, while the desired heading angle is the direction the vehicle should travel.

[0109] Based on the current state, reference speed, and desired heading angle, the controller directly calculates the optimal torque distribution. This lookup process relies heavily on the control strategy database generated through multi-parameter optimization during the initial offline phase.

[0110] During online operation, the status acquisition module provides the current status. After determining its location, the controller outputs the optimal control command according to the corresponding explicit control law. :

[0111] ;

[0112] By combining offline design with online application, the control system can achieve rapid response while ensuring control accuracy, effectively realize vehicle dynamics control, enable the vehicle to accurately track the reference trajectory, and meet performance requirements such as energy consumption.

[0113] Step 227: During system operation, the current vehicle status information is acquired in real time, which is used as the lookup input to quickly retrieve the optimal torque distribution result, and the result is sent to each drive motor to achieve high-frequency, low-energy torque coordinated control.

[0114] The control signal is sent to the underlying driver, and the motor executes the corresponding torque to achieve the unified goal of path accuracy control and energy minimization.

[0115] Step 300: The obtained optimal torque command is transmitted to the underlying drive to control each wheel motor to perform drive control tasks under energy consumption optimization.

[0116] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An energy-saving torque control method for a five-axis transport robot based on dimension-reduced display MPC, characterized in that, Includes the following steps: Step 100: Collect current vehicle status information in real time; Step 200: Based on the current vehicle status information, quickly look up the optimal torque distribution scheme from the pre-generated explicit control strategy and obtain the optimal torque command; Step 300: The obtained optimal torque command is transmitted to the underlying drive to control each wheel motor to perform drive control tasks under energy consumption optimization; Based on the current vehicle state information, before quickly looking up the table from the pre-generated explicit control strategy to obtain the optimal torque distribution scheme, the process also includes: simplifying the five axes of the five-axis transfer robot into two three-degree-of-freedom dynamic models and one two-degree-of-freedom dynamic model, establishing the vehicle dynamic state equation, and using longitudinal velocity, lateral velocity and yaw rate as the core vehicle state variables. Based on the current vehicle status information, the optimal torque distribution scheme is quickly obtained from a pre-generated explicit control strategy by looking up a table, including: The original high-dimensional vehicle state vector is subjected to state dimensionality reduction processing to obtain an equivalent low-dimensional vehicle state vector. A control equation is introduced, in which the control quantity is the control torque of each wheel. By applying different control torques to different wheels, the vehicle state information can be controlled. The energy consumption per unit time of each wheel motor is constructed and the total energy consumption is derived accordingly. Based on the equivalent low-dimensional vehicle state vector, the reference vehicle state vector, the control torque and the total energy consumption, a cost function is constructed to achieve a multi-objective unified optimization of path tracking accuracy and energy minimization. Based on the current vehicle state information and control torque, a discrete dynamics model of the vehicle is constructed to describe the vehicle state information at the next moment. Based on the discrete dynamics model of the vehicle, a model predictive control problem is constructed, taking into account multiple constraints. The model predictive control problem is solved offline by dividing and optimizing the multi-parameter region, generating a mapping relationship between the vehicle state information space and the optimal torque control command, and deploying it to the controller in the form of a lookup table; Real-time vehicle status information is acquired and used as input for table lookup to retrieve the optimal torque distribution result.

2. The energy-saving torque control method for a five-axis transport robot based on dimension-reduced display MPC according to claim 1, characterized in that, Construct the energy consumption per unit time of each wheel motor and derive the total energy consumption accordingly, including: Construct energy consumption models for each wheel motor, and determine its energy consumption per unit time. for: ； in, Indicates time k wheels wheel speed, Indicates time k wheels The control torque, For the first Efficiency function of an electric motor under different operating conditions; According to the above formula, it is defined at time... Total energy consumption for: ； Where n represents the number of motors.

3. The energy-saving torque control method for a five-axis transport robot based on dimension-reduced display MPC according to claim 2, characterized in that, Based on the equivalent low-dimensional vehicle state vector, the reference vehicle state vector, the control torque, and the total energy consumption, a cost function J is constructed to achieve a unified multi-objective optimization that minimizes path tracking accuracy and energy, including: ； Where Q and R are the weight matrices of the state error and the control input, respectively; For a moment k The reference vehicle state vector; For a moment k The actual state vector, that is, the equivalent low-dimensional vehicle state vector obtained after state dimensionality reduction; N p The length of the time range to be calculated.

4. The energy-saving torque control method for a five-axis transport robot based on dimension-reduced display MPC according to claim 3, characterized in that, Multiple constraints include upper and lower limits of control inputs, vehicle state boundaries, tire adhesion limits, and motor efficiency operating range constraints.

5. The energy-saving torque control method for a five-axis transport robot based on dimension-reduced display MPC according to claim 1, characterized in that, Vehicle status information includes longitudinal speed, lateral speed, yaw rate, and wheel speeds.

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