96 results about "Non linear model predictive control" patented technology

The invention relates to a multi-cell control method for hydrogen production alkaline electrolytic cells based on nonlinear model predictive control, which belongs to the technical field of hydrogen production and comprises the following steps of: performing data acquisition on key positions of a hydrogen production alkaline electrolytic cell system in real time; based on the dynamically determined priority of each electrolytic cell, dynamic load distribution of each electrolytic cell is carried out, and a multi-dimensional cooperative regulation strategy is implemented to obtain a hydrogen yield target value of each electrolytic cell; constructing a nonlinear model comprising an electrolytic reaction model, a heat transfer model and an inter-tank coupling model, and performing parameter identification and verification; and designing a nonlinear model predictive control algorithm, performing discretization processing on the nonlinear model, introducing Kalman filtering to process the uncertainty of the model, taking a corresponding hydrogen yield target value reached by each electrolytic cell on the premise of meeting the constraint as a final control target, constructing an optimized target function, and performing solving to obtain an optimal control scheme. Compared with the prior art, the control precision and the system stability can be effectively improved.

The invention discloses a mobile robot accurate docking method based on edge calculation, and aims to solve the problems that the dynamic docking precision is reduced and the collision risk is increased due to micro-motion or drifting of a target station. According to the method, unified time reference alignment is carried out on data of a camera, a laser radar, an inertial measurement unit, an ultra-wideband range finder, a station encoder and a programmable logic controller at an edge node, and a three-dimensional special Euclidean group equivariant multi-source fusion network is used for outputting relative pose estimation and covariance; the estimation in the time window is further used as a condition to be input into a conditional diffusion short-time prediction model to obtain a time-varying mean value and a time-varying covariance, an anisotropic probability tube is constructed, and prediction-measurement joint correction is carried out based on a score function; scenarized opportunity constraints are constructed under correction probability tube constraints, a tubular nonlinear model is adopted to predict, control and solve a reference trajectory, an actuator command is generated in combination with depth visual servo and compliance control, and the technical effects of high precision, robustness and safe docking under the station dynamic disturbance condition are achieved.

The invention discloses a dynamic calibration control method for the detection precision of a non-standard automatic testing fixture, and particularly relates to the field of industrial automation and control, which comprises the following steps of: constructing a real-time dynamic error field model of the testing fixture; a distributed optical fiber sensing network and a miniature laser interferometer cooperatively collect real-time detection data and spatial-temporal variation errors under a multi-physics coupling effect, a precision degradation track is predicted based on spatial-temporal variation error data, a pre-compensation signal is generated, and a self-adaptive Kalman filtering algorithm is adopted to generate a precision compensation parameter. Environment temperature, vibration and electromagnetic interference data are monitored in real time, environment disturbance data are generated, a hybrid controller combining nonlinear model predictive control and reinforcement learning is adopted, multi-source data are fused, a calibration instruction is generated through multi-target optimization, and an execution mechanism is driven to achieve online dynamic calibration. And digital threads and transfer learning are integrated to realize full-life-cycle data closed-loop management and continuous optimization.

The invention discloses six-degree-of-freedom wave compensation embarkation equipment suitable for complex sea conditions and a control method of the six-degree-of-freedom wave compensation embarkation equipment. The six-degree-of-freedom wave compensation embarkation equipment comprises a base platform fixed to an operation ship deck; a lower platform of the Stewart parallel mechanism is connected with the base platform, and an upper platform of the Stewart parallel mechanism is connected with the lower platform through six hydraulic cylinders; the embarkation gangway ladder system is mounted on the upper platform; the hydraulic servo driving system is used for driving the six hydraulic cylinders; the intelligent sensing system is used for collecting ship motion data and relative pose data of the target platform; the intelligent control system is electrically connected with the hydraulic servo driving system and the intelligent sensing system; wherein the intelligent control system is configured to execute a compound control algorithm based on a nonlinear extended state observer and nonlinear model predictive control so as to drive the hydraulic servo driving system and compensate ship motion. The embarkation equipment provided by the invention has the advantages of high precision, high stability and strong robustness, and can realize accurate and stable compensation of complex sea conditions.

The invention discloses a multi-mode nonlinear model predictive control method and system for a visual servo-driven arm-carrying unmanned aerial vehicle, and belongs to the technical field of intelligent control of arm-carrying unmanned aerial vehicles. The objective of the invention is to solve the problems of unstable control mode switching and difficulty in unified control modeling of different target structures in existing unmanned aerial vehicle grabbing control. According to the technical scheme, a task-driven modal judgment mechanism is introduced, the current task modal can be judged in real time based on the target type, the image features and the flight state information, control strategy self-adaptive switching between a rod-shaped target and a plane target is achieved, and continuity and stability of control logic are guaranteed. For a rod-shaped structure target, a remote approaching, servo adjustment and compliant contact three-stage control strategy is designed, and a dynamic weight adjustment mechanism is introduced into NMPC control solution, so that smooth transition of control performance from quick response to fine compliant is realized, and the control precision and compliance of the arm-carrying unmanned aerial vehicle in different operation stages are remarkably improved. For a plane structure target, the invention provides an adsorption control strategy based on impact time prediction and angle feedforward compensation, the contact time can be accurately estimated, the attitude of the aircraft can be adjusted in advance, the adsorption stability is effectively improved, and the contact failure probability caused by inclination of a target surface is reduced.

The invention relates to a robot dog dynamic balance and fall self-recovery method and device for a complex terrain, and the method comprises the steps: building a robust local elevation map through fusing multi-sensor data and quantifying the motion uncertainty of a robot; and a safe foot region set defined by the convex polygon is extracted through geometric feature analysis and plane segmentation. Then, collaborative planning of the trunk and the landing point is carried out in combination with a user instruction and the region set. In a control layer, a nonlinear model predictive control algorithm is adopted, a safety area is used as a hard constraint, and incomplete speed constraint is applied to a wheel type foot end, so that an optimized trunk track and a foot end force reference are output. The whole-body controller converts an upper-layer instruction into an accurate joint torque through a multi-task optimization framework with a weight. When instability is detected, the system can autonomously plan and execute a recovery action sequence including curling, controllable rolling and cooperative supporting.

The invention discloses a decoupling motion control method and system for a four-footed mobile operation robot considering the acting force of a mechanical arm, and belongs to the field of motion control of foot type mobile operation robots. An expected motion track input by a user can be tracked by performing planning through linear model prediction control MPC; on the basis of the tracked motion trail, mechanical arm joint control torque is calculated through a mechanical arm dynamic model and PD feedback; through nonlinear model predictive control NMPC planning, a quadruped robot whole-body motion trail of mechanical arm acting force obtained through calculation according to a mechanical arm dynamic model is considered, and an expected speed trail and an expected force trail input by a tracking user are obtained; and according to an expected speed trajectory and an expected force trajectory input by a tracking user, a whole body controller WBC based on hierarchical quadratic programming calculates a joint driving torque for tracking the expected trajectory according to task priorities. According to the method, the acting force / torque of the mechanical arm to the robot body is considered during motion control of the quadruped robot, and decoupling control over the mechanical arm and the quadruped robot in the quadruped mobile operation robot is achieved.

The invention belongs to the technical field of control, and discloses a water surface robot high-precision trajectory tracking control method and related equipment, and the method comprises the steps: constructing a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; according to the dynamic multi-modal environment map, an improved quantum particle swarm optimization algorithm embedded into a water flow kinetic equation constraint is adopted to carry out global path planning, smooth parameterization processing is carried out on a global path obtained through planning, and a reference trajectory is generated; according to the vector information of the water flow field, flow field disturbance is predicted based on a fluid-structure interaction dynamics model, system residual disturbance is estimated by using an extended state observer, and feedforward control quantity is generated in combination; according to the real-time flow velocity of the water flow field and the trajectory tracking error, a nonlinear model prediction controller and an adaptive sliding mode controller are adaptively switched, and a control instruction is generated in combination with a feedforward control quantity to drive the water surface robot to track a reference trajectory; therefore, high-precision trajectory tracking control is realized.

The invention requests to protect a multi-sensor fusion adaptive motion control system and method for a boat-type lotus root harvester, and the system comprises a multi-sensor array which is used for obtaining the position, posture, speed, inclination angle and motor load information of the lotus root harvester, and comprises a GPS, an IMU, a current sensor and an encoder; the microcontroller is used for running an extended Kalman filter (EKF) data fusion algorithm, a multi-mode PID self-adaptive control algorithm, a pure tracking path planning algorithm and a nonlinear model prediction control algorithm, and comprehensively considering path tracking precision, control smoothness and multiple constraint conditions in a prediction time domain through a rolling time domain optimization strategy; generating an optimal control instruction according to the state estimation; the executing mechanism comprises a walking motor and a steering engine and is used for responding to the control instruction and driving the lotus root harvester to move; the invention discloses a Simulink hardware-in-the-loop verification system.

The invention relates to a robot motion control method and system based on centroid dynamics and hierarchical optimization, and the method comprises the steps: building a centroid dynamics model of a four-wheel-foot robot, and constructing a nonlinear model prediction control optimization problem according to the centroid dynamics model in combination with centroid-joint coupling dynamics constraint, wheel-ground rolling constraint and auxiliary constraint; solving by adopting a real-time solving strategy based on discretization and iterative optimization to obtain an optimal state variable and an optimal input variable; and based on the optimal state variable and the optimal input variable, the optimal torque and the wheel angular velocity of the robot are solved through a layered optimized whole-body control strategy. According to the method, control errors caused by model simplification are avoided, and the overall motion performance and robustness of the system in a complex scene are remarkably improved by fusing a high-precision dynamic model and hierarchical optimization control.

A self-adaptive cruise and torque distribution cooperative control method for a distributed driving electric vehicle comprises the following steps: establishing a two-degree-of-freedom vehicle dynamics model, combining a nonlinear tire model and a longitudinal load transfer effect, establishing a longitudinal motion control model, adopting a permanent magnet synchronous motor, introducing a motor efficiency MAP graph, and predicting a control algorithm by using a multi-target optimization model. And constructing a multi-state variable optimization model, dynamically adjusting a weight coefficient, introducing a relaxation factor, converting a problem into a quadratic programming problem, and generating an optimization control instruction. And designing a nonlinear model predictive controller to calculate an optimal direct yaw moment, determining a torque distribution coefficient, solving a target function for minimizing the total loss power of the motor through quadratic programming, and finding an optimal torque distribution coefficient to be applied to the motor. And a dynamic model, a longitudinal motion control model and a nonlinear model predictive controller are integrated to form a comprehensive vehicle control system and realize closed-loop control iteration, so that the safety, comfort and energy efficiency of the vehicle under complex working conditions are improved.

The invention relates to the field of remote control, and discloses an intelligent bucket throwing operation method and system for remotely controlling a grab ship unloader, which are used for realizing intelligent bucket throwing operation for remotely controlling the grab ship unloader. The intelligent bucket throwing operation method for remotely controlling the grab ship unloader comprises the steps of generating a full-state expected trajectory containing position, speed and acceleration information, ensuring that the trajectory is smooth and accurate, constructing a nonlinear model prediction controller, integrating a nonlinear disturbance observer, effectively estimating and compensating wind disturbance and unmodeled dynamics, and enhancing the anti-jamming capability of the system. A discrete element method is utilized to simulate interaction of material particles, and in combination with bilge topographic data, spatial distribution after material throwing is accurately predicted, system stability is monitored in real time, and a control instruction is dynamically adjusted. The precision, the stability and the efficiency of bucket throwing operation of the grab ship unloader are remarkably improved, the operation difficulty and the labor cost are reduced, and the grab ship unloader can well adapt to complex working conditions.

The invention discloses an optimization-based double-unmanned aerial vehicle hoisting rigid load path planning algorithm, and relates to the technical field of unmanned aerial vehicles. The method comprises the following steps: constructing a coordinate-free dynamic model, and describing positions and postures of a load and an unmanned aerial vehicle; generating a smooth trajectory by adopting differential flatness; initializing a trajectory through an A * algorithm constrained by a dynamical model, and optimizing trajectory parameters in combination with full-state security constraints and dynamic feasibility constraints; adopting nonlinear optimization to generate a dynamic feasible track; through nonlinear model predictive control, the state is predicted in real time, control input is generated, and an optimized trajectory is tracked. According to the invention, a path planning algorithm based on optimization of the double unmanned aerial vehicles for cooperatively hoisting the rigid load is established, the method is applied to actual testing, stable path planning is realized, and the problem of path planning of the unmanned aerial vehicles for hoisting the rigid load is solved.

The invention discloses an underwater robot global positioning and trajectory tracking method based on a hull NURBS curved surface. The method comprises the following steps: firstly, constructing a hull NURBS curved surface model, and extracting differential geometric parameters including a curved surface basis vector, a curved surface normal vector, a curved surface first basic form matrix and a Cristomide symbol; then, on the basis of the differential geometry theory, according to the hull NURBS curved surface model, a continuous time nonlinear kinematics model is constructed; and finally, constructing an extended Kalman filter based on the continuous time nonlinear kinematics model. Based on an extended Kalman filter, fusing the multi-modal sensing data to perform global positioning, and obtaining a posteriori estimation state vector at the current moment; and performing curved surface trajectory tracking based on nonlinear model predictive control according to the posteriori estimation state vector at the current moment. The problem that the trajectory tracking precision is low or fails due to the fact that the robot is prone to accumulative drifting in positioning and cannot achieve global positioning in underwater, curved-surface, GPS-free and external-vision-free denial environments is solved.

The invention provides a cooperative control method suitable for a thermoelectric unit, and relates to the technical field of thermoelectric units, and the method comprises the steps: constructing a machine-furnace coupling nonlinear state space model, designing a dynamic heat storage state observer, calculating a heat storage state index in real time to quantify the transient energy profit and loss of the unit, and building a multi-objective optimization function. And a dynamic weighting factor based on the index is introduced, the factor guides the controller to automatically switch strategies under different working conditions according to the priority of the real-time energy state, the intelligent balancing load response speed and the main steam pressure stability, and finally, a nonlinear model predictive control algorithm is adopted to solve in a rolling time domain, so that the real-time energy state is obtained. According to the method, the optimal steam turbine control valve and fuel quantity control increment is obtained, decision-making level deep cooperation of a turbine-boiler system is effectively achieved, the problem of divergence control under deep peak regulation is solved, the frequency modulation potential of a unit is released to the maximum extent on the premise that absolute safety of pressure is guaranteed, and the response rate of a power grid is increased.

The invention belongs to the technical field of robot control, and discloses a foot-arm multi-task control method and system based on time-varying terminal cost, and the method comprises the steps: generating a whole-body reference trajectory of a robot offline, and pre-calculating a time-varying terminal cost matrix sequence based on the reference trajectory; based on the time-varying terminal cost matrix sequence, constructing and solving a nonlinear model predictive control optimization problem to obtain an optimization sequence of a system state and control input; and the optimization sequence serves as a reference instruction and is input into a layered whole-body controller for multi-task priority optimization, and a joint control instruction is generated and output to a robot execution mechanism. According to the method, the problem of short vision of a traditional NMPC in a long-time task is effectively relieved, and the operation precision of the robot is remarkably improved.

The invention discloses an intelligent vehicle stability and trajectory tracking cooperative control method in a tire burst scene, and belongs to the technical field of unmanned vehicle control. The invention aims to provide a cooperative control method for stability and trajectory tracking of an intelligent vehicle in a tire burst scene, which is based on a nonlinear model predictive control (NMPC) strategy and fuses nonlinear mechanical characteristics of tires and vehicle state prediction. According to the method, firstly, a vehicle second-order reference model is designed, then a complete vehicle dynamics model is established, and finally, an NMPC control framework with the three-degree-of-freedom vehicle dynamics model as the core is established. According to the method, based on a nonlinear model predictive control strategy, nonlinear mechanical properties of the tire and vehicle state prediction are fused, and vehicle instability can be effectively inhibited while accurate trajectory tracking is achieved.

The application discloses an inverter nonlinear composite control method and system based on a PR controller and belongs to the field of power electronics. In view of the problems that the existing control scheme is weak in control performance or even fails when facing actual inverter parameter uncertainty, external disturbance during grid connection or load access operation and the like, Taylor series is adopted to solve the problem that a nonlinear model predictive control structure is complex and difficult to implement, and a nonlinear disturbance observer is introduced to improve control accuracy, so that the finally constructed grid-connected inverter nonlinear composite control inverter has the advantages of simple control structure implementation and convenient actual application while being capable of realizing good tracking capability on a reference signal and rapid inhibition capability on external interference.

This application discloses a digital twin control method for facility poultry farming environments based on deep spatiotemporal data fusion, relating to the field of poultry farming technology. It proposes a spatiotemporal sequence prediction model for environmental parameters using a bidirectional long short-term memory network (VMD-Attention-BiLSTM) that integrates variational mode decomposition and attention mechanisms, effectively overcoming the system's large time lag. A digital twin decision engine based on nonlinear model predictive control is constructed, and the optimal control sequence for energy consumption and environmental quality is solved under multiple constraints by establishing a joint state equation for thermodynamics and gas diffusion. Specifically, for complex winter conditions, a ventilation-heating collaborative game strategy based on real-time heat loss compensation is designed. Adaptive sliding mode control with radial basis function neural network compensation is introduced into the underlying execution unit, significantly enhancing the system's anti-disturbance capability. This achieves precise environmental control under complex conditions, improving poultry welfare and energy efficiency.

This invention discloses an adaptive portable pipe external wall cleaning mechanism and control method suitable for different pipe diameters, belonging to the technical field of pipe maintenance equipment. The mechanism includes a grinding mechanism, a cylinder, a scanning device, and a clamping and moving mechanism. This invention employs a centralized-distributed intelligent control system and method, where the centralized unit uses a nonlinear model predictive control algorithm based on Koopman operator theory to dynamically map the system nonlinearly to a high-dimensional linear space for rolling optimization; the distributed unit is responsible for the real-time control of each actuator. This system updates model parameters through an online adaptive mechanism, significantly improving control accuracy and robustness in complex nonlinear and time-varying operating environments. This invention solves the problems of poor adaptability, low efficiency, and insufficient intelligence in traditional pipe external wall cleaning methods, achieving efficient, high-quality, and fully automated pipe external wall cleaning operations.

The invention provides an automatic driving vehicle lane changing method considering surrounding vehicle interaction. The method comprises the steps of obtaining state information of an automatic driving vehicle and other vehicles in the same scene at the current moment; driving behavior characteristics are analyzed, a fuzzy reasoning method is adopted to estimate a vehicle aggressiveness coefficient, and a social potential field model considering a driving style is constructed; based on collision risk assessment and vehicle interaction behavior analysis, constructing a non-cooperative game model, and solving non-cooperative game equilibrium to obtain an optimal lane changing decision and acceleration; taking the longitudinal acceleration of the lane changing decision variable output by the game layer as a reference input signal of an NMPC controller; and based on the vehicle kinematic model and the potential field information, generating a driving track of the autonomous vehicle by using an NMPC controller and outputting corresponding motion planning information. The upper layer makes a decision through a game model, and the lower layer performs transverse and longitudinal coupling planning based on nonlinear model predictive control, so that smooth and controllable path tracking is realized, and the real-time performance and stability of the system are improved.

This invention relates to a robust model predictive control method for urban wastewater treatment processes under random sampling and continuous packet loss constraints, belonging to the field of intelligent control technology for wastewater treatment processes. Addressing the challenges of existing technologies, such as random sampling of dissolved oxygen concentration, continuous packet loss in data transmission, and significant external disturbances, this invention establishes a probability distribution model with an equivalent random sampling interval and constructs multiple prediction models based on a fuzzy neural network to obtain predicted outputs for dissolved oxygen concentration. Secondly, a disturbance state observer is designed to estimate system uncertainties online, obtaining disturbance prediction outputs. Finally, by fusing the predicted output information and the disturbance prediction output information, a robust model predictive controller is designed, achieving stable control of dissolved oxygen concentration. Compared to traditional PID control and nonlinear model predictive control methods with fixed-period sampling, this invention effectively solves the problems of insufficient stability and weak anti-interference capability of the control system, achieving stable control of dissolved oxygen concentration.

The invention discloses a rehabilitation robot auxiliary control system and method based on multi-modal perception, and belongs to the field of robot auxiliary rehabilitation and mobile support. The method comprises the following steps: acquiring and processing tactile interaction information between a user and a robot, and calculating contact force, torque and a contact position; in order to ensure stable and reliable support, centroid dynamics is used for modeling a human body state, and nonlinear model predictive control is used for optimizing a sequence of contact force and a contact position; inputting the contact force, the torque and the contact position into an admittance controller, generating a track adjustment amount, and performing adaptive correction on the reference track; solving a joint angular velocity control instruction of the robot by adopting a quadratic programming method; the joint position, the speed limit and the self-collision avoidance constraint are strictly included in the quadratic programming problem, and the safety of the whole interaction process is ensured. The walking ability can be remarkably improved, emergency situations such as falling down can be effectively handled, and the walking aid is particularly suitable for the field of elderly nursing.

The application discloses a robot imitation learning method and device based on a hybrid perception nonlinear model predictive control, and the method comprises the following steps: collecting an external perception picture sequence and an ontology perception information sequence to obtain a first data set; sampling the first data set according to a preset field of view length; performing static kinematics coding and dynamic space-time coding processing on the ontology perception information sequence in the sampled data set to obtain a second data set; constructing a composite loss function; inputting the second data set into a prediction model, outputting a prediction sequence through a nonlinear model predictive control algorithm; according to the prediction sequence, a robot performs an action to complete an imitation learning task; and according to the composite loss function, the prediction model is updated, and the step of collecting the external perception picture sequence and the ontology perception information sequence to obtain the first data set is returned. The application can improve the reasoning speed and the imitation learning task accuracy, and can be widely applied to the technical field of robot imitation learning.

This application relates to an adaptive disturbance rejection control method and system for aircraft used in offshore wind power operation and maintenance. The method includes receiving wind field data from a lidar terminal mounted on the aircraft in real time and acquiring wake field data of the wind turbine to be repaired / tested; fusing the wind field data and wake field data and projecting them onto a three-dimensional grid space in the aircraft's body coordinate system to generate a three-dimensional disturbed wind field grid map; inputting the three-dimensional disturbed wind field grid map into a preset nonlinear model predictive controller and acquiring the aircraft's flight state data in real time; the nonlinear model predictive controller predicts, based on the aircraft's current flight state data, that the aircraft will pass through grid points in the three-dimensional disturbed wind field grid map within the next n seconds; and calculating the feedforward compensation amount of the aircraft's power system based on the disturbance data of the grid points to be passed. This application has the effect of improving the aircraft's disturbance rejection kinetic energy in complex airflow environments around offshore wind turbines.

The application belongs to the technical field of industrial robots, and discloses a wall-climbing machining robot curved surface motion control method and system, which comprises the following steps: (1) establishing a robot kinematics model under the constraint of instantaneous motion plane; (2) regarding the model error caused by the kinematics model uncertainty and external disturbance as an extended state, then constructing an extended state observer to observe the kinematics model error of the robot, and obtaining an error compensation control signal; on the basis of not considering the kinematics uncertainty, establishing a kinematics error state space model of the robot trivial system, and establishing a nonlinear model predictive controller, then solving the optimal control sequence of a cost function to obtain the control signal of the trivial system; (3) calculating the control signal of the closed-loop control system of the robot, and then realizing the motion control of the wall-climbing machining robot on the curved surface in three-dimensional space. The application provides protection for efficient and high-precision machining of the wall-climbing machining robot.

The application provides a quad-rotor unmanned aerial vehicle anti-disturbance control method, and specifically comprises the following steps: S1, a nonlinear model predictive controller outputs expected angular velocity to an angular velocity model reference adaptive controller; S2, the nonlinear model predictive controller outputs expected total thrust to a thrust model reference adaptive controller; S3, a rigid body dynamics theory is used to obtain a quad-rotor dynamics model, a collision impact disturbance analysis is performed on the angular velocity model reference adaptive controller, body torque instructions are output according to the quad-rotor dynamics model, a load uncertainty analysis is performed on the thrust model reference adaptive controller, and total thrust instructions are output according to the quad-rotor dynamics model; S4, a control distribution module is used to map the body torque instructions and the total thrust instructions to thrust instructions of each rotor of the quad-rotor unmanned aerial vehicle; and S5, the calculated thrust instructions are transmitted to the quad-rotor for execution.

The invention discloses an intelligent self-adaptive gearbox system based on a multi-mode fusion control theory and a control method of the intelligent self-adaptive gearbox system, and belongs to the technical field of vehicle transmission systems. The system adopts an innovative four-layer intelligent control architecture, and comprises a basic transmission layer used for hardware interaction; the state sensing layer is used for realizing dynamic balance, multi-physics field efficiency evaluation and working condition identification through wavelet transformation and based on an efficiency pulse spectrogram pre-established by finite element and computational fluid dynamics joint simulation and a support vector machine algorithm; the intelligent decision-making layer is used for generating a globally optimized gear shifting instruction through the cooperation of nonlinear model predictive control, graph theory A search and a time sequence planning algorithm; and the adaptive optimization layer realizes health prediction and strategy persistent evolution through a hierarchical reinforcement learning framework of a hidden Markov model and offline experience playback. Through engineering fusion of a multi-mode theory, the technical problems that an existing gearbox control strategy is rigid and is lack of self-adaption and predictive capacity are solved.

The invention discloses a self-adaptive admittance control aerial operation robot and a control method and system thereof.According to the control method, a full-drive aerial operation robot is regarded as an integrated aerial actuator, and then an unmanned aerial vehicle-mechanical arm integrated system model is constructed; introducing an adaptive admittance controller as an outer loop force controller, and designing an adaptive admittance control algorithm based on the Lyapunov stability theory; and a nonlinear model predictive control method based on an integrated model is designed, and sliding force output of the platform can be restrained. Therefore, by means of a motion control method and a self-adaptive admittance control algorithm of the all-drive aerial robot platform, it is guaranteed that the tail end sliding operation mechanism stably slides on the contact operation surface, and aerial precise sliding control and force interaction operation under the complex and unknown environment are achieved.

The application discloses an intelligent battery thermal management system based on thermoelectric coolers, and relates to the technical field of new energy and electronic engineering.The system comprises a TEC optimization layout module, a micro-channel heat dissipation module, a detector preparation module and an NMPC control module.The TEC optimization layout module is responsible for optimizing the position and contact area of the thermoelectric cooler (TEC), analyzing the internal temperature distribution of the battery unit, determining the heat accumulation area, and embedding a heat-conducting metal plate between the battery units by arranging the TEC near the top of the battery, so that the internal heat balance is promoted by using the cooling capacity of the TEC.The micro-channel heat dissipation module is responsible for establishing a micro-channel radiator, and the double goals of compact structure and high-efficiency heat dissipation are achieved by adopting the micro-channel technology, so that the battery is in an ideal temperature zone.The detector preparation module is responsible for the preparation process of a nickel / gallium oxide / silicon carbide metal oxide, a semiconductor field effect detector, and real-time monitoring of the battery temperature.The NMPC control module is responsible for predicting and optimizing the energy consumption in the battery thermal management system by using a nonlinear model predictive control (NMPC) algorithm, and dynamically adjusting the TEC operation strategy according to the current battery working condition and future prediction.