Intelligent stable control method and system for bionic transport robot

Abstract

The present invention discloses an intelligent stable control method and system for a bionic transport robot. By implementing model predictive control and adaptive control strategies, the system can adjust its control parameters in real time to adapt to changing environmental and load conditions, thereby calculating the driving torque based on sensor data of the weight, center of mass and inclination of the bionic transport robot and combining the dynamic characteristics of the bionic transport robot, adjusting the proportional valve to achieve torque output to the hydraulic motor, and ensuring rapid response and high operating accuracy through dynamic adjustment of real-time control system parameters, thereby greatly improving the robot's work efficiency and reducing safety accidents caused by erroneous operations.

Description

Technical Field

[0001] The present invention relates to the technical field of robot equipment, and in particular to an intelligent stable control method and system of a bionic transport robot. Background Art

[0002] The intelligent and stable control system of the bionic transport robot mainly relies on the high integration of multiple sensors, control algorithms and actuators. Traditional control systems usually adopt control strategies with fixed parameters, such as PID control, which are not easy to adapt to rapid changes in the environment or load after the parameters are set. Although this method performs well in a stable environment, its performance is often limited when faced with dynamically changing actual application scenarios, such as loads of different weights and changing terrain conditions. For example, when the robot transitions from flat ground to a slope, the traditional control system may not be able to adjust the output quickly, resulting in unstable robot motion or slow response.

[0003] In addition, traditional systems also have deficiencies in multi-sensor data processing and fusion. The data from a single sensor often cannot provide enough information to fully evaluate and control the robot's state, resulting in insufficient accuracy and reliability of control decisions. These limitations are particularly evident in demanding industrial applications and complex operating environments, which may increase machine failure rates and reduce work efficiency and safety.

[0004] Chinese patent document CN113204892B discloses a "center of mass trajectory generation method, device, computer-readable storage medium and robot". The method determines the actual center of mass posture, left foot posture and right foot posture of the robot; determines the first posture tracking vector of the robot according to the actual center of mass posture and the left foot posture; determines the second posture tracking vector of the robot according to the actual center of mass posture and the right foot posture; controls the expected center of mass posture of the robot to alternately track the left foot posture and the right foot posture according to the first posture tracking vector and the second posture tracking vector, so as to generate the expected center of mass trajectory of the robot. The above technical solution makes it difficult to adjust the control parameters in time to adapt to the changing environment and load conditions. Summary of the invention

[0005] The present invention mainly solves the technical problem that the original technical solution is difficult to adjust the control parameters in time to adapt to the constantly changing environment and load conditions, and provides an intelligent stable control method and system for a bionic transport robot. By implementing model predictive control and adaptive control strategies, the system can adjust its control parameters in real time to adapt to the constantly changing environment and load conditions, thereby calculating the driving torque based on the sensor data of the weight, center of mass and inclination of the bionic transport robot and combining the dynamic characteristics of the bionic transport robot, and adjusting the proportional valve to achieve the torque output to the hydraulic motor. By dynamically adjusting the system parameters in real time, rapid response and high operating accuracy are guaranteed, thereby greatly improving the robot's work efficiency and reducing safety accidents caused by incorrect operations.

[0006] The above technical problem of the present invention is mainly solved by the following technical solution: The present invention comprises the following steps:

[0007] S1 senses the state of the bionic transport robot and obtains the current state quantity of the bionic transport robot;

[0008] S2 calculates the driving torque based on the dynamic characteristics of the bionic transport robot, predicts the future system output and state based on the current state and future control, achieves optimal control by rolling solution of the constrained optimization problem, and adjusts the control input according to the control error;

[0009] S3 controls the torque output according to the driving torque, and drives the bionic transport robot to complete the predetermined movement.

[0010] Preferably, the current state quantity of the bionic transport robot in step S1 includes the center of mass position of the bionic transport robot and the orientation and inclination of the bionic transport robot in three-dimensional space, and also includes the moving speed, rotation speed and actual load of the bionic transport robot on the ground.

[0011] Preferably, the step S2 includes continuously optimizing and solving the control process, that is, at each moment, according to the state quantity error at the current moment, solving the quadratic programming problem, obtaining the optimal solution of the control quantity when the error between the system output and the reference quantity is minimized, and repeatedly performing the optimization process online.

[0012] Preferably, the step S2 includes that each sampling moment is corrected by feedback, that is, the prediction result is corrected by using the actual output, and then the next optimization process is carried out.

[0013] Preferably, the model predicted in step S2 is represented as a linear state space model assuming a dynamic model of a bionic transport robot:

[0014]

[0015] in, is the state vector at time k, is the control input, A and B are the state transfer matrix and control input matrix of the system respectively;

[0016] The objective function and constraints are defined as:

[0017]

[0018] in, and are the reference state and reference control input respectively, Q and R are weight matrices, and N is the prediction horizon;

[0019] The constraints controlling the input are expressed as:

[0020]

[0021] Among them, u is the control input.

[0022] Preferably, the control error in step S2 is controlled by PID control, and the control law is expressed as:

[0023]

[0024] in, is the control error, , and are the proportional, integral and derivative gains respectively.

[0025] Preferably, the control model combining the predicted model and PID control is expressed as:

[0026]

[0027] in, is the main control signal calculated based on the predictive control module, and It is a PID control signal used for fine adjustment.

[0028] An intelligent stable control system for a bionic transport robot includes a sensor unit, a control unit and an execution unit, wherein the control unit includes a model prediction control module and a PID control module. The sensor unit senses the state of the bionic transport robot in real time, and obtains sensor data of the weight, center of mass and inclination of the bionic transport robot. The control unit designs a corresponding module to calculate the driving torque in combination with the dynamic characteristics of the bionic transport robot, and then sends a control signal to the execution unit, which realizes the torque output of the hydraulic motor by adjusting the proportional valve. The model prediction control module predicts the future system output and state quantity based on the current state quantity and future control quantity of the system, and realizes the optimal control algorithm by rolling solution of the constrained optimization problem, including a prediction model, a rolling optimization module and a feedback correction module; the PID control module adjusts the control input according to the control error, that is, the difference between the expected value and the actual value.

[0029] Preferably, the sensor unit includes a load detection module and a center of mass monitoring module, and also includes a posture monitoring module and a speed monitoring module.

[0030] The load detection module measures the pressure change on the bionic transport robot through a load cell installed on the chassis or the load-bearing structure of the bionic transport robot, and then calculates the actual load;

[0031] The center of mass monitoring module calculates the center of mass position of the bionic transport robot by providing data on the bionic transport robot's posture, acceleration and angular velocity through an integrated accelerometer and gyroscope, the IMU;

[0032] The attitude monitoring module monitors the orientation and inclination of the bionic transport robot in three-dimensional space through the gyroscope and magnetometer in the IMU. The gyroscope measures the angular velocity around three axes, and the magnetometer provides information about the earth's magnetic field to determine the direction of the bionic transport robot relative to the earth.

[0033] The speed monitoring module is used to monitor the linear velocity and angular velocity, which correspond to the moving speed and rotation speed of the bionic transport robot on the ground respectively, and are monitored by encoders, wheel speed sensors or visual odometers.

[0034] Preferably, the execution unit is a control system that uses hydraulic fluid as an energy transfer medium, including a hydraulic pump and a proportional valve, and also includes components of a hydraulic motor and a controller.

[0035] The beneficial effects of the present invention are as follows: by implementing model predictive control and adaptive control strategies, the system can adjust its control parameters in real time to adapt to the ever-changing environment and load conditions, thereby calculating the driving torque based on the sensor data of the weight, center of mass and inclination of the bionic transport robot, combined with the dynamic characteristics of the bionic transport robot, and adjusting the proportional valve to achieve the torque output to the hydraulic motor. By dynamically adjusting the system parameters in real time, rapid response and high operating accuracy are guaranteed, thereby greatly improving the robot's work efficiency and reducing safety accidents caused by incorrect operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of the present invention.

[0037] Figure 2 It is a principle connection structure diagram of the present invention.

[0038] Figure 3 It is a structural schematic diagram of a sensor unit of the present invention.

[0039] Figure 4 It is a structural schematic diagram of an intelligent stable electrical module of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the technical solution of the present invention is further described in detail below through embodiments and in combination with the accompanying drawings. It should be understood that the specific implementation method described here is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0041] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0042] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0043] Embodiment: This embodiment is an intelligent stable control method and system for a bionic transport robot, such as Figure 1 , Figure 2 , Figure 3 An intelligent stable control method for a bionic transport robot comprises the following steps:

[0044] S1 senses the state of the bionic transport robot and obtains the current state quantity of the bionic transport robot. The current state quantity of the bionic transport robot includes the center of mass position of the bionic transport robot and the orientation and inclination of the bionic transport robot in three-dimensional space, as well as the moving speed, rotation speed and actual load of the bionic transport robot on the ground.

[0045] S2 calculates the driving torque based on the dynamic characteristics of the bionic transport robot, and predicts the future system output and state based on the current state and future control. The predicted model is expressed as a linear state space model assuming the dynamic model of the bionic transport robot:

[0046]

[0047] in, is the state vector at time k, is the control input, A and B are the state transfer matrix and control input matrix of the system respectively;

[0048] The objective function and constraints are defined as:

[0049]

[0050] in, and are the reference state and reference control input respectively, Q and R are weight matrices, and N is the prediction horizon;

[0051] The constraints controlling the input are expressed as:

[0052]

[0053] Among them, u is the control input.

[0054] The optimal control is achieved by rolling solution of the constrained optimization problem, and the control input is adjusted according to the control error, that is, the difference between the expected value and the actual value. The control error is controlled by PID, and the control law is expressed as:

[0055]

[0056] in, is the control error, , and are the proportional, integral and derivative gains respectively.

[0057] The control process is continuously optimized and solved, that is, at each moment, according to the state quantity error at the current moment, the quadratic programming problem is solved to obtain the optimal solution of the control quantity when the error between the system output and the reference quantity is minimized, and the optimization process is repeated online.

[0058] Each sampling moment will be corrected by feedback, that is, the actual output will be used to correct the prediction result before the next optimization process.

[0059] The control model combining the prediction model and PID control is expressed as:

[0060]

[0061] in, is the main control signal calculated based on the predictive control module, and It is a PID control signal used for fine adjustment.

[0062] S3 controls the torque output according to the driving torque, and drives the bionic transport robot to complete the predetermined movement.

[0063] An intelligent stable control system for a bionic transport robot includes a sensor unit, a control unit and an execution unit. The sensor unit senses the state of the bionic transport robot in real time, and the control unit designs a corresponding module to calculate the driving torque based on the sensor data of the weight, center of mass and inclination of the bionic transport robot in combination with the dynamic characteristics of the bionic transport robot, and then sends a control signal to the execution unit, which realizes the torque output of the hydraulic motor by adjusting the proportional valve.

[0064] The control unit includes a model prediction control module and a PID control module; the model prediction control module predicts the future system output and state quantity based on the current state quantity and future control quantity of the system, and implements the optimal control algorithm by rolling solution of the constrained optimization problem, including a prediction model, a rolling optimization module and a feedback correction module; the PID control module adjusts the control input according to the control error, that is, the difference between the expected value and the actual value.

[0065] The sensor unit includes a load detection module and a center of mass monitoring module, and also includes a posture monitoring module and a speed monitoring module.

[0066] The load detection module measures the pressure change on the bionic transport robot through a load cell installed on the chassis or the load-bearing structure of the bionic transport robot, and then calculates the actual load;

[0067] The center of mass monitoring module calculates the center of mass position of the bionic transport robot by providing data on the bionic transport robot's posture, acceleration and angular velocity through an integrated accelerometer and gyroscope, the IMU;

[0068] The attitude monitoring module monitors the orientation and inclination of the bionic transport robot in three-dimensional space through the gyroscope and magnetometer in the IMU. The gyroscope measures the angular velocity around three axes, and the magnetometer provides information about the earth's magnetic field to determine the direction of the bionic transport robot relative to the earth.

[0069] The speed monitoring module is used to monitor the linear velocity and angular velocity, which correspond to the moving speed and rotation speed of the bionic transport robot on the ground respectively, and are monitored by encoders, wheel speed sensors or visual odometers.

[0070] The execution unit is a control system that uses hydraulic fluid as the energy transmission medium, including a hydraulic pump and a proportional valve, as well as components of a hydraulic motor and a controller.

[0071] The intelligent stability control system also includes an adaptive control module, which specifically includes: load weight adaptive control; ground slope adaptive control; adaptive model predictive control adjustment; multi-sensor data fusion; real-time control system parameter dynamic adjustment.

[0072] In the adaptive control module, for changes in load weight, the controller adjusts its parameters in real time based on load weight adaptive control to cope with weight increases and decreases to ensure stability. The following is the adaptive PID control formula:

[0073]

[0074] in is a function that adjusts the PID gain according to the load weight W, assuming that:

[0075]

[0076]

[0077]

[0078]

[0079] The intelligent stability control system also includes a safety protection module, which is provided with a limit switch, an overload protection device, a collision detection and obstacle avoidance system, temperature monitoring, voltage and current monitoring, software monitoring and fault diagnosis.

[0080] The intelligent stability control system also includes an intelligent stability electrical module, the architecture of which is divided into a perception planning layer and an execution layer:

[0081] The perception planning layer includes an unmanned driving domain controller, a lidar sensor, and USERCAN;

[0082] The execution layer controls the drive-by-wire, brake-by-wire and steering-by-wire through the vehicle control unit, and then controls the corresponding mechanical components to achieve precise control of the bionic transport robot.

[0083] Example:

[0084] See also Figure 1 An intelligent stable control system for a bionic transport robot includes a sensor unit, a control unit and an execution unit. The sensor unit senses the state of the bionic transport robot in real time. Based on the sensor data of the weight, center of mass and inclination of the bionic transport robot, the control unit designs a corresponding module to calculate the driving torque in combination with the dynamic characteristics of the bionic transport robot, and then sends the control signal to the execution unit. The execution unit realizes the torque output to the hydraulic motor by adjusting the proportional valve.

[0085] The sensor unit includes a load detection module, a center of mass monitoring module, a posture monitoring module and a speed monitoring module.

[0086] See also Figure 2 , in the sensor unit:

[0087] The load detection module measures the pressure change on the bionic transport robot through a weighing sensor installed on the chassis or the bearing structure of the bionic transport robot, and then calculates the actual load.

[0088] Load cells can be piezoelectric, strain gauge or piezoresistive. Load monitoring helps prevent overloading and ensures that the bionic transport robot operates within a safe load range. It can also be used to optimize load distribution. The following mechanisms are used:

[0089] Load cell: High-precision load cells are installed at the key supporting points of the bionic transport robot to measure the pressure at each supporting point.

[0090] Data fusion: By analyzing data from multiple sensors, the load capacity of the entire bionic transport robot can be estimated more accurately.

[0091] Dynamic adjustment: The load data can be used to dynamically adjust the motion parameters of the bionic transport robot, such as acceleration and speed, to ensure stable operation.

[0092] The center of mass monitoring module, center of mass position monitoring is very important for maintaining the stability of the bionic transport robot, especially when performing dynamic operations (such as turning, accelerating or climbing). This is usually achieved through integrated accelerometers and gyroscopes. IMU can provide data about the posture, acceleration and angular velocity of the bionic transport robot, thereby helping to calculate the center of mass position of the bionic transport robot. Center of mass monitoring helps to adjust the balance of the bionic transport robot during movement to avoid tipping over or loss of control. The following mechanisms are specifically adopted:

[0093] Pressure distribution analysis: By analyzing the data from the load cell, the center of mass position of the bionic transport robot can be estimated.

[0094] Multi-dimensional sensors: Use multi-dimensional force sensors to measure forces in multiple directions to more accurately determine the location of the center of mass.

[0095] Real-time feedback: The center of mass position data is fed back to the control system to adjust the posture and motion strategy of the bionic transport robot in real time.

[0096] The attitude monitoring module includes monitoring the orientation and inclination of the bionic transport robot in three-dimensional space. This is mainly accomplished by the gyroscope and magnetometer in the IMU. The gyroscope can measure the angular velocity around three axes, while the magnetometer can provide information about the earth's magnetic field to help determine the direction of the bionic transport robot relative to the earth. Attitude monitoring helps the bionic transport robot maintain stability on uneven terrain, and also supports navigation and positioning functions. The following mechanisms are specifically adopted:

[0097] IMU sensor: An IMU sensor that integrates an accelerometer, gyroscope, and magnetometer is used to monitor the posture of the bionic transport robot.

[0098] Inertial navigation: By analyzing IMU data, inertial navigation and attitude stabilization of the bionic transport robot can be achieved.

[0099] Calibration and compensation: Regularly calibrate the IMU sensor and compensate for system errors to improve the accuracy of attitude monitoring.

[0100] The speed monitoring module includes two aspects: linear speed and angular speed, which correspond to the moving speed and rotation speed of the bionic transport robot on the ground. This is usually achieved through encoders, wheel speed sensors or visual odometers. Speed ​​monitoring helps control the movement of the bionic transport robot to ensure that it travels at a predetermined speed and avoids collision or loss of control. The following mechanisms are specifically adopted:

[0101] Encoder: Install an encoder on the drive wheel or joint to measure linear and angular velocity.

[0102] Slip detection: By monitoring the changes in the encoder signal, the slip or skidding of the bionic transport robot can be detected.

[0103] Multi-sensor fusion: Combining data from the IMU and other sensors can more accurately estimate the speed and direction of the bionic transport robot.

[0104] The control unit includes a model predictive control (MPC) module and a PID control module.

[0105] The model predictive control (MPC) module predicts the future system output and state based on the current state and future control of the system, and implements the optimal control algorithm by rolling solution of the constrained optimization problem, including a prediction model, a rolling optimization module and a feedback correction module.

[0106] In this embodiment, in the model predictive control module:

[0107] The prediction model is expressed as a linear state space model assuming the dynamics model of the bionic transport robot:

[0108]

[0109] in, is the state vector at time k, is the control input, A and B are the state transfer matrix and control input matrix of the system respectively;

[0110]

[0111] in, and are the reference state and reference control input respectively, Q and R are weight matrices, and N is the prediction horizon;

[0112] The constraints controlling the input are expressed as:

[0113]

[0114] Among them, u is the control input.

[0115] In this embodiment, in the model predictive control module:

[0116] The rolling optimization module needs to be continuously optimized and solved during the control process, that is, at each moment, the controller will solve the quadratic programming problem according to the state quantity error at the current moment to obtain the optimal solution of the control quantity when the error between the system output and the reference quantity is minimized. The optimization process is repeatedly performed online;

[0117] In the feedback correction module, there will be deviations between the model prediction results and the ideal state due to factors such as model mismatch and environmental interference during the model predictive control process. Therefore, in order to prevent such phenomena from occurring, the controller will correct the system by means of feedback at each sampling moment, that is, use the actual output of the system to correct the model prediction results, and then proceed to the next optimization process.

[0118] In this embodiment, the model predictive control principle is: when the system is at the current moment (moment k), the model predictive controller can obtain a section of system output in the future prediction time domain based on the current state quantity measurement value and the control quantity measurement value combined with the prediction model, and then obtain a series of control sequences in the control time domain by optimizing and solving the constrained objective function. The controller uses the first element of the control sequence as the actual control quantity of the system, inputs it into the controlled object, and then controls the controlled object to the next moment (moment k+1), and repeats the above process to complete the optimization problem of the constrained objective function in a rolling manner, thereby realizing the control of the research object.

[0119] The PID control module adjusts the control input according to the control error, that is, the difference between the expected value and the actual value.

[0120] In this embodiment, the PID control module has a built-in PID control model. For PID control, the control law is expressed as:

[0121]

[0122] in, is the control error, , and are the proportional, integral and derivative gains respectively.

[0123] In this embodiment, in the control unit, the prediction control module is used for the main control strategy, and the PID control module is used to adjust small errors and fast response. The control model combining the prediction control module and the PID control module is expressed as:

[0124]

[0125] in, is the main control signal calculated based on the predictive control module, and It is a PID control signal for fine adjustment. Based on MPC, PID control provides a fast-response control mechanism that can instantly correct system deviations and ensure the stability of the bionic transport robot under instantaneous disturbances. The PID controller achieves fast response and elimination of system deviations by adjusting the gains of the proportional, integral and differential terms, and is an important supplement to the MPC control strategy.

[0126] In this embodiment, the execution unit is a hydraulic servo control system, which is a control system that uses hydraulic fluid as an energy transfer medium, and includes components of a hydraulic pump, a proportional valve, a hydraulic motor, a sensor, and a controller; it is responsible for converting the control signal into actual mechanical torque to drive the bionic transport robot to complete the predetermined movement.

[0127] In the bionic transport robot, the hydraulic servo system is responsible for converting the control signal into actual mechanical torque to drive the bionic transport robot to complete the predetermined movement. In the application of bionic transport robots, the hydraulic servo control system is usually used to achieve high-precision and high-force motion control.

[0128] The proportional valve is a key component in the hydraulic servo control system. It can adjust the flow and pressure of the hydraulic fluid to the hydraulic motor according to the control signal, thereby achieving the torque output of the hydraulic motor. By accurately controlling the opening of the proportional valve, the precise torque control of the hydraulic motor can be achieved, thereby achieving precise motion control of the bionic transport robot.

[0129] The control signals generated by the MPC and PID control algorithms are fed into the hydraulic servo control system. The pressure and flow of the hydraulic fluid are controlled through the precise adjustment of the proportional valve, thereby adjusting the speed and torque output of the hydraulic motor. This process requires a high degree of accuracy and response speed to ensure the stability and accuracy of the bionic transport robot when performing complex movements.

[0130] The basic steps of the MPC algorithm are as follows: First, establish a dynamic model of the system. For a hydraulic servo control system, the dynamic model of the system can be established using hydraulic transmission theory and fluid mechanics principles. Second, based on the prediction model, predict the future behavior of the system. This can be achieved by solving the difference equations numerically or using an optimization algorithm. Then, the optimization algorithm is used to solve the optimal control strategy. The optimization goal can be to minimize error, minimize energy consumption, or maximize performance, etc. Finally, the optimal control strategy is converted into an actual control signal and applied to the system to achieve control of the system.

[0131] In the MPC control algorithm, the PID controller is often used as part of the prediction model to make local adjustments based on the current state of the system. The input of the PID controller is the current error of the system, and the output is the adjustment amount of the controller. The PID controller consists of three parts: the proportional part, the integral part, and the differential part. The proportional part generates the control amount based on the current error size, the integral part generates the control amount based on the past error accumulation, and the differential part generates the control amount based on the rate of change of the error. The output of the PID controller is the sum of these three parts. The PID controller controls the system by adjusting the parameters of these three parts.

[0132] For hydraulic servo control systems, the application of MPC+PID control algorithm can be carried out as follows: First, establish the dynamic model of the hydraulic servo control system. According to the hydraulic transmission theory and fluid mechanics principles, the mathematical model of the hydraulic servo system can be established. Secondly, use the MPC algorithm to predict the future behavior of the system. According to the dynamic model and current state of the system, the behavior of the system in the future can be predicted. Then, the optimization algorithm is used to solve the optimal control strategy. According to the predicted results and optimization goals, the optimal control strategy can be determined. Finally, the optimal control strategy is converted into an actual control signal and sent to the hydraulic servo control system. The torque output of the hydraulic motor is achieved by adjusting the proportional valve.

[0133] The specific implementation method is as follows:

[0134] First, establish the mathematical model of the system, including the system dynamics model, the hydraulic servo system model, etc.;

[0135] Then, the MPC algorithm is used to predict the system behavior in the future and design the optimal control input;

[0136] Next, the PID controller is used to adjust the difference between the actual system behavior and the expected behavior to reduce the error and maintain the stability of the system;

[0137] Finally, the control signals generated by the MPC and PID controllers are sent to the hydraulic servo control system, and the torque output of the hydraulic motor is achieved by adjusting the proportional valve.

[0138] Through reasonable controller design, parameter adjustment and system configuration, the combination of MPC+PID control algorithm and hydraulic servo control system can achieve stable operation and precise control of the bionic transport robot under various complex working conditions.

[0139] In this embodiment, the intelligent stability control system further includes an adaptive control module, which specifically includes:

[0140] Load weight adaptive control;

[0141] When the bionic transport robot carries goods of different weights, its dynamic characteristics will change significantly, which will directly affect the stability and motion control performance of the bionic transport robot. The adaptive control strategy can dynamically adjust the control parameters by estimating the load mass online. When the load increase is detected, the adaptive control algorithm will increase the proportional gain of the PID controller accordingly to enhance the response speed of the system; at the same time, the integral and differential gains are appropriately adjusted to suppress overshoot and oscillation, ensuring the stable operation of the bionic transport robot under load changes. To compensate for the impact caused by load changes.

[0142] The specific implementation method is as follows: use a weighing sensor to measure the load weight in real time; adjust the center of mass position and control parameters of the bionic transport robot based on the measurement results; use a model predictive control algorithm to predict future system behavior based on the current load weight and center of mass position, and design the optimal control input; use a PID control algorithm to adjust the control input based on the difference between the actual system behavior and the expected behavior to reduce errors and maintain system stability.

[0143] Adaptive ground slope control;

[0144] Changes in ground slope will also affect the stability of the bionic transport robot. On slopes or rugged ground, the bionic transport robot needs to adjust its posture and torque output to maintain stability and normal driving. The adaptive control strategy monitors the ground slope in real time and adjusts the torque distribution and steering angle of the drive wheels to maintain the stable driving of the bionic transport robot on the slope. When the bionic transport robot detects that it is going uphill, the adaptive control algorithm will increase the torque of the rear wheels to overcome the tendency of gravity to slide down, and adjust the steering angle of the front wheels to ensure that the bionic transport robot moves steadily along the target path. When going downhill, the rear wheel torque will be reduced and the front wheel braking force will be increased to prevent uncontrolled sliding.

[0145] The specific implementation method is as follows: use the inclinometer to measure the ground slope in real time; adjust the posture and torque output of the bionic transport robot according to the measurement results; use the model predictive control algorithm to predict the future system behavior and design the optimal control input according to the current ground slope and the bionic transport robot posture; use the PID control algorithm to adjust the control input according to the difference between the actual system behavior and the expected behavior to reduce the error and maintain the stability of the system.

[0146] Multi-sensor data fusion;

[0147] In order to improve the adaptive ability of the intelligent stabilization system, multi-sensor data fusion technology can be used. By fusing multiple sensor data such as weighing sensors, inclination sensors, acceleration sensors, etc., the state of the bionic transport robot can be estimated more accurately and the robustness of the control strategy can be improved.

[0148] Sensor fusion: Use multiple sensors (such as IMU, torque sensor, GPS, etc.) to obtain information such as the posture, speed, position, and environmental parameters such as load weight and ground slope of the bionic transport robot.

[0149] Real-time data processing: Through high-speed processors, sensor data is analyzed in real time to quickly identify environmental changes and provide decision-making basis for adaptive control.

[0150] Dynamic parameter adjustment: Design adaptive control algorithms to adjust control parameters such as PID gain, torque distribution ratio, etc. in real time according to environmental changes to optimize control performance.

[0151] Model prediction and learning: Model predictive control combined with machine learning technology is used to improve the predictability and adaptability of control strategies, enabling bionic transport robots to respond more accurately in unknown or changing environments.

[0152] Real-time control system parameters are dynamically adjusted.

[0153] In order to achieve real-time control, the intelligent stabilization system needs to have high-speed data processing and transmission capabilities. High-performance microprocessors and real-time operating systems, as well as high-speed communication interfaces, can be used to ensure the real-time and effectiveness of the control strategy.

[0154] In short, the intelligent stabilization system should have adaptive capabilities and be able to adjust the control strategy in real time according to environmental changes to maintain the stability of the bionic transport robot. By adopting adaptive control strategies, multi-sensor data fusion technology and real-time control systems, precise control of the bionic transport robot can be achieved and its stability and reliability in complex environments can be improved.

[0155] In the adaptive control module, for changes in load weight, the controller adjusts its parameters in real time based on load weight adaptive control to cope with weight increase or decrease to ensure stability. The following is the adaptive PID control formula:

[0156]

[0157] in is a function that adjusts the PID gain according to the load weight W, assuming that:

[0158]

[0159]

[0160]

[0161]

[0162] In this embodiment, the intelligent stability control system also includes a safety protection module, which is provided with a limit switch, an overload protection device, a collision detection and obstacle avoidance system, temperature monitoring, voltage and current monitoring, software monitoring and fault diagnosis.

[0163] (1) Limit switch: A safety device used to detect and limit the range of mechanical motion. In a bionic transport robot, a limit switch can be installed on the moving parts. When the bionic transport robot approaches the limit of its range of motion, the limit switch will be triggered, thereby cutting off the power source and immediately stopping the movement of the relevant parts to prevent collision or damage.

[0164] (2) Overload protection device: It is mainly used to monitor and control the working status of the motor. When the motor load exceeds the set value, the power supply is automatically cut off to prevent the motor from being damaged due to overheating or overcurrent. This is usually achieved by installing pressure sensors or force sensors, which can monitor the load of the bionic transport robot in real time. Once it is detected that the load exceeds the set safety threshold, the overload protection device will immediately cut off the power source to prevent further damage.

[0165] (3) Collision detection and obstacle avoidance system: By integrating sensors (such as lidar, ultrasonic sensors, and visual sensors), the bionic transport robot can perceive the surrounding environment and identify obstacles in real time. Once a potential collision risk is detected, the system will quickly adjust the path or slow down and stop to avoid contact with the obstacle, thereby protecting the safety of the bionic transport robot itself and the people around it.

[0166] In addition, an emergency stop button is set on the operation interface of the bionic transport robot. When encountering an emergency, the operator can immediately press the button to immediately stop all actions of the bionic transport robot. This button should be easily accessible and clearly marked to ensure that it can be operated quickly in an emergency.

[0167] (4) Temperature monitoring:

[0168] During the movement of the bionic transport robot, the motor and hydraulic system may generate a lot of heat. Temperature sensors can be used to monitor the temperature of these key components to prevent overheating. If the temperature exceeds the safe range, the system will automatically reduce power or stop operation to prevent damage.

[0169] (5) Voltage and current monitoring:

[0170] Abnormal fluctuations in voltage and current may cause malfunctions in the bionic transport robot system. By installing voltage and current sensors, the working status of the power supply and motor can be monitored in real time. Once an abnormality is detected, the system will automatically take measures such as power off or power reduction to protect the bionic transport robot from damage.

[0171] (6) Software monitoring and fault diagnosis:

[0172] The software part of the intelligent stabilization system should include monitoring and fault diagnosis functions. This includes real-time monitoring of key system parameters such as speed, acceleration, torque, etc., and the ability to identify any abnormal behavior. If a potential problem is detected, the system should be able to automatically adjust the operation or notify the operator to take manual intervention. The bionic transport robot is equipped with a self-diagnosis function that can regularly check the status of its own hardware and software. Once an abnormality is found, protective measures are immediately initiated, such as automatic shutdown, sending alarm information, etc., to ensure that the problem is handled in a timely manner and prevent the fault from further expanding.

[0173] See also Figure 4 , in this embodiment, the intelligent stability control system further includes an intelligent stability electrical module, and the architecture of the intelligent stability electrical module is divided into a perception planning layer and an execution layer;

[0174] The perception and planning layer mainly includes autonomous driving domain controllers, sensors such as LiDAR, and USERCAN. The autonomous driving domain controller is the core of the entire system, responsible for processing data from various sensors, performing data fusion and processing, and making decisions, such as determining the driving route and speed. Sensors such as LiDAR are used to obtain information about the surrounding environment, such as the location and distance of obstacles. USERCAN refers to a part of the user interface and communication protocol, which is used for data transmission between different components.

[0175] At the execution layer, the VCU (Vehicle Control Unit) is mainly used to control the drive-by-wire, brake-by-wire and steering-by-wire, and then control the corresponding mechanical components to achieve precise control of the vehicle, including acceleration, braking, steering, etc. The functions that can be realized by the chassis control system are shown in Table 1.

[0176] Chassis control system Control method Key hardware equipment Realizable functions Communication Brake-by-wire system Brake pressure ESC, brake-by-wire assembly Active regulation of brake pressure CAN network communication Steer-by-wire system Target corner Steering-by-wire assembly Active Corner Control CAN network communication Drive-by-wire system Motor torque Electric drive, motor Active regulation of vehicle speed CAN network communication

[0177] Table 1 Chassis control system function table

[0178] In terms of communication control, the three modules of wire control brake, wire control drive and wire control steering all adopt CAN (Controller Area Network) signal communication mode. CAN bus is a protocol widely used in internal communication of vehicles. It can realize high-speed and high-reliability data transmission and ensure accurate information exchange between various control units. Through CAN bus, these three key modules can exchange status information and control instructions in real time, so as to achieve coordinated work and improve the dynamic performance and response speed of the vehicle.

[0179] For the drive-by-wire gear position, the drive-by-wire chassis is designed with a set of DNR (Drive Neutral Reverse) three-level control and safety logic settings. The DNR system allows the driver to control the gear shifting through electrical signals, thereby achieving the vehicle's drive, neutral and reverse gears. This system not only improves driving convenience and operability, but also ensures the safety of the switching process through strict safety logic settings, preventing accidents caused by misoperation of the gear position.

[0180] In order to support the remote control driving function, the wire-controlled chassis is equipped with a self-developed CAN communication remote control. The remote control can remotely control the various functions of the bionic transport robot, including braking, steering and driving, through wireless signals, providing convenience for unmanned driving and remote operation. This remote control driving function can greatly improve operating efficiency and safety in specific environments, such as working in narrow spaces or dangerous areas.

[0181] In terms of power management, through the BMS (Battery Management System), the bionic transport robot can detect voltage and current in real time, display the battery status, and optimize the power output. BMS can not only protect the power battery from overcharging and over-discharging, but also monitor the battery health and predict the battery life, providing intelligent support for the vehicle's energy management.

[0182] The chassis is equipped with a vehicle self-check function, including power-on self-check and driving self-check. During the startup phase, the chassis will self-check the key wire-controlled components of the bionic transport robot to ensure that all systems can work normally. During operation, the system will detect the working status of the chassis components in real time and feedback driving data so that the autonomous driving system can timely understand the operating status of the bionic transport robot and take corresponding measures to ensure the safe operation of the vehicle.

[0183] The beneficial effects of the present invention are:

[0184] The intelligent and stable control system of the bionic transport robot can significantly improve the performance and safety of the bionic transport robot in complex environments. By implementing model predictive control and adaptive control strategies, the system can adjust its control parameters in real time to adapt to changing environmental and load conditions. The application of multi-sensor data fusion technology enables the system to estimate the robot's state more accurately and enhance the robustness of the control strategy. In addition, through the dynamic adjustment of real-time control system parameters, fast response and high operational accuracy are guaranteed, thereby greatly improving the robot's work efficiency and reducing safety accidents caused by incorrect operations. These improvements provide bionic transport robots with significant competitive advantages in industrial automation and complex task execution.

[0185] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0186] The specific embodiments described herein are merely examples of the spirit of the present invention. The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that technicians in the technical field to which the present invention belongs can make various modifications or supplements to the described specific embodiments or replace them in a similar manner, but will not deviate from the spirit of the present invention or exceed the scope defined in the attached claims. For ordinary technicians in this field, multiple deformations and improvements can also be made without departing from the concept of the present invention. Therefore, the scope of protection of the patent of the present invention shall be based on the attached claims.

Claims

1. An intelligent stable control method for a bionic transport robot, characterized in that: The following steps are involved: S1 senses the state of the bionic transport robot and obtains the current state quantity of the bionic transport robot; S2 calculates the driving torque based on the dynamic characteristics of the bionic transport robot and predicts the future system output and state based on the current state and future control. The objective function is: x ref and u ref are the reference state and reference control input, Q and R are weight matrices, and N is the prediction horizon; the constraint on the control input is expressed as: min ≤u i ≤u max , where u i is the control input; the control model combining the prediction model and PID control is expressed as: k =u MPC，k +u PID，k ,u MPC，k is the main control signal calculated based on the predictive control module, and u PID，k It is a PID control signal for fine-tuning. It achieves optimal control by solving the constrained optimization problem in a rolling manner and adjusts the control input according to the control error. S3 controls the torque output according to the driving torque, and drives the bionic transport robot to complete the predetermined movement.

2. The intelligent stable control method of a bionic transport robot according to claim 1, characterized in that: The current state quantity of the bionic transport robot in step S1 includes the center of mass position of the bionic transport robot and the orientation and inclination of the bionic transport robot in three-dimensional space, as well as the moving speed, rotation speed and actual load of the bionic transport robot on the ground.

3. The intelligent stable control method of a bionic transport robot according to claim 1, characterized in that: The step S2 includes continuously optimizing and solving the control process, that is, at each moment, according to the state quantity error at the current moment, solving the quadratic programming problem, obtaining the optimal solution of the control quantity when the error between the system output and the reference quantity is minimized, and repeatedly performing the optimization process online.

4. The intelligent stable control method of a bionic transport robot according to claim 1, characterized in that: The step S2 includes that each sampling moment is corrected by feedback, that is, the prediction result is corrected by using the actual output, and then the next optimization process is carried out.

5. The intelligent stable control method of a bionic transport robot according to claim 1, 3 or 4, characterized in that: The model predicted in step S2 is represented as a linear state space model assuming the dynamics model of the bionic transport robot: x k+1 =Ax k +Bu k Among them, x k is the state vector at time k, u k is the control input, A and B are the state transfer matrix and control input matrix of the system respectively.

6. The intelligent stable control method of a bionic transport robot according to claim 5, characterized in that: In step S2, the control error is controlled by PID control, and the control law is expressed as: Where, e(t) is the control error, K p , K i and K d are the proportional, integral and derivative gains respectively.

7. An intelligent stable control system for a bionic transport robot, applicable to the control method according to claims 1 to 6, characterized in that: It includes a sensor unit, a control unit and an execution unit, wherein the control unit includes a model prediction control module and a PID control module.

8. The intelligent stable control system of a bionic transport robot according to claim 7, characterized in that: The sensor unit includes a load detection module and a center of mass monitoring module, and also includes a posture monitoring module and a speed monitoring module.

9. The intelligent stable control system of a bionic transport robot according to claim 7, characterized in that: The execution unit is a control system that uses hydraulic fluid as an energy transmission medium, and includes a hydraulic pump and a proportional valve, as well as components of a hydraulic motor and a controller.

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