696 results about "Predictive controller" patented technology

The invention discloses a high-precision coating die head coating thickness adjusting method based on intelligent feedback control. The method comprises the following steps: S1, constructing a data set and transmitting the data set to a controller module; s2, calculating an error and inputting the error to a fuzzy controller for preliminary control, and generating a preliminary control instruction; s3, inputting the generated preliminary control instruction into a PID controller; s4, a model prediction controller is used for predicting the change trend of the coating thickness in the future, and an adjustment signal is sent out in advance; s5, dynamically adjusting parameters of a controller module based on a dual adaptive neural network control mechanism; s6, continuously optimizing the control strategy through a'heuristic-feedback 'mechanism; s7, the control signal generated by the controller module is transmitted to the execution mechanism module; and S8, recording and storing all data in the coating process through a data processing and self-learning module, and continuously optimizing the control strategy. According to the method, fuzzy control, PID and the dual adaptive neural network are combined, and accurate control over the coating thickness is achieved.

The invention relates to the technical field of clean environment control, in particular to a dynamic pipe network and model predictive control clean room energy-saving pressure control method and system. Obtaining current pipe network impedance according to the pipe network impedance curve; calculating the current valve impedance according to the valve opening; calculating the total pressure drop of the current pipe network; constructing a fan dynamic model based on a fan similarity law to calculate the air volume at the next moment; a clean room pressure difference dynamic model is constructed, and the clean room pressure difference at the next moment is calculated; constructing a state space model and a target function of a model prediction controller; and when the pressure difference of the clean room is unstable, predicting the change trend of the pressure difference of the clean room in the next time period, and reversely solving the optimal fan frequency and valve opening of the discretization state space model by using the model prediction controller so as to achieve the lowest pipe network impedance and obtain the optimal fan frequency. The control precision and robustness of the pressure difference of the clean room are remarkably improved, and meanwhile energy consumption is reduced.

The invention discloses a biped robot reinforcement learning control method which comprises the following steps: after a model prediction controller receives a walking instruction, outputting expected angle data of each joint of a robot to a reinforcement learning neural network, and meanwhile, returning joint angle data of an actual strategy of the reinforcement learning neural network to the neural network by a sensing system at a bottom layer; a joint angle taking time as a sequence and planned by model prediction is compared with a joint angle actually generated by a neural network, and model prediction control is fused into a reinforcement learning training process by setting a reward function for punishment, so that the reinforcement learning training efficiency is improved, and a stable gait is more quickly achieved. Excellent gaits planned by the MPC are transplanted into reinforcement learning control, and control robustness can be improved under the condition that the excellent gaits of the MPC are reserved; and meanwhile, the joint angle data which is obtained by taking time as a sequence and is obtained by taking MPC as a planner can accelerate the reinforcement learning training process, so that the training speed and the control effect of reinforcement learning are greatly improved.

The invention discloses an aircraft attitude control method and device based on a semiconductor microcomputer system, and the method comprises the steps: collecting the multi-source sensor data of an aircraft, calculating an attitude error vector, and constructing a three-dimensional error space coordinate system; taking the three-dimensional error space coordinate system as a reference, adjusting the model in real time, and solving the optimal attitude adjustment amount in a prediction window period; the optimal attitude adjustment amount is input into an execution mechanism distribution module, and a thrust adjustment instruction sequence and a control surface deflection angle instruction queue of each vector propeller are generated through calculation in combination with a propeller response matrix and a control surface efficiency coefficient; dynamically correcting and predicting weight parameters of the controller through closed-loop verification; and updating the multi-objective optimization model based on the corrected parameters, adjusting the instruction distribution proportion through a dynamic reconfiguration interface, and writing the updated parameters into the attitude database to form a complete closed-loop period. According to the invention, high-precision real-time control of the attitude of the aircraft is realized, and the flight stability and the energy efficiency ratio in a complex environment are improved.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention discloses a new energy commercial vehicle fast charging working condition thermal management control method and system, and relates to the technical field of new energy vehicle thermal management and fast charging control, and the method comprises the following steps: collecting the temperature, voltage and current data of each subarea of a battery in a fast charging process, forming a time synchronization sequence, and based on the sequence, obtaining a new energy commercial vehicle fast charging condition; calculating internal thermal resistance and thermal capacity parameters of the battery in real time by using an online identification algorithm, updating a basic thermal model to obtain a corrected thermal dynamic model reflecting actual thermal characteristics of the current battery, importing the corrected model into a model prediction controller, constructing a multi-objective optimization function, and performing rolling solution in a prediction time domain to obtain a prediction model; and outputting an optimal target charging current instruction and a cooling total demand, adjusting the charging power according to the target current, and combining the temperature difference distribution of each partition. Accurate prediction and partition cooperative control of the thermal state of the battery in the fast charging process are achieved, the temperature rise and the temperature difference are effectively restrained while the charging efficiency is guaranteed, the service life of the battery is prolonged, and the system safety is improved.

The invention provides a high-speed magnetic levitation suspension system control method and system based on edge calculation and Transform prediction, and the method comprises the steps: constructing a Transform prediction model with a space-time attention mechanism and an autoregression mechanism based on obtained local low-delay calculation resources and train real-time sensing data, and deploying the Transform prediction model in a vehicle-mounted edge calculation unit; performing short-term high-precision prediction on the gap, the acceleration and the disturbance trend at a plurality of sampling moments in the future through a Transform prediction model to obtain a prediction result; processing actuator current saturation and gap safety threshold hard constraints in a limited prediction domain by using a model prediction controller, and solving an optimization control sequence in real time in combination with a prediction result; and overlapping a control barrier function as a safety filter of the model prediction controller, correcting the optimized control sequence to obtain an optimal control sequence, and controlling the high-speed magnetic suspension system. According to the method, the cloud communication delay and jitter are reduced, and the robustness and security of the system under uncertain disturbance are improved.

The invention provides an overhead and portal crane lifting appliance control method based on multi-stage model predictive control, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring a control parameter and an initial state variable so as to define an operation boundary and an initial state, and establishing a dynamic model containing a coupling relationship among a trolley acceleration, a sling rope length acceleration and a sling swing angle; acquiring obstacle distribution by using space sensing equipment, and calculating the dynamic safety height of the lifting appliance by combining obstacle parameters and control parameters; judging whether the lifting appliance is in an obstacle avoidance stage or a target tracking stage according to the initial state variable and the dynamic safety height; constructing a model prediction controller and dynamically adjusting a state weight matrix and a control input weight matrix; finally, a control input sequence is generated through an optimization algorithm, and track safety and attitude stability control of the lifting appliance is achieved. According to the technical scheme, the swing angle of the lifting appliance is restrained, meanwhile, the obstacles are effectively avoided, and the safety of the lifting appliance in the whole operation process is guaranteed.

The invention is suitable for the technical field of vehicle intelligent control, and provides a model prediction controller parameter self-calibration method based on deep reinforcement learning. According to the method, modeling expression is carried out on the delay characteristic of a vehicle steering system by utilizing a first-order inertial link, the delay characteristic is used as a state variable to be augmented to an error tracking control architecture, and a model prediction controller considering the steering delay characteristic is designed according to the state variable; the problem that the transverse motion control performance of the automatic driving vehicle is deteriorated due to hysteresis between the target wheel rotation angle and the actual vehicle rotation angle is solved. Meanwhile, a deep Q network (DQN) of a multi-target reward mechanism is designed, and an optimal strategy is trained to quickly calibrate multiple parameters of model predictive control (MPC). The generalization performance of reinforcement learning enables the vehicle to adapt to most complex working conditions after sufficient training, the problems that in a traditional method, the parameter calibration time of a control system is long, and robustness is insufficient are solved, and high-precision and stable transverse control over the full-drive-by-wire four-wheel steering vehicle under the complex working conditions is achieved.

The invention belongs to the technical field of robot control, and particularly relates to a self-adaptive whole-body control method suitable for a quadruped robot in an uncertain dynamic environment, and the method integrates an extended state observer (ESO), a model prediction controller (MPC) and a whole-body controller (WBC) based on hierarchical optimization. And a closed-loop control architecture is constructed to improve the robustness and motion coordination ability of the quadruped robot in a complex environment. Firstly, external disturbance is estimated and compensated in real time through ESO; then, the MPC optimizes future system behaviors in a rolling manner under the condition of considering interference terms, and an expected foot end counter-acting force is output; and finally, the WBC is combined with the priorities of the multiple tasks to generate all joint control instructions in a constraint optimization form, and task collaboration and dynamic adaptation are achieved. The method has the advantages of being high in disturbance self-adaptive capacity, high in control precision, suitable for a multi-task environment and the like, and is particularly suitable for quadruped robot control tasks in complex dynamic scenes such as transportation and search and rescue.

The invention relates to the field of aircraft intelligent control, and discloses a sensor fault resistant low-altitude aircraft multi-mode propulsion control method, which comprises the following steps: constructing a nonlinear model of a short-vertical aircraft hybrid propulsion system, taking deep exploration optimization reinforcement learning as a main control, and combining a fuzzy controller and a model prediction controller to form a strategy fusion group; the method comprises the following steps: introducing a residual perception mechanism and a GIRLS-EKF health estimation module, extracting residual signal features by using CNN and Transform, and carrying out real-time diagnosis and dynamic weight adjustment on an abnormal measurement state of a sensor in combination with generalized residual least square filtering and an extended Kalman structure; the controller fusion module outputs the optimal control quantity in a self-adaptive manner according to the health state and the flight mode, and the fault-tolerant performance and the energy efficiency scheduling capability of the multi-propulsion system under the complex working condition are improved; the method is suitable for low-altitude economy and urban air traffic task scenes, and has good engineering integration and intelligent control application prospects.

The invention discloses a double neural network model predictive control method for a robot flexible joint, and belongs to the technical field of robot intelligent control, and the method comprises the steps: carrying out the modeling of a nonlinear dynamic system of a flexible joint robot, and building a dynamic model; setting a GRU neural network model to approach a dynamic model of the system; based on a GRU neural network model, constructing a hybrid control NNMPC controller including position, speed and torque; optimization of the NNMPC controller is optimized on the basis of a nonlinear optimization solver; and setting an NARX neural network model to approach a solution of an NNMPC controller, and forming a dual-neural network model predictive control method. According to the double-neural-network model prediction control method for the flexible joint of the robot, two neural networks are introduced to work cooperatively, one neural network is used for approaching the dynamics of the system, the other neural network is used for accelerating the optimization solving process of the model prediction controller, the calculation overhead possibly caused by a traditional method in a high-dimensional nonlinear system is reduced, and the calculation efficiency is improved. Therefore, real-time control is realized.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a wind disturbance compensation control method for a quad-rotor unmanned aerial vehicle, which comprises the following steps: tracking flight paths of the quad-rotor unmanned aerial vehicle under different wind conditions through a model prediction controller to obtain flight state data under different wind conditions; according to the flight state data and the kinetic equation under different wind conditions, a kinetic model of the four-rotor unmanned aerial vehicle is constructed, and a flight data set is obtained based on the kinetic model; determining the attitude correction of the unmanned aerial vehicle based on the optimized deep network learning model; according to the attitude correction amount of the unmanned aerial vehicle and the flight state data under different wind conditions, the motor basic thrust of the four-rotor unmanned aerial vehicle is obtained; and according to the basic thrust of the motor and the wind disturbance compensation force output by the optimized deep network learning model, obtaining the target thrust of the motor after wind disturbance compensation. The invention further discloses a wind disturbance compensation control device and system for the quad-rotor unmanned aerial vehicle.

The invention discloses a response control method for a charging station participating in a low-voltage treatment scene, and the method comprises the following steps: S1, collecting the voltage data of a charging pile, calculating a deviation, and judging a low-voltage grade; s2, when the deviation is in a light interval, outputting reactive power support voltage in proportion; s3, when the deviation exceeds a heavy threshold value, reducing active power and starting V2G feedback; s4, constructing a graph structure model, and extracting inter-node power coupling and user behavior factors; s5, inputting the model into an improved prediction controller, predicting a future voltage state, and optimizing and outputting a current period power control value; s6, power adjustment is executed, and feedback power is slowly output in the V2G stage; and S7, comparing the control deviation with the behavior factor change, updating the node control priority, and feeding back for rolling optimization of the next period. According to the invention, while the charging regulation and control precision is improved, the user experience and the voltage stability are effectively considered, and the method is suitable for various low-voltage treatment scenes.

The invention belongs to the technical field of fine chemical reaction safety testing and automatic chemistry, and particularly discloses a nonlinear model predictive temperature control method of a semiconductor temperature control type synthesis reactor. The method comprises the following steps: constructing a dynamic heat transfer model of the system based on the semiconductor temperature control type synthesis reactor; designing a nonlinear model prediction controller according to the heat transfer model of the synthesis reactor; constructing a multi-constraint objective function for nonlinear predictive control according to the characteristics of the controlled object; and solving an optimal control sequence for the nonlinear model prediction controller by adopting a projection gradient method. Compared with a traditional PID temperature control algorithm, the temperature of the reactor can be accurately predicted and controlled through the designed nonlinear model prediction temperature control algorithm, it is ensured that the chemical reaction is conducted at the optimal temperature, and the robustness and adaptability of a control system are improved.

The invention provides a brushless motor multi-axis cooperative control method and system for an industrial robot, and relates to the technical field of control, and the method comprises the steps: processing the real-time parameters of a brushless motor of each joint axis through constructing an extended dynamic model, and obtaining a temperature drift coefficient and a load inertia parameter; a self-adaptive pre-compensation model, a neural network compensator and a recurrent neural network prediction model are combined, a torque control instruction is output, a three-phase current prediction value is calculated, a distributed model prediction controller is adopted to generate an optimal switching sequence, multi-axis cooperative control is achieved, and the system stability and the response speed can be effectively improved.

The invention provides a multivariable decoupling control method and system for an air inlet system of a high-altitude simulation cabin, and the method comprises the steps: firstly building a precombustion chamber cavity thermodynamic differential equation, and building a state equation in combination with a regulating valve first-order inertia model and an engine model; constructing a decoupling matrix by using a differential flatness theory, and converting a pressure and temperature coupling system into an independent control channel; monitoring a system state in real time by using an extended state observer, uniformly estimating internal and external disturbances as total disturbances, and generating a compensation signal; the model prediction controller performs rolling optimization on future time domain control quantity according to deviation between a set value and an actual value, generates an initial instruction in combination with the compensation signal, and converts the initial instruction into an independent valve control signal through a decoupling matrix; the high / low-temperature airflow mass flow is controlled through the opening degree of the adjusting valve, the engine model outputs actual parameters to form closed-loop feedback, and the controller updates the control quantity in each cycle. The multi-variable strong coupling, the dynamic disturbance and the model uncertainty are effectively overcome, the control precision and the parameter robustness are both achieved, and the complex working condition requirements are met.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention is suitable for the technical field of permanent magnet synchronous motors, and provides a permanent magnet synchronous motor current prediction control method based on parameter identification, which comprises the following steps: constructing a permanent magnet synchronous motor current prediction control model; performing parameter sensitivity analysis based on the constructed current prediction control model; and according to a parameter sensitivity analysis result of the current prediction control model, carrying out online identification on parameters of the permanent magnet synchronous motor by adopting improved extended Kalman filtering, sending the identified parameters to the current prediction controller in real time, and updating the parameters in the current prediction controller. And the current error of the current prediction control of the permanent magnet synchronous motor during parameter mismatch is reduced. According to the permanent magnet synchronous motor current prediction control method based on improved extended Kalman filtering parameter identification provided by the invention, the identification precision of extended Kalman filtering on motor parameters is improved, so that the current prediction control performance of the permanent magnet synchronous motor is effectively improved under the condition that the motor parameters are mismatched.

The invention discloses a shape adaptive planning and control method for a deformable unmanned aerial vehicle, and the method comprises the steps: carrying out the searching based on an occupied grid map of a scene through employing a variable-size kinematics A * path planning algorithm, and obtaining an initial path; by taking the initial path as an initial condition, shape-adaptive trajectory optimization in a continuous space-time space is carried out to obtain an optimized trajectory, and in the trajectory optimization process, the mass center position and deformation parameters of the unmanned aerial vehicle are optimized at the same time; and based on the optimized trajectory, on the basis of a nonlinear model predictive controller and by combining an incremental nonlinear dynamic inverse algorithm, calculating and compensating external force disturbance and external torque disturbance caused by deformation of the unmanned aerial vehicle and a load, so as to realize shape adaptive control of the deformable unmanned aerial vehicle.

The invention discloses a commercial vehicle team cruise control method and system, computer equipment and a storage medium, and relates to the technical field of automatic driving and vehicle infrastructure cooperative control, and the method comprises the steps: carrying out the vehicle role interaction of a team vehicle according to a cooperative relation and a vehicle role interaction logic; establishing a surrounding environment potential field model, and describing an interaction relationship between vehicles inside and outside the motorcade; calculating the longitudinal and transverse potential fields of the vehicles of the fleet, and controlling the distance between the vehicles and the safe distance; a model prediction controller for team cruise is constructed by considering the target speed and the environmental influence; and the safety, speed and control constraints of vehicle movement are set, and the driving performance is optimized. The stability of formation control is enhanced, the formation requirements of complex scenes are met, the real-time dynamic control capability is improved, and the formation energy efficiency is optimized.

The invention relates to a tractor clutch test bench cooperative control method based on MPC-BP neural network PID, and belongs to the technical field of tractor clutch performance testing. The method specifically comprises the steps of establishing an enhanced mathematical model of a tractor clutch test bed system; an MPC prediction controller is designed, and an optimal control sequence is generated through rolling optimization of an objective function; a BP neural network PID controller is improved; a multi-unit cooperative control strategy is designed, MPC prediction and BP neural network PID control are combined, and cooperative adjustment of the torque of the loading unit, the rotating speed of the driving unit and the displacement of the clutch separation mechanism unit is achieved; a self-adaptive adjustment and load compensation mechanism is introduced, and the adaptability of the system to different working conditions and clutch models is improved through online model correction, heat fading modeling and fault diagnosis. The control precision, the dynamic response speed and the robustness of the test bed are remarkably improved, and the test bed can be widely applied to research, development and performance testing of the tractor clutch.

The invention relates to a trajectory tracking optimization control method for a two-connecting-rod mechanical arm with an incomplete measurable state, and belongs to the technical field of control engineering. The method utilizes a preset performance function and an event triggering mechanism to implement event triggering model prediction optimization control: establishing a mathematical model of a two-connecting-rod mechanical arm system, and obtaining a state-space equation of the system according to the mathematical model; designing an extended state observer, setting a preset performance function, adjusting parameters of the preset performance function to meet transient performance requirements, and performing error conversion on an output tracking error according to the transient performance requirements; designing an event triggering model predictive controller based on a preset performance function; solving an optimization problem to obtain an optimization control input variable and a state variable; and designing a self-adaptive time-varying trigger mechanism to reduce the optimization times of predictive control. The method integrates the advantages of a preset performance control method, an event triggering mechanism and a prediction control method, has the advantages of high response speed, high positioning precision, small calculation amount and the like, and has relatively good tracking performance and control flexibility.

The invention relates to a coaxial multi-rotor unmanned aerial vehicle control method based on a Koopman operator, and the method comprises the steps: constructing a deep neural network based on the Koopman operator, and mapping a state variable to a high-dimensional observable space through an encoder; in a high-dimensional observable space, state variables are predicted through a linear connection layer based on dimension rising state variables and rotor rotating speeds; the decoder restores the prediction result to an original state space; training a deep neural network by using historical flight data to obtain codec parameters and a weight of a linear connection layer, and constructing a linear dynamic model of the observation function and the unmanned aerial vehicle; and the prediction controller predicts state variables of future multiple steps of the unmanned aerial vehicle by using a linear dynamics model, and enables the unmanned aerial vehicle to track a given trajectory by minimizing a cost function to obtain optimal control input. Compared with the prior art, the method has the advantages that complete data-driven modeling is realized, and the method can adapt to unmanned aerial vehicles with different sizes, loads and flight conditions.

The invention belongs to the technical field of temperature control, and discloses a temperature control method and device of a hot runner system for an inverted mold. The method comprises the following steps: defining a critical melt area in a physical sprue which directly influences the product quality in a sprue of a hot runner system as a virtual sprue; dynamically generating a melt viscosity target track of the virtual gate region according to the type of an injection molding material and an injection molding process stage; based on measurable injection molding process parameters and sprue area temperature, estimating a current melt viscosity value of a virtual sprue area in real time through a state observer; calculating to obtain an optimal power control sequence by adopting a multivariable model prediction controller; and driving a sprue area heater according to the optimal power control sequence, so that the actual change of the melt viscosity of the virtual sprue area tracks a melt viscosity target track. According to the invention, the defects such as salivation and wire drawing can be effectively eliminated, so that the sprue quality stability and the production efficiency are improved.

The invention is suitable for the technical field of unmanned aerial vehicle control, and provides a reinforcement learning optimization unmanned aerial vehicle trajectory tracking model prediction control method, which comprises the following steps: S1, establishing a four-rotor unmanned aerial vehicle dynamics model, which comprises a position dynamics model and an attitude dynamics model; s2, constructing a model prediction controller based on the unmanned aerial vehicle kinetic model, and establishing a model prediction control optimization problem to obtain an unmanned aerial vehicle trajectory tracking cost function; and S3, constructing a near-end strategy optimization algorithm network which comprises an action home network and a reviewer network, adjusting a weight matrix of a cost function of the model prediction controller through a near-end strategy optimization algorithm, and solving a model prediction control optimization problem to obtain optimal control input. According to the method, the problem of difficulty in controller parameter selection in an existing unmanned aerial vehicle control method is solved, and the processing capacity and stability of the unmanned aerial vehicle facing different task environments are improved.

The invention discloses a ground wire adaptive visual trajectory tracking control method for unmanned aerial vehicle inspection, relates to the technical field of unmanned aerial vehicle inspection, and solves the problem that it is difficult to effectively fuse geometric, texture and multispectral features to construct a ground wire lightweight model. And a three-dimensional coupling constraint system is difficult to be combined to adapt to a dynamic scene of ground wire inspection. Comprising the following steps: collecting an initial visual image and multispectral data of a ground wire, extracting and splicing geometric, texture and state features, and constructing a ground wire lightweight model based on knowledge distillation to output a state evaluation result; the flight controller combines the model and geographic information to construct a three-dimensional coupling constraint system, calls a dynamic programming algorithm to generate an initial trajectory and sets a constraint deviation real-time monitoring mechanism; multi-dimensional deviation is calculated, and comprehensive deviation is obtained through weighting of the incidence matrix; and if the deviation exceeds a threshold value, starting a lightweight prediction controller to solve an optimal control problem and adjust flight parameters to generate a new trajectory, otherwise, flying along an initial trajectory.

The invention discloses a fuel cell rapid cold start method considering initial water content optimization, and belongs to the technical field of fuel cell automobile optimization control. According to the fuel cell rapid cold start method considering initial water content optimization, a closed-loop model prediction controller is firstly designed to accurately control the current of a galvanic pile, and then the initial film water content is optimized by using a particle swarm optimization algorithm, so that the rapidity of a cold start system is improved. The method can be structurally divided into the following parts: establishment of a cold start model, design of a parameter self-tuning model prediction controller and optimization of the optimal initial membrane water content. In the cold start process, the current output of the electric pile is adjusted in real time according to different environment temperatures and membrane water contents, controller parameters are adjusted in a self-adaptive mode, the freezing rate and the heating rate of water produced by the electric pile can be effectively balanced, and the performance of a cold start system is improved.

The invention relates to the technical field of industrial process control, and discloses a digital twinning-based fermentation process temperature adaptive control system, which comprises a data acquisition and state representation module, a probability generation type digital twinning module, an adaptive risk management module, an opportunity constraint model prediction control module and a model iteration trigger module, according to the method, probability distribution of future states is predicted by constructing probability generation type digital twinning, and the uncertainty of the model is quantified in real time; the system can dynamically adjust the risk tolerance, drives the chance constraint model prediction controller, seeks an optimal control solution on the premise of ensuring the process safety probability, triggers the model adaptive update by means of an online evaluation mechanism, and realizes the optimal control solution on the basis of effectively managing the process uncertainty. The fermentation temperature is subjected to high-performance self-adaptive control considering safety and economy, and the problem that a traditional deterministic model is insufficient in control robustness is solved.

The invention discloses a commercial vehicle driving control method considering a load non-uniform distribution characteristic. The method comprises the following steps: acquiring and preprocessing sensor data of a commercial vehicle; estimating yaw disturbance torque caused by uneven load distribution in real time; a robust model prediction controller based on yaw disturbance torque compensation is constructed, and multi-axis coordinated yaw torque meeting the driving stability requirement under the non-uniform load working condition is output; driving force optimization distribution is carried out, and target driving force of each electric wheel is determined; and designing a driving force synchronous tracking controller based on the tire relaxation characteristic model, and outputting the torque of each electric wheel to complete the driving stability control of the commercial vehicle. According to the method, the yaw disturbance torque observer and robust model predictive control are fused, and the disturbance yaw torque is estimated in real time and compensated in a feedforward mode, so that pertinence and fineness are better in the aspect of disturbance suppression; the auxiliary sliding mode control can further improve the robustness of the system in an unknown or rapidly changing external environment.