1146 results about "Torque distribution" patented technology

The invention provides a control method based on differential rotation speed torque distribution, which relates to the technical field of intelligent control and comprises the following steps: performing difference calculation on an initial input quantity and a preset rotation speed instruction to generate a rotation speed deviation signal and a deviation change rate, and establishing a dynamic membership function library through a fuzzy controller; mapping the rotating speed deviation and the change rate thereof into a fuzzy language variable, performing reasoning operation according to a preset fuzzy control rule base, and outputting a frequency regulation quantity and a torque compensation coefficient of the frequency converter; and reading the generated torque compensation coefficient and shaft system torque values actually measured by the two torquemeters, calculating a dynamic difference value between the current total load torque and the target torque, establishing a torque distribution weight model according to the transmission characteristics of the differential mechanism, and generating differential torque correction instructions of the two motors in combination with the real-time rotating speed values. The control performance of differential drive is improved.

The invention discloses a braking pressure degradation strategy of an electronic mechanical braking system, and belongs to the technical field of vehicle braking control, and the braking pressure degradation strategy of the electronic mechanical braking system comprises the following steps: S1, multi-sensor fusion is carried out to correct a friction coefficient in real time, and a high-precision gridding pavement model is constructed; s2, performing time sequence prediction on braking torque distribution, dynamically constraining a braking pressure threshold value and performing pre-adjustment; s3, reinforcement learning is carried out to generate a prospective degradation sequence, and thermal coupling and pressure fluctuation control are optimized; s4, dynamically encrypting the hierarchical communication architecture, and ensuring that the instruction is complete and consistent with the time sequence through redundancy switching; s5, generating an adversarial network compensation failure signal, and dynamically adjusting the weight of the sensor to resist interference; s6, triggering a man-machine interaction strategy in a grading manner, and dynamically adjusting a safety threshold in combination with a driving behavior; s7, carrying out simulation verification on the digital twin platform, and carrying out fault injection encryption to update full-life-cycle parameters; the method has the beneficial effects that self-adaptive optimization and hierarchical fault tolerance of the brake pressure are realized, and the system safety and response efficiency are improved.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention discloses a chassis coordination control method and system based on tire side deviation state evaluation, and relates to the technical field of four-wheel steering four-wheel drive vehicle dynamics control. The method comprises the steps that an expected front wheel rotation angle is determined based on an expected steering wheel rotation angle input by a driver, and a front wheel rotation angle is determined; further determining a feed-forward front wheel steering angle, a feed-forward rear wheel steering angle and an additional yaw moment based on feed-forward control and feedback control, determining a first tire slip angle and a second tire slip angle based on a road adhesion coefficient, and further determining a wheel torque control weight, a feedback front wheel steering angle control weight and a feedback rear wheel steering angle control weight; and further determining a driving yaw moment, a feedback front wheel steering angle and a feedback rear wheel steering angle, determining a total longitudinal force based on an expected longitudinal acceleration input by a driver, and performing four-wheel torque distribution based on the total longitudinal force and the driving yaw moment to obtain a four-wheel driving torque. The driving stability of the vehicle under the limiting working condition can be improved.

The invention belongs to the technical field of automation, and discloses an improved multi-humanoid robot distributed load sharing cooperative carrying control method based on model predictive control, and the method comprises the steps: inputting and torque inverse solution; carrying out MPC modeling and degree-of-freedom setting; optimizing a target and a constraint condition; stable contact between the robot and the object in the carrying process is ensured, and special carrying constraints are defined; a comprehensive cost function is formulated, and optimization of the carrying process is achieved. Through the improved MPC method and the torque distribution strategy, the efficient and accurate humanoid robot cooperative carrying control method is provided, coordination and cooperation among the robots can be achieved, accurate control over the torque and stability of the postures of the robots in the carrying process can be guaranteed, and therefore the overall performance of multi-robot cooperative carrying is improved.

The invention relates to the technical field of ship propulsion system monitoring, in particular to a shafting load analysis method based on real-time data monitoring, and the method comprises the following steps: S1, collecting a pressure parameter and a displacement parameter of a shafting; s2, performing timestamp alignment and data frame encapsulation to generate a synchronous data stream; s3, shafting natural vibration noise and real load signals are separated, and shafting real-time torque distribution is calculated; s4, if the torque distribution deviates from the correction threshold value, graded early warning is triggered; and S5, generating an interactive report according to the triggered early warning level and the torque distribution result. According to the invention, through multi-sensor synchronous acquisition, edge calculation data alignment, vibration noise decoupling, dynamic safety threshold adjustment and intelligent fault tracing analysis, high-precision monitoring, abnormity early warning and intelligent maintenance of the shafting load are realized, and the safety and operation and maintenance efficiency of ship operation are improved.

The invention discloses a vehicle stability adaptive control method under low attachment road surface multi-source interference, and belongs to the field of vehicle control, and the method comprises the following steps: S1, multi-source interference perception and attachment coefficient prediction; s2, dynamic correction of tire dynamic parameters: correcting tire cornering stiffness and slippage stiffness on line; s3, multi-source interference collaborative observation and estimation: dynamically estimating an interference source by using an enhanced extended state observer; s4, phase plane stability envelope analysis and weight adjustment: dynamically allocating control priorities; and S5, on the premise of considering an interference source, performing dynamic torque distribution and actuator cooperative control: optimizing four-wheel torque by using a quadratic programming algorithm to obtain a torque distribution result. By adopting the vehicle stability adaptive control method under the low-attached road surface multi-source interference, the problems of insufficient modeling precision, weak interference suppression, target conflict and redundancy deficiency in low-attached road surface vehicle stability control are solved, and the trajectory tracking precision and the yaw stability are remarkably improved.

The invention discloses a high dynamic response control method and system for a linear actuator of a humanoid robot, and the method comprises the steps: collecting the pressure and moment data of a contact point, carrying out the data preprocessing and signal calibration processing, and obtaining the calibration pressure and moment data; based on the calibrated pressure and torque data, performing high-frequency signal extraction to obtain dynamic change characteristics, and performing distribution diagram construction in combination with the pressure and torque data to obtain a pressure distribution diagram and a torque distribution diagram; performing data fusion processing according to the pressure distribution diagram and the torque distribution diagram, generating real-time contact area distribution, and performing dynamic prediction model construction based on the pressure and torque data to obtain a dynamic prediction model; according to the dynamic prediction model, actuator output force and joint angle adjustment amount are calculated, output torque and angle compensation amount are generated, torque distribution optimization and force feedback compensation are carried out, and an optimized final control signal is obtained. According to the method, high-dynamic response control of the controller can be realized.

The invention relates to a speed planning optimization method and system for industrial robot repeated path operation, and the method comprises the steps: carrying out the constraint iterative learning speed planning based on path arc length coordinates, building a precise rigid-flexible coupling dynamic model of a robot, and obtaining the motion constraint condition of the robot; calculating the speed planning motion performance of the robot through the motion performance evaluation function, and updating the parameters of the motion performance evaluation function based on a dynamic linear scaling mechanism; an intelligent iterative learning mechanism based on arc length coordinate parameterization, dynamic constraint on-line satisfaction and provable monotonic convergence performance is realized through an updating law of an iterative learning motion performance evaluation function; and outputting the optimal speed curve of the robot. Track deviation, vibration data and energy consumption characteristics are automatically recorded when a task is executed each time, the root of a problem is autonomously analyzed, key parameters such as an acceleration curve and joint torque distribution are dynamically adjusted, and dependence on manual parameter adjustment in repeated operation is eliminated.

The invention discloses a distributed driving automobile control method. The method comprises the steps that 1, dynamic parameters of an automobile are collected in real time, and the equivalent adhesion coefficient of each wheel is calculated; 2, dividing types and optimizing torque distribution according to pavement adhesion coefficients: respectively adopting priority, uniform and dynamic weight distribution strategies for low, high and asymmetric pavements; 3, establishing a multi-objective optimization model, and solving an optimal torque distribution scheme considering the tire utilization rate and the yaw stability by using a quadratic programming algorithm; 4, compensating a torque error and motor response delay by adopting sliding mode control and a feedforward-feedback mechanism; and 5, the control parameters are dynamically corrected based on errors of the longitudinal acceleration and the yaw velocity, and torque limiting or safety mode switching is triggered. Through multi-strategy collaborative optimization, the stability and driving efficiency of the vehicle under different attachment road surfaces are improved, the tire slipping risk is reduced, and driving safety under extreme working conditions is guaranteed.

The invention relates to the technical field of forklift control, and discloses a refrigeration house unmanned forklift anti-skid control method and system based on multi-sensor fusion, and the method comprises the steps: synchronously collecting wheel angular velocity, vehicle body inertia data, motor current, vibration and acoustic signals through a multi-mode sensor, and carrying out the time alignment and feature extraction; inputting a time-space diagram neural network to estimate the friction coefficient and the optimal slip rate of each wheel in real time; and in combination with model predictive control, four-wheel independent torque distribution is optimized in a rolling manner under the condition that physical constraints are met, so that high-precision tracking and dynamic stability are realized. The system comprises a data acquisition module, a data processing module, a friction estimation module, a torque distribution module and an execution module. The attachment change can be sensed in advance, and the safety, efficiency and operation precision of the unmanned forklift in the wet and slippery refrigeration house environment are improved.

The invention relates to the technical field of electric driving system energy-saving control, and discloses a multi-motor driving system torque optimal distribution method and system, and the method comprises the steps: building a single power unit power loss model of a multi-motor driving system; constructing a fitting straight line segment; constructing a torque-current mapping relation curve; a train speed prediction model, a multi-motor driving system power unit motor rotating speed prediction model, a multi-motor driving system total torque reference value prediction model and a multi-motor driving system power unit power loss prediction model are constructed; establishing a multi-motor driving system energy efficiency evaluation function, a multi-motor driving system stability evaluation function, a multi-motor driving system total torque following evaluation function and a system optimization objective function; designing a learning rate of a control vector; and updating the control vector according to the learning rate, and obtaining an optimized torque distribution coefficient and an optimized torque reference value distributed by each power unit. Therefore, the torque optimization distribution of the multi-motor driving system is realized, and the energy utilization efficiency of the system is improved.

The invention provides a tractor semitrailer full-working-condition active stability control system and method, and belongs to the technical field of vehicle engineering. The system comprises a kinetic model module, a reference quantity determination module and an active stability controller module. The dynamic model module constructs a linear three-degree-of-freedom tractor semitrailer model, a hub motor model and a longitudinal driver model. The reference quantity determination module generates an ideal state value according to the steady state condition of the system and corrects the ideal state value in combination with the road attachment condition. The active stability controller module is divided into three layers, an MIMO-IMFAC controller calculates additional yawing moment, a torque optimization controller optimizes tire load distribution through a quadratic programming algorithm, and a driving / braking torque distribution controller preferentially uses regenerative braking and dynamically adjusts braking torque of the semitrailer according to the yawing moment. By adopting the control system and the control method, the stability and the dynamic response capability of the vehicle under complex working conditions are remarkably improved.

A vehicle drift control method and system, and a vehicle are provided. The vehicle drift control method comprises: in response to a drift operation instruction by a user, obtaining a whole-vehicle required torque and vehicle state parameters; determining a front-axle torque ratio according to the state parameters; determining a front-axle required torque according to the front-axle torque ratio and the whole-vehicle required torque; and performing torque control respectively on a front-axle motor and a rear-axle motor according to the front-axle required torque and the rear-axle required torque, such that the torque distribution between the front and rear axles is more reasonable, and the drift duration and the drift safety are improved.

The invention provides a new energy light truck energy distribution method and system, and the method comprises the steps: obtaining the position and track of a vehicle to obtain the road section data of a current driving road section, carrying out the data preprocessing of the road section data, and carrying out the fusion of the preprocessed data, so as to obtain the current load and driving state of the vehicle; inputting the preprocessed data into an initial energy consumption model, introducing a deep learning algorithm to dynamically adjust model parameters of the initial energy consumption model to obtain an iterated target energy consumption model, and obtaining energy consumption coefficient data according to the target energy consumption model; and a motor torque demand curve is generated in real time according to the current load, the road slope and the predicted endurance mileage, torque distribution data are adjusted in combination with an adaptive algorithm, the adjusted torque distribution data are transmitted into a vehicle power control system to obtain real-time operation data of the vehicle, and then energy distribution is conducted on the new energy light truck. According to the invention, the endurance prediction precision is improved, and the real-time adaptive optimization of torque distribution control is realized.

The invention relates to the technical field of new energy automobile electric control, and discloses a torque compatible intelligent matching system and method for a new energy automobile, and the system comprises a parameter pre-storage module, an EOL configuration module, a dynamic selection module, a data collection module, a torque calculation module, and an execution output module. A driving system configuration identifier is written in through a vehicle production line EOL terminal to realize hardware adaptation selection; an eagle optimization algorithm is innovatively integrated and improved to construct a historical driving database, an output proportion table is periodically adjusted to improve the driving smoothness, and meanwhile a supplier batch compensation mechanism is established to solve the hardware tolerance problem; fault grading processing is achieved through parameter table dual redundancy check, and torque distribution efficiency is continuously optimized in combination with a full-life-cycle closed-loop self-learning module. According to the scheme, the development cost adaptive to different power systems is reduced, and the core pain points of poor multi-source hardware compatibility, weak working condition adaptability and extensive energy efficiency management of the new energy automobile are solved.

The invention discloses an energy management method and system for a plug-in hybrid power vehicle, and the method comprises the steps: obtaining the multi-dimensional data of the vehicle in real time in the vehicle driving process, determining the actual demand power of the vehicle according to the multi-dimensional data, and obtaining the predicted demand power according to the current accelerator opening degree change rate; establishing an optimized objective function composed of fuel economy, battery health degree and dynamic responsiveness, and solving the optimized objective function in a rolling time domain by using a model predictive control algorithm according to the predicted demand power of the vehicle to obtain a torque distribution result of an engine and a motor; and the torque of the engine and the torque of the motor are distributed according to the torque distribution result of the engine and the torque distribution result of the motor so that the vehicle can be controlled. The problem that in the prior art, the energy management effect of the plug-in hybrid electric vehicle is poor is solved.

The invention relates to the technical field of vehicle braking control, and discloses a vehicle braking control system based on a high-speed permanent magnet synchronous motor, which comprises a state sensing module, a braking force calculation module, a braking force control module and a braking force control module, the torque distribution module distributes the total braking torque into a predictable regenerative component and a precisely matched friction component, a deterministic regenerative braking force datum line is established, a braking system is converted into stable output control from passive response energy fluctuation, and linear mapping of pedal travel and deceleration is achieved; and meanwhile, by combining road bumping degree perception and model online correction, the output stability is kept under the complex working condition, and the technical problem that energy recovery and control quality in a hybrid braking system are difficult to consider at the same time is solved.

The invention provides a torque distribution method and device for distributed driving, a vehicle and a medium, and relates to the technical field of vehicles. The method comprises the steps that when the driving mode of the vehicle is an out-of-trap mode, according to the opening degree of an accelerator pedal and state parameters of the whole vehicle, torque distribution is conducted; determining a first target demand torque of a target axle provided with the EDS, a first target torque of each wheel corresponding to the target axle and an allowable maximum driving torque; and when the first wheel of the target axle slips, calculating a second target demand torque of the target axle according to the first target demand torque of the target axle and the allowable maximum driving torque of each wheel corresponding to the target axle. And if the second wheel meets the preset condition, the motor torque capacity of the first wheel is transmitted to the second wheel, and therefore it is guaranteed that power is utilized to the maximum extent.

Aspects of the present invention relate to a method and to a control system for a vehicle, the vehicle comprising an internal combustion engine configured to provide torque to a first axle of the vehicle for generating first axle wheel torque, and an electric machine configured to provide torque to a second axle of the vehicle for generating second axle wheel torque, the method comprising: outputting a torque request for the engine and a torque request for the electric machine, the torque requests having a first ratio dependent on a required torque split between the first axle wheel torque and the second axle wheel torque, wherein received first axle wheel torque and received second axle wheel torque have a second variable ratio dependent on a difference between wheel torque response capabilities of the engine and of the electric machine; determining that a trigger condition is satisfied; and controlling, in dependence on satisfaction of the trigger condition, determination of the torque request for the electric machine such that deviation of the second ratio from the first ratio is inhibited.

The invention provides a vehicle torque distribution method and a double-electric-drive-axle vehicle, and relates to the technical field of new energy automobiles. The method is applied to a vehicle controller of a double-electric-drive-axle vehicle and comprises the steps that actual operation parameters of two motors in the double-electric-drive-axle vehicle are obtained; the total demand torque of the double-electric-drive-axle vehicle is obtained; according to the total demand torque and the actual operation parameters of each motor, the demand power of each motor under the multiple preset distribution coefficients is calculated; determining the corresponding total demand power according to the demand power of each motor under the plurality of preset distribution coefficients; determining a target distribution coefficient from the plurality of preset distribution coefficients according to the total demand power corresponding to the plurality of preset distribution coefficients; and performing torque distribution control on the two motors by adopting the target distribution coefficient according to the total demand torque. The target distribution coefficients of the motors are dynamically determined, then the two motors are controlled, the overall energy efficiency of the vehicle reaches the optimal level, energy efficiency waste is avoided, and the driving experience is improved.

The invention relates to the technical field of driving control, and discloses a somatosensory driving control method and system of a scooter. The method comprises the following steps: acquiring an original somatosensory signal of the scooter; performing resonance amplification and multi-mode frequency mixing on the original somatosensory signal to obtain a somatosensory characteristic signal; establishing a scooter-rider coupling system dynamic model based on the somatosensory characteristic signal, and performing real-time state calculation to obtain a current state parameter and a state deviation value; performing motion sensing body weight distribution change mapping conversion on the current state parameter and the state deviation value to obtain a riding control demand parameter; and calculating a real-time driving control signal sequence according to the riding control demand parameters, and carrying out front and rear wheel motor torque dynamic distribution on the real-time driving control signal sequence to obtain a target motor driving execution instruction set. The problem of delay in traditional somatosensory control is eliminated, and safe and stable motor driving control is realized by combining dynamic torque distribution and a safety monitoring mechanism of road adhesion conditions.

The invention discloses an electric drive multi-axle vehicle torque optimal distribution control method and system, and relates to the technical field of electric drive multi-axle vehicle energy consumption control, and the method comprises the steps: solving the optimal number of drive axles of an electric drive multi-axle vehicle and the drive torque of each drive axle through a first objective function; optimizing the maximum copper loss sequence within the safe temperature rise threshold of the motor by using a second objective function, and further obtaining the maximum torque which can be provided within the safe temperature rise threshold of the motor; according to the distribution strategy, the motor temperature rise rate can be delayed, the efficiency of the electric driving system can be improved, the number of driving shafts and torque distribution on all the driving shafts can be adjusted in real time when the temperature of a certain shaft motor is too high, the motor temperature rise is limited by dynamically adjusting the motor output torque, and therefore the motor temperature of all the shafts is balanced, the economical efficiency of the electric driving system is improved, and the cost is reduced. And the thermal fault probability of the motor is reduced.

The invention discloses a composite braking slip rate control system and method based on a hub motor and an EMB. The method comprises the steps that vehicle information is obtained; calculating a target slip rate according to the vehicle information; according to the target slip rate, calculating a slip rate threshold value for opening and exiting the ABS; according to the vehicle speed, the opening degree of a brake pedal, the actual slip rate and the slip rate threshold value, an ABS start-stop mark is given; according to the vehicle speed, parameters of a slip rate tracking controller are calculated through the attachment coefficient, the slip rate and the slip rate change rate, and then the total target braking torque is calculated; selecting a braking torque distribution mode according to a motion mode switch, gradient and the like, and distributing target braking torque to the hub motor and the EMB; and the hub motor and the EMB execute the target braking torque command, and connection between the inverter and the battery is cut off in time. According to the method, the slip rate can be calculated more finely, the target slip rate and controller parameters are adaptively adjusted according to working conditions, the road adhesion coefficient utilization rate can be effectively improved, and the braking efficiency and stability are improved.

The invention belongs to the field of distributed driving automobile control, and discloses a distributed driving automobile path tracking and stability control optimization method and related equipment, and the method comprises the steps: firstly building a tire cornering stiffness dynamic range through multi-source sensing data, and reducing the influence of nonlinear characteristics on a model; then, through a dynamic output feedback model prediction control algorithm, an estimation state sequence and an error boundary of a vehicle side slip angle are calculated online, an optimal control gain is matched in real time based on a two-dimensional table, a combined control quantity of a four-wheel steering angle and an additional yawing moment is formed, and parameter uncertainty caused by vehicle speed change is effectively compensated; and finally, torque distribution is carried out by adopting a quadratic programming algorithm and taking tire utilization rate minimization as a target. According to the method, the vehicle path tracking precision and the transverse stability under the complex working condition are improved, meanwhile, the tire saturation risk is reduced, the tire utilization rate is reduced, the transverse tracking error is reduced, and unified optimization of safety and controllability in the automatic driving process is achieved.

The invention relates to the technical field of auxiliary driving, in particular to a man-machine co-driving control method and device and a vehicle. According to the invention, the takeover intention of the user is predicted based on the steering wheel control parameter, the corresponding intelligent driving control coefficient is obtained, and the intelligent driving control coefficient participates in generation of the target control parameter for finally controlling the vehicle steering system; the control degree of intelligent driving steering request parameters output by intelligent driving in the target control parameters conforms to the takeover intention of a user, the problem that an intelligent driving system and a driver grab a steering wheel is solved, safety, comfort and compliance are considered at the same time, intelligent torque distribution of man-machine co-driving is achieved in combination with cooperation of an upper computer and a lower computer, and the user experience is improved. And the flexibility of intelligent driving is improved.

The invention belongs to the field of vehicle stability control, and particularly relates to a control method suitable for integration of four-wheel independent drive and active rear wheel steering. Comprising the steps of establishing a whole vehicle dynamics model suitable for controller design, performing stable region division by combining front and rear wheel side slip angle phase planes, setting an ideal yaw velocity and an ideal side slip angle, designing a feedforward and PID feedback integrated controller, performing torque distribution and the like. According to steering wheel rotation angle input, corresponding parameters of the vehicle are combined, the vehicle stability coefficient, the ideal yaw velocity and the side slip angle are calculated, the feedforward and PID feedback integrated controller inputs the calculated rear wheel rotation angle into the controlled vehicle, the calculated additional yaw moment obtains the basic driving moment based on pedal information, and the vehicle stability coefficient, the ideal yaw velocity and the side slip angle are calculated. Torques corresponding to four wheels are input to a controlled vehicle through torque distribution, so that the purpose of integrated control is achieved, and the vehicle handling stability is improved.

The invention discloses an electric vehicle dual-motor cooperative driving energy-saving optimization method and system, and relates to the technical field of energy-saving optimization, and the method comprises the steps: collecting the data of a vehicle operation state; the torque distribution ratio alpha of the double motors is optimized according to a set objective function, the moving direction and speed are adjusted according to the optimal position of the double motors and the optimal position of a group, the optimal point is found, and the optimal torque distribution ratio is obtained; and generating a corresponding torque distribution instruction according to the optimal torque distribution ratio, sending the torque distribution instruction to a motor controller, and executing corresponding torque control. Parameters of the ES-PSO algorithm can be adjusted according to specific problems, and the flexibility helps to better adapt to different drive distribution strategy optimization problems.

The invention discloses an industrial robot collaborative optimization system, which relates to the technical field of humanoid robots and comprises a joint state acquisition module for acquiring joint torque, angular velocity, acceleration and external disturbance data in real time; the joint first constraint analysis module is used for decomposing a motion task into a high-level task and bottom-level joint control, introducing joint limiting and torque saturation constraint, solving joint optimal torque distribution through QP quadratic programming, and obtaining a first joint torque distribution result; the second joint constraint analysis module is used for designing a multi-objective optimization function based on the first joint torque distribution result and a humanoid robot joint real-time state database, carrying out weight self-adaption according to the humanoid robot joint real-time state database, synthesizing the weight of the multi-objective optimization function according to a dynamic weight, and obtaining a second joint torque distribution result; and solving an optimal joint angle, speed and moment sequence to obtain a joint moment optimal distribution result. Joint abrasion can be effectively reduced, and meanwhile task adaptability and energy efficiency optimization are enhanced.