365 results about "Robust control" patented technology

The invention discloses a renewable energy power generation power prediction and power dispatching method and system, and the method comprises the steps: collecting the historical power generation data and real-time meteorological data of renewable energy power generation, carrying out the linear interpolation of the historical power generation data and the real-time meteorological data, and carrying out the missing value filling and box plot anomaly detection, obtaining a normalized training data set; constructing a hybrid prediction model by using the normalized training data set and adopting a neural symbol acceleration technology with time logic constraints, extracting medium and long term space time features, and generating a renewable energy power generation power prediction result; and according to the renewable energy power generation power prediction result and the system constraint condition, adopting a linear one-dimensional projection constrained distribution robust control method to formulate a scheduling strategy, and utilizing the scheduling strategy to solve an optimal scheduling scheme through mixed integer linear programming. According to the method, the renewable energy power generation power prediction precision and the power dispatching robustness are remarkably improved.

The invention discloses a data-based predefined time heterogeneous multi-agent formation collision avoidance method. The method comprises the following steps: establishing a nonlinear heterogeneous multi-agent system; designing a bimodal artificial potential field function to perform dynamic obstacle avoidance and prevent regional escape; establishing a self-adaptive robust controller used for generating an obstacle avoidance safety motion trail of the root leader; designing a self-adaptive formation zooming mechanism of the leader, and constructing a predefined time affine observer of the follower based on the self-adaptive formation zooming mechanism; designing a unified obstacle function; constructing a virtual control law for processing tracking errors based on the unified obstacle function; designing a neural network estimator; designing controllers of the leader and the follower according to the virtual control law; and forming a collision avoidance decision of the heterogeneous multi-agent formation based on a self-adaptive robust controller, a predefined time affine observer, a neural network estimator and controllers of the leader and the follower. According to the method, safe, efficient and robust cooperative control of the formation in a complex environment is realized, and the safety and task execution efficiency of the heterogeneous multi-agent formation in a limited and unknown environment are remarkably improved.

The invention discloses an adversarial reinforcement learning training method for robust control of a fixed-wing aircraft, and the method comprises the steps: constructing a nonlinear simulation environment and a task set of the fixed-wing aircraft, and constructing a plurality of fixed-wing aircraft agents in parallel in the simulation environment; an adversarial agent module based on a neural network AdvNet is introduced to generate environmental disturbance. A near-end strategy optimized Actor-Critic structure is adopted, an intelligent agent and an adversarial agent module are subjected to joint training through a task set, and a robust control strategy is obtained step by step. In the training process, based on a course learning mechanism, disturbance constraint parameters of the confrontation agency are dynamically adjusted according to the completion condition of a flight task. After training convergence, obtained control strategy parameters are applied to the fixed-wing aircraft, and robust flight control is achieved. According to the method, the robustness and the control precision of the aircraft facing complex environment disturbance can be improved, and the method is suitable for various flight missions with uncertainty.

The invention discloses a data center machine room energy-saving optimization method and system based on thermal environment prediction. The method comprises two stages of offline modeling and online prediction optimization. In the off-line stage, a CFD simulation model is constructed based on a machine room physical structure, equipment layout and thermal load parameters, and high-precision temperature field data is generated; using the thermal environment prediction model to input equipment parameters and load change to output future space temperature distribution; meanwhile, an XGBoost hybrid energy consumption prediction model is constructed based on the wind speed ratio or the fan frequency, the number of running fans and related characteristics. In the online stage, the lowest energy consumption and the minimum temperature deviation serve as targets, and a Pareto optimal solution set is generated through an MOEA / D algorithm; the optimal wind speed ratio / frequency and equipment number combination is selected as required to control operation of the air conditioner; and dynamically updating model parameters by sliding a time window to realize long-term robust control of the system. According to the method, intelligent energy-saving control of the machine room is realized by fusing CFD simulation, time sequence prediction, energy consumption modeling and a multi-objective optimization algorithm.

The invention discloses a power grid broadband oscillation suppression method and device based on adaptive robust control, terminal equipment and a storage medium, and belongs to the field of power systems. The method comprises the following steps: performing fuzzy disturbance classification according to an oscillation mode, an oscillation mode characteristic parameter and a state parameter obtained according to a power grid operation parameter to obtain a disturbance type label and a disturbance degree; the disturbance degree serves as a weighting factor, the initial control parameters are corrected according to the power grid operation parameters, the initial control parameters of the controller and the disturbance type label, and a self-adaptive control strategy is generated after correction; when the disturbance degree is smaller than a disturbance threshold value, the state parameters are regulated and controlled according to a self-adaptive control strategy to update power grid operation parameters; and when the disturbance degree is not less than a disturbance threshold value, taking the minimum sum of the system gain, the frequency deviation and the voltage fluctuation as a target, and regulating and controlling the state parameters under the H infinite robust constraint to update the power grid operation parameters. The problem that in the prior art, broadband oscillation suppression is difficult to adapt to sudden change working conditions is solved.

The invention discloses a reinforcement learning-based quadrotor unmanned aerial vehicle attitude preset performance control method, which comprises the following steps of: establishing an unmanned aerial vehicle attitude kinetic equation by considering a time-varying inertial parameter and external unknown interference; a sub-channel preset performance controller is constructed, and a robust control quantity is generated in combination with preset performance and a non-singular fast terminal sliding mode control technology; designing a reinforcement learning parameter generator, and dynamically optimizing 12 time-varying parameter estimated values through a time sequence feature extraction network and a residual network; and establishing an online reinforcement learning training mechanism, constructing a multi-target reward function, and optimizing the output of the parameter generator through the multi-target reward function. According to the method, a reinforcement learning method is adopted to replace a traditional self-adaptive method to estimate time-varying parameters, multi-degree-of-freedom decoupling optimization is achieved through a sub-channel control architecture, tracking error preset performance constraint is achieved under the working condition of time-varying inertial parameters, and the dynamic adjustment capacity and anti-interference robustness of an attitude system are remarkably improved.

The invention provides a self-adaptive fractional order nonsingular terminal sliding mode control method based on time delay estimation, and relates to the technical field of manipulator control. Designing a non-singular terminal sliding mode surface containing fractional differential and power transformation, and further constructing a reaching law with variable gain; then, the unknown dynamic state and disturbance of the system are compensated in real time through a time delay estimation technology by utilizing historical control input and state information; finally, a control law is generated by integrating the sliding mode surface, the reaching law and the time delay estimation compensation item, high-precision and strong-robustness control over an uncertain nonlinear system is achieved, and the tracking precision and the anti-interference capacity of the manipulator in the uncertain environment are effectively improved.

The invention provides a robust control automatic setting method for a constant-pressure water supply system, and the method comprises the steps: obtaining a system working condition in real time, and extracting multi-dimensional state parameters including real-time flow, pressure deviation, current deviation and power deviation; the current load working condition is judged according to the pressure deviation, disturbance characteristics are analyzed by integrating all parameters, and the disturbance grade and type are determined. On the basis, robust control parameters are automatically set in combination with real-time working conditions, load conditions and disturbance characteristics of the system. And finally, performing multi-stage cooperative control on the system by using the set parameters, so that the constant-pressure water supply system is automatically set. According to the robust control automatic setting method for the constant-pressure water supply system, load working conditions and disturbance characteristics are recognized based on multi-dimensional state parameters, robust control parameters are self-set in a targeted mode, and automatic setting of the constant-pressure water supply system is achieved through multi-stage cooperative control. Therefore, the intelligent level of the constant-pressure water supply system and the adaptability in different operation environments are effectively improved.

The invention discloses a spraying equipment automatic control system based on the industrial internet, and belongs to the technical field of industrial automatic control. The system comprises a state sensing unit, a feature extraction unit, a risk prediction unit and a closed loop correction unit. The state sensing unit obtains a real-time working condition data set; the feature extraction unit generates a core process feature set; the risk prediction unit generates a quality risk prediction set; the closed-loop correction unit calculates a target process set point. The system senses multi-source data such as environment temperature and humidity and coating flow in real time, quantifies the influence of environment and process fluctuation on spraying quality, predicts indexes such as coating film thickness and defect probability in a prospective mode, and adjusts process parameters in a self-adaptive mode on the basis of working condition risk levels. Compared with the prior art, complex working condition insight, quality risk look-ahead prediction, dynamic intelligent optimization and extreme working condition robust control are achieved, the contradiction between high quality and high efficiency in automatic spraying is relieved, and the spraying quality stability and the production efficiency are improved.

The invention belongs to the technical field of aviation hybrid power, and discloses a self-adaptive intelligent power distribution control method for an aviation hybrid power system, which comprises the following steps: constructing a full-dimensional state model through multi-dimensional perception fusion, predicting working condition transition in combination with deep reinforcement learning, and dynamically adjusting a power distribution strategy; the response speed is guaranteed preferentially during emergency takeoff, energy recovery is emphasized during conventional landing, the problem that a traditional distribution mode is disjointed with actual requirements is solved, power output of an engine and a battery better fits the flight working condition, and stable operation can still be achieved in complex environments such as sudden airflow; by introducing digital twinborn rehearsal verification and adaptive robust control, risks of engine overtemperature, battery overcharge and the like are exposed and corrected in advance through virtual simulation, disturbance is dealt with in combination with a'basic + compensation 'power structure, part faults caused by unreasonable power distribution in traditional control are avoided, and the reliability of the system is improved. And the reliability of the hybrid power system in key stages of takeoff and landing is improved.

The invention discloses a tunnel pipe roofing construction dynamic deviation correction method based on laser ranging and optical fiber sensing, and relates to the technical field of measurement and sensing, and the method comprises the steps: S1, collecting laser and optical fiber data, obtaining real strain through temperature coupling compensation, and calculating global pose parameters; s2, generating an eigenmode component based on the real strain and the global pose parameter, and screening and constructing an intelligent feature vector according to the contribution degree of the physical coupling feature; s3, constructing a physical information driving state estimation model in combination with pipe joint mechanical constraints, and outputting a pipe joint state estimation value in real time; s4, performing construction deviation prediction by using the pipe joint state estimation value, and generating an optimal control sequence and instruction; and S5, applying the control instruction to the construction device, and adaptively updating the key parameters of the system through a meta-optimization algorithm based on feedback. And through multi-modal data fusion and physical constraint intelligent prediction, high-precision deviation correction, closed-loop robust control and system self-adaptive optimization of the pipe joints are realized, and the intelligence and long-term precision of tunnel pipe curtain construction are improved.

The invention discloses a fuzzy neural network PID photovoltaic series welding temperature control method based on improved PSO optimization. The method comprises the steps that a target photovoltaic series welding equipment heating transfer function model is acquired; setting an initial PID parameter; adjusting a PID increment parameter of the PID control module in real time according to the temperature error, the temperature error change rate and a preset fuzzy rule base; the neural network is combined with fuzzy control, and parameters and rules of fuzzy control are automatically modified through training data; according to the photovoltaic series welding temperature condition, the fitness function of the PSO algorithm is improved, the adjustment mode of the inertia weight is improved, and the improved PSO algorithm is used for optimizing the initial PID parameters of fuzzy neural network PID photovoltaic series welding temperature control. Self-adaptive precise control and robust control of the welding temperature control system are achieved, the temperature fluctuation phenomenon in the photovoltaic series welding process is effectively improved, and the robustness and control reliability of the system are enhanced.

The invention discloses a new energy electric power robustness control method, system and device for preventing chain overload of a power transmission section and a medium. The method comprises the following steps: obtaining the active power flow size and the operation state of each branch in the power transmission section; calculating the comprehensive sensitivity of all the controllable nodes to each branch in the power transmission section, and generating a composite priority index based on the calculated comprehensive sensitivity, the classification of the nodes and the new energy fluctuation risk; performing priority ranking on the generator nodes to generate a node sequence; in combination with a reverse equivalent pairing principle, selecting an adjustment node pair, and calculating an adjustment amount of node output by considering an adjustment amount dynamic margin constraint of new energy output uncertainty; recalculating the active power flow and the operation state of each branch in the section; and judging whether the section power flow and the margin meet a termination condition or not to realize optimal control. According to the method, the operation robustness of the new energy power system is improved, and the overload of the transmission section can be quickly and efficiently eliminated.

The invention discloses an under-actuated unmanned surface vehicle safe formation tracking control method based on robust obstacle control. The method comprises the following steps: establishing a multi-unmanned ship group and performing real-time communication, and enabling each follower to follow a leader according to a formation configuration and to avoid an obstacle; a safe formation tracking control framework of the underactuated unmanned surface vehicle is established, during formation tracking, followers input state information into the control framework, processing is carried out according to a reference trajectory of a leader and real-time information of a current follower and an obstacle, and forward and steering control torque instructions are output to control the current follower. Obstacles are avoided and formation configuration is restored to continue following the leader. According to the method, the underactuation characteristic problem of the unmanned surface vehicle is solved, the worst case error generated when the neural network approaches the unknown dynamic state can be effectively compensated, it is ensured that the unmanned surface vehicle can strictly avoid collision with the obstacle in the complex environment, and meanwhile the formation tracking performance is optimally kept.

The invention belongs to the technical field of robot motion control, and particularly relates to a quadruped robot control method based on error symbol robust integral feedback, which combines an RISE control mechanism with a constraint-based optimization method, and realizes high-precision trajectory tracking and strong-robustness control under the condition that a system model has relatively large uncertainty. According to the method, RISE controllers are designed in a position subsystem and a posture subsystem respectively, quadratic programming (QP) optimization distribution of ground contact force is fused, control input is dynamically adjusted while the physical feasibility of foot end force is ensured, and closed-loop stable control over the whole-body movement of the quadruped robot is achieved. The control strategy has an asymptotic error convergence characteristic, and can be widely applied to quadruped robot motion tasks with severe load change and frequent interference.

The invention relates to the technical field of closed-loop drift control, and particularly discloses a closed-loop drift control method and system for an autonomous vehicle in an uncertain environment, and the method comprises the steps: building a vehicle kinematic model based on a Frenet coordinate system, and constructing an MPC path tracking controller with a preview mechanism; establishing a three-degree-of-freedom vehicle dynamics model with additional yawing moment; the method comprises the following steps: establishing a UniTile-Ctrl tire dynamic model; designing a robust drift controller by combining LQR and integral sliding mode control on the basis of the whole vehicle dynamic model and the tire dynamic model, designing a torque distribution module by taking minimization of distribution error and tire load utilization rate as targets on the basis of ideal rear wheel rotating speed and additional yawing moment output by the robust controller, and outputting a torque distribution model; according to the drifting vehicle tracking control method, closed-loop drifting control can be achieved, the drifting vehicle tracking control performance and robustness are improved, and safety guarantee is provided for limit control over the uncertain road surface of the vehicle.

The invention discloses a graphical interface automatic control code generation method based on a large model agent, which comprises the following steps of: firstly, generating a task execution track through interaction between a ReAct agent based on a large language model and a graphical interface environment, and translating a hard coding action in the track into a soft coding action based on a semantic positioning page element; constructing a track library of a tree structure; then, on the basis of information in the trajectory library, a retrieval enhancement mechanism is adopted to generate an RPA function capable of being independently executed, and the RPA function is synthesized on the basis of soft coding actions and is automatically injected into robustness control logic; then executing the RPA function, if execution fails, triggering breakpoint analysis, and optimizing the RPA function through a mixed trajectory; and finally, testing the generated RPA function, and selecting to execute the RPA code or back to the ReAct agent for execution according to a verification result. According to the method, an automatic generation process from an intelligent agent interaction track to a robust and reusable RPA function is realized.

The invention relates to an automatic printing calibration method and system combining vision and AI and a storage medium, a multi-mode sensing unit integrated on a printing head assembly synchronously collects two-dimensional images, three-dimensional shapes and working spacing data of a printing area in real time, and data fusion is carried out by using a model based on a neural radiation field; generating a high-fidelity multi-dimensional digital mapping; carrying out defect identification and positioning by adopting a pre-trained convolutional neural network CNN model, and predicting an optimal adjustment parameter according to the defect information and the historical sequence through a long short-term memory network LSTM model; the edge calculation and control unit is combined with a material characteristic database to generate a hierarchical calibration instruction in a millisecond level, adopts fuzzy PID self-optimization control to dynamically set PID control parameters, and is combined with feed-forward compensation of the inertial measurement unit to perform accurate, smooth and robust control on an execution mechanism, so that the accuracy of calibration is improved. And full-automatic, high-precision and real-time online calibration of the printing process in a complex industrial environment is realized.

The invention discloses a self-adaptive optical system parameter adjusting method and device combining offline pre-training and online reinforcement learning, and belongs to the field of intelligent control combining self-adaptive optics and reinforcement learning. Multi-modal state modeling, off-line strategy pre-training and an online strategy enhancement mechanism guided by behavior advantages are introduced, and key control parameters in the AO system are dynamically optimized by using a reinforcement learning algorithm. According to the method, multi-modal heterogeneous data can be effectively integrated, the dynamic modeling and prediction capability of the system is improved, and stable, efficient and robust control strategy updating is realized.

The invention relates to the technical field of airspace control, and discloses a low-altitude airspace precise control system and method for a low-altitude aircraft, and the system comprises an airspace digital twinning construction module, a multi-dimensional airspace distribution engine and a carbon constraint flight control module. When a low-altitude digital twin airspace is constructed, the system can solve the problem of signal distortion in airspace situation awareness by deploying a multi-source environmental interference filtering algorithm, analyzing waveform characteristics of sensor data in real time and dynamically identifying and eliminating the influence of electromagnetic interference and meteorological distortion on a monitoring signal; the authenticity and reliability of environment dynamic data acquisition are guaranteed, the construction precision of a digital twin model is improved, the accuracy of an airspace management and control decision is further guaranteed, and when dynamic monitoring of the flight path of the aircraft is implemented, a three-dimensional attitude real-time feedback mechanism is established, the space vector included angle between the actual course of the aircraft and a planned path is continuously calculated, and the real-time dynamic monitoring of the flight path of the aircraft is realized. Therefore, the system can sense the pose offset state in time, and the robust control capability of the low-altitude dense traffic flow is enhanced.

The invention relates to the technical field of transformer voltage regulation cooperative control, in particular to a transformer voltage regulation cooperative control system based on Internet of Things data acquisition, and the system comprises a heterogeneous control strategy execution unit which is used for generating an expected control instruction signal; the power grid regulation toughness quantification unit is used for collecting real-time control instruction data and calculating to obtain an accumulated toughness attenuation index; the data attack identification unit is used for synchronously acquiring an original voltage measurement signal and an expected control instruction signal generated by the heterogeneous control strategy execution unit; obtaining an attack correlation index; the control right switching decision unit is used for calculating to obtain a switching trigger value; generating a final control output; the heterogeneous control strategy execution unit is also used for responding to the final control output and performing switching execution between a deep reinforcement learning strategy and a classic robust control strategy; according to the invention, accurate evaluation of physical consequences caused by attack behaviors is realized, and equipment failure caused by overfatigue is avoided.

The invention provides an output feedback robust control method, system and device for a humanoid robot, and belongs to the technical field of humanoid robot control. The method comprises the following steps: establishing a kinetic model of a robot limb end system, and defining a state variable, a control variable and an output variable; determining a first robust control equation based on a state feedback optimal control principle, the kinetic model and an output feedback controller constructed by the output variables; converting the first robust control equation into a second robust control equation based on a mapping relation between a state variable and an output variable in the kinetic model, determining a to-be-estimated parameter vector according to the second robust control equation, and performing online estimation based on a preset adaptive law to obtain a control gain matrix; a second robust control equation is constructed based on the output variables; and based on the control gain matrix and the output variable obtained in real time, generating a corresponding control variable to be applied to the robot acra joint so as to carry out robust control on the robot acra system.

The invention relates to the technical field of intelligent control of power electronic converters and induction heating, in particular to an intelligent control system of a medium-frequency power supply, which comprises a data acquisition module configured to acquire end-side voltage data and end-side current data of power conversion hardware in real time; the feature extraction module is configured to receive the end-side voltage data and the end-side current data and extract broadband waveform distortion features; the state evaluation module is configured to receive the broadband waveform distortion characteristics and calculate a model trust entropy value based on the broadband waveform distortion characteristics; the self-adaptive control module is configured to receive the model trust entropy value, trigger a second control mode to generate a second switch instruction and send the second switch instruction to the power conversion hardware; the model trust entropy represents a prediction deviation degree of a preset robust control model to the current real physical state of the power conversion hardware; according to the invention, the problem of insufficient perception of fine-grained service parameters in the background technology is solved.

The invention discloses a humanoid robot joint motor control method with model prediction control, and relates to the technical field of robot motion control. The method is used for solving the problems of control instruction infeasibility and model disturbance caused by voltage saturation during high-speed motion. An error state space model of a joint motor system is established, and non-linear terms such as gravity are used as nominal feed-forward quantities for stripping; then, the rotating speed of the motor and the bus voltage of the driver are collected in real time, the back electromotive force is calculated, and the voltage margin is mapped into a dynamic torque physical limit value at the current moment; and meanwhile, estimating an unmodeled lumped disturbance value of the model by using an extended state observer. And finally, taking the dynamic limit as a real-time inequality constraint, introducing a disturbance value correction prediction equation, constructing and solving a quadratic programming problem, extracting an optimal compensation torque, superposing the optimal compensation torque with an inverse dynamic feedforward torque, and converting the superposed torque into a current signal to drive a motor, thereby realizing high-dynamic and high-precision robust control under the physical boundary constraint.

The invention relates to the technical field of heavy-load robot control, in particular to a self-adaptive robust control method of a heavy-load robot fusing differential homeomorphic mapping. According to the method, position information of each joint is collected in real time and accurately compared with an expected trajectory, a ternary input vector is output, differential homeomorphic mapping parameters are updated in real time through a T-S fuzzy inference system, and a joint space state is mapped to a new coordinate space based on the optimized parameters, so that the position information of each joint is accurately compared with the expected trajectory. Neighbor joint information is obtained through distributed communication to generate a cooperative control item, a sliding mode surface is constructed in combination with a local tracking error to generate a robust control item, a compensation item is generated based on adaptive law estimation parameter uncertainty, three-item parallel control output is formed, dynamic weight distribution and fusion are performed on the three control items, a basic control moment is obtained, and the control precision is improved. And then a feed-forward compensation item based on a nominal dynamical model is superposed to generate a final comprehensive control torque, and the comprehensive control torque optimizes energy distribution while ensuring the performance.

The invention belongs to the technical field of robot control, and discloses a visual servo intelligent robust control method for a mobile mechanical arm for a complex operation task, which comprises the following steps of: 1, acquiring a working scene image, and calculating a space coordinate of a target feature point in real time through a projection transformation model; step 2, dynamically inhibiting visual measurement noise by adopting adaptive Kalman filtering, generating a feedforward compensation signal through an integral sliding mode observer, and decoupling chassis slippage disturbance; step 3, inputting the pose signal into a radial RBF neural network, designing an RBF gain scheduler, and adjusting an output proportion-integral gain in real time to suppress external time-varying disturbance; 4, adopting a hybrid visual servo mode switching mechanism, and generating a mobile platform control instruction through an integral sliding mode surface in a position servo mode; and 5, designing a three-order composite controller to drive the mechanical arm so as to realize accurate grabbing. According to the method, the grabbing deviation caused by kinematics uncertainty of the mobile platform and dynamic environment disturbance is eliminated.

The invention discloses a self-adaptive robust control method for a pipeline robot, and belongs to the technical field of robot control. The method comprises the following steps: firstly, establishing a high-fidelity dynamic model considering a pipeline inclination angle and nonlinear friction; on this basis, a hierarchical control architecture is designed, which operates a high gain state observer in parallel to estimate speed, a finite time disturbance observer (FTDO) to quickly estimate and compensate lumped disturbance, and an adaptive nonsingular terminal sliding mode controller (NTSMC) to ensure finite time convergence of tracking errors. An adaptive law is introduced into a control law, robust gain is adjusted online to suppress residual disturbance, and the upper bound of disturbance does not need to be predicted. And an anti-saturation mechanism and a boundary layer flutter suppression strategy are integrated, so that the smoothness of a control signal and the safe operation of an actuator are ensured. According to the method, the problem that the control precision and robustness of the pipeline robot are insufficient due to parameter uncertainty, nonlinear friction and external disturbance in a complex environment is effectively solved.

The invention belongs to the technical field of bioengineering, and particularly relates to a biomass fermentation safety reinforcement learning control method and system, and the method comprises the steps: training an initial strategy network through imitation learning by employing historical data, and providing safety weight initialization for a reinforcement learning agent; and constructing a virtual training environment fusing the mechanism model and the data-driven residual error correction network, applying domain randomization, training an agent in the environment to maximize long-term accumulated rewards, and obtaining a robust control strategy. A training strategy is deployed to a real fermentation system, a control instruction is corrected in real time through an independent safety layer according to a hard constraint rule to ensure operation safety, and meanwhile online fine tuning and updating are conducted on the strategy based on actual operation data. According to the method, safe and smooth transition from virtual training to practical application is realized, and the final ethanol concentration and the overall control performance in the fermentation process are remarkably improved.

The invention provides a high-order robust control method for a mobile robot in combination with a weighted double-Q learning algorithm. The high-order robust control method comprises the following steps: step 1, constructing an intelligent trolley motion scene including time-varying friction, variable gradient, random external disturbance and sensor noise complex uncertainty; step 2, designing a weighted double-Q learning adaptive nonlinear expansion state observer; step 3, based on the step 2, constructing a reward function including a trajectory tracking error, a disturbance estimation error and a stability constraint; 4, designing an improved super-spiral sliding mode control law based on the weighted double-Q learning adaptive nonlinear extended state observer; and 5, setting a plurality of groups of working condition simulation comparison schemes, and carrying out simulation experiment verification. According to the invention, real-time adaptive control of the mobile robot on different working conditions is realized, the trajectory tracking precision of the system is improved, and sliding-mode control buffeting is inhibited at the same time.

The invention relates to the technical field of sidewall contact force control, and discloses a well wall sidewall contact force control method, which comprises the following steps of: acquiring fluid pressure gradient data, synchronously acquiring contact stress distribution of a working surface, performing synchronous association, acquiring associated data of time-space alignment, and performing fusion processing to eliminate irregular fluctuation; a smoothed pressure and stress correlation data set is obtained; the local stress concentration state is detected in real time, and a control instruction used for adjusting the displacement of the contact execution mechanism is generated; constructing a nonlinear relation model between a pressure and stress associated data set and contact force output, embedding a dynamic compensation mechanism, correcting and fusing the input real-time data to generate a collaborative feedback quantity, and adjusting the displacement of the contact execution mechanism in real time through the collaborative feedback quantity. And the response delay in the adjustment process is compared with a preset time range, and parameters of the dynamic compensation mechanism are adjusted according to a comparison result to form closed-loop control. According to the invention, high-precision and high-robustness control of the sidewall pushing force can be realized.