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

The invention discloses a humanoid robot whole-body coordination control method based on a mass center dynamical model. The method comprises the steps that system modeling is conducted on a humanoid robot according to the mass center dynamical model; establishing a relationship between arm momentum and centroid momentum based on whole-body kinematics and a momentum conservation principle; a whole nonlinear optimization control problem is constructed and solved in real time, and a controller predicts the motion trail of the robot in a future period of time; feedforward torque is obtained through inverse dynamics calculation, and joint torque is issued in combination with a joint PD controller. The invention designs a nonlinear model predictive control problem for whole-body coordination control of the humanoid robot, reasonably plans a system state, an arm tail end state, a foot end state and a reference trajectory of control input, and designs equality constraints and inequality constraints necessary for whole-body coordination movement of the robot; task trajectory constraint and momentum conservation constraint on the tail end of the arm are added, so that the ability of the arm to assist in motion balance is more prominent.

The invention relates to the technical field of automatic driving, and particularly provides an automatic driving vehicle track re-planning method, which comprises the following steps of: firstly, evaluating stability risk and defining three states of vehicle stability through real-time vehicle dynamic parameters, and designing intervention and exit criteria of track re-planning according to the three states; when the vehicle driving is evaluated to be in an unstable state, generating a virtual obstacle by using prediction information of a path tracking module, and converting a dynamic stability boundary into a spatial constraint; performing trajectory re-planning by adopting nonlinear model predictive control, and adjusting the weight between the path tracking precision and the obstacle avoidance demand according to the relative position of the virtual obstacle in combination with a dynamic weight adjustment mechanism; and finally, generating an optimized trajectory containing stability constraint, and realizing high-precision tracking through linear MPC. The method can effectively balance the path tracking precision and the vehicle stability under a complex working condition, and prevents the vehicle from entering a dynamic unstable region.

The invention belongs to the technical field of control, and discloses a multi-axis mechanical arm predictive control method based on an information physical neural network. Comprising the following steps that (1) a nonlinear dynamic mechanical arm model is established and configured based on the physical relation between all shafts of the mechanical arm; 2) based on the mechanical arm model, randomly sampling by using a Sobol sequence to generate data, and training the data by using a discrete physical information neural network PINNs; 3) screening data generated by training, and predicting a next state in a noise environment by using an extended Kalman filter; (4) according to the reference trajectory, a nonlinear model is used for predictively controlling the mechanical arm to conduct trajectory tracking, and an optimal control sequence is generated; and (5) according to the current state of the reference track, the optimal control input of the extended Kalman filter is updated, the step (4) and the step (5) are cyclically iterated, and real-time control over the mechanical arm is completed. The position and the speed of the multi-axis mechanical arm can be controlled in real time, smaller errors are obtained, and better control capacity is displayed.

The invention discloses a robot dynamic path tracking method and system, and the method comprises the steps: constructing a nonlinear model prediction control model for a differential robot chassis; building a target cost function by considering the obstacle environment information around the robot; adjusting a control input vector in the nonlinear model predictive control model, performing real-time tracking control on the robot, calculating a target cost function value, taking the corresponding control input vector as an optimal control input vector when the target cost function value is minimum, and performing real-time tracking control on the robot. Performing dynamic path tracking control on the robot by using the optimal control input vector; the method has the advantages that real-time obstacle avoidance is reliably carried out while the robot tracks the path, complicated path planning calculation steps are not needed, the calculation process is simple, and the efficiency is high.

The invention discloses a humanoid robot step terrain walking method based on a whole-body kinetic model, and the method comprises the steps: designing a nonlinear model prediction control problem oriented to the walking of a humanoid robot, and reasonably planning a system state, input and a reference track of a foot end; equality constraints and inequality constraints which are necessary for walking of the robot are designed, and the constraint that a landing point of the robot needs to fall in a landing area is taken as one of the inequality constraints, so that the humanoid robot is capable of walking on a step terrain; and finally, solving a nonlinear model predictive control problem in real time by utilizing a solver, and issuing a robot joint torque according to an inverse kinetic equation of the whole-body kinetic model and PD in combination with a feedforward controller, so that the robot is better in control effect and more stable in walking.

The invention provides a full-scale humanoid robot coordination control method and system, and the method comprises the steps: building a nonlinear model prediction controller, carrying out the optimization solution through sequential quadratic optimization, and optimizing the system input and system state; a trunk pose task, a swing leg and support leg task and an arm task are extracted from the optimized trajectory, and a plurality of tracking tasks are mapped to a joint space according to an allocated priority weight by using a high-frequency hybrid whole body motion control HWBC algorithm, so that the robot can coordinately process the motions of the arms and the feet, the system response is improved, and the system reliability is improved. The method has the ability of multi-gait robust motion.

The invention relates to a rigid-flexible trans-mass self-adaptive robot force position and rigidity collaborative optimization method and system, and relates to the technical field of robot control, and the method comprises the steps: firstly, carrying out the linearization processing of a robot dynamic model through a semi-implicit integral method, and obtaining a robot dynamic model; and in combination with an extended Kalman filter, stiffness parameters in the contact operation process are estimated in real time, a dynamic stiffness sensing model is constructed, and the system response capability is effectively improved. Then, a rigidity parameter is embedded into a Hertz contact model, the dependence of a traditional model on prior information of a contact surface is broken through, a dynamic interaction model between the robot and an operation object is established, and high-precision real-time sensing of normal force and friction force is achieved. And finally, under a nonlinear model predictive control framework, multi-dimensional physical constraints are constructed based on the contact force, the position and the contact rigidity, an optimal control model is obtained, control input is dynamically and adaptively updated through rolling optimization, and the stability and the control precision of the robot in rigid-flexible heterogeneous contact operation are improved.

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 reconfigurable distributed driving multi-axis vehicle trajectory tracking method and a carrying device. The method comprises the following steps: acquiring longitudinal and transverse movement speeds and yaw velocity of a vehicle body in real time; acquiring position information and target path information of a vehicle, and matching a trajectory tracking target point; establishing a vehicle three-degree-of-freedom dynamic model for trajectory tracking; according to a longitudinal dynamic model of the vehicle, a sliding mode control method is adopted to solve longitudinal force needed by the whole vehicle, and the effect of tracking the specified speed is achieved; a tracking error model is obtained, and the rotation angle of each wheel and the yaw moment of the whole vehicle are calculated based on a nonlinear model predictive control algorithm; distributing the longitudinal tire force of each wheel according to the definition of the tire adhesion rate and the requirements of the longitudinal force and the yawing moment of the whole vehicle output by the upper-layer controller, and converting the longitudinal tire force into motor torque; and controlling a steering system and a wheel motor of the vehicle to execute the wheel steering angle and torque instruction. The control stability and trajectory tracking precision of the vehicle can be improved.

The invention relates to the field of four-rotor control, and discloses an optimal trajectory planning method for a four-rotor suspension payload system, which comprises the following steps of: establishing a four-rotor suspension payload system kinetic model; for unknown lumped disturbance in the established model, estimating the lumped disturbance through a predefined time nonlinear disturbance observer to obtain a compensated nominal model; based on the real-time positions of the four rotors, a dynamic window particle swarm optimization algorithm is adopted in the detection range of a sensor carried by the four rotors to minimize a cost function about path points, and an initial optimal track is found; and taking the compensated nominal model as a new prediction model, taking the initial optimal trajectory as a reference trajectory, and using nonlinear model prediction control to minimize a cost function so as to obtain a prediction trajectory meeting system constraints. According to the method disclosed by the invention, the load transportation reliability and stability can be improved, and the control precision is effectively ensured.

The invention relates to an unmanned surface vessel dynamic obstacle avoidance control method based on real-time nonlinear model predictive control, and the method comprises the steps: constructing a dynamic model, a static obstacle model and a dynamic obstacle model of an unmanned surface vessel; based on the dynamic model, the static obstacle model and the dynamic obstacle model, a multi-objective optimization model is constructed, and the multi-objective optimization model takes trajectory tracking cost, obstacle avoidance cost and control smoothing cost as objective functions and takes a motion range, speed limitation, control input limitation and a collision avoidance rule as constraint conditions; and obtaining the state of the unmanned surface vessel and the obstacle state in real time, inputting a multi-target optimization problem for solving, obtaining control input, and completing dynamic obstacle avoidance control of the unmanned surface vessel based on the control input. According to the invention, the adaptability and safety of the unmanned surface vessel in a complex navigation environment can be improved.

Belongs to the technical field of motion control of foot-type robots, and particularly relates to an anti-interference whole-body control method and system for a quadruped robot based on stability margin perception. The expected joint position, speed and foot end contact force are obtained through nonlinear model predictive control NMPC; a whole body controller WBC based on hierarchical quadratic programming calculates a joint driving torque according to the task priority; according to the joint position, the speed and the torque fed back by the driver and a robot dynamic model, estimating the foot end contact force; calculating resultant force which causes instability of the robot relative to each support polygon side line of the robot; projecting the resultant force, and calculating an included angle between the projection force and the vertical direction; calculating a compensation acceleration through a PI controller; and the compensation acceleration is used as a part of a fuselage linear acceleration task in the WBC, so that the anti-interference capability of the robot is improved. The method is used for solving the problems that in the prior art, the stable state of the quadruped robot cannot be effectively sensed, and an anti-interference control strategy cannot be rapidly generated according to the stable state.

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 discloses a control method for optimal visual perception of an unmanned aerial vehicle, and provides an optimal perception control method for the unmanned aerial vehicle for the key problem of task execution of the unmanned aerial vehicle, namely the stability and the high efficiency of unmanned aerial vehicle control according to the requirement of the unmanned aerial vehicle for the perception stability in the flight process. The method comprises the steps of designing an unmanned aerial vehicle visual perception algorithm and a control algorithm. The visual perception positioning uses ORB-SLAM3 to obtain the position and attitude information of the unmanned aerial vehicle; the control algorithm uses a non-linear model prediction control algorithm, an unmanned aerial vehicle control instruction sequence is solved according to the current position attitude and positioning information of the unmanned aerial vehicle and an expected track, the unmanned aerial vehicle is controlled, and meanwhile it is ensured that the perception quality of the unmanned aerial vehicle meets the control requirement.

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 relates to a vehicle trajectory tracking control method considering dynamic motion requirements. Comprising the following steps: 1, establishing a nonlinear vehicle dynamics model by comprehensively considering longitudinal motion, lateral motion and yawing motion of a vehicle; 2, searching saddle node bifurcation points with qualitative change of the stability state of the vehicle through a particle swarm optimization algorithm and a Lyapunov criterion, and constructing a dynamic stability domain boundary of the vehicle under the change of the longitudinal vehicle speed and the road adhesion coefficient; and 3, designing a vehicle multi-target trajectory tracking controller based on a nonlinear model predictive control theory, quantitatively evaluating the stability state of the vehicle by adopting a stability index, and meanwhile, designing a controller built-in parameter adjustment strategy to match the dynamic motion requirements of the distributed driving electric vehicle in different stability states. According to the method, the driving safety and the movement comfort of the distributed driving electric automobile are effectively ensured, and a set of solution is provided for realizing the optimal cooperative control of the chassis subsystem of the automatic driving automobile.

The invention provides a process optimization analysis system for vacuum concentration, which comprises a multi-source sensing module for acquiring temperature gradient distribution, vacuum degree dynamic waveform, material rheological characteristics and phase change latent heat parameters of an evaporation chamber in real time by adopting a distributed sensor array; the digital twin modeling module is used for constructing a hidden Markov chain model of process parameters and concentration kinetics based on transfer learning, and generating an adaptive covariance matrix by fusing historical batch data; the multi-objective optimization module is used for solving a Pareto frontier solution set in a non-convex solution space by adopting a mixed integer dynamic programming algorithm, and synchronously optimizing an energy efficiency ratio, a product yield and an equipment life index; and the adaptive execution module predicts and controls the hysteresis effect of the real-time compensation system through a nonlinear model, and establishes a double-closed-loop anti-interference regulation mechanism. Accurate sensing, dynamic modeling, multi-objective optimization and self-adaptive execution of the concentration process can be realized, the concentration efficiency, the product quality and the equipment operation stability are improved, and the process intelligence level is improved.

The invention discloses a rigid-flexible integrated aerial contact type operation robot and a control method. The robot comprises a full-drive type unmanned aerial vehicle platform, a single-degree-of-freedom omnidirectional rotation rigid mechanism and a single-section soft arm. The control method comprises the steps that a coordinate system is constructed, and a forward kinematics model of the air contact type operation robot system is established; constructing an inverse kinematics model of the single-section soft arm; designing a self-adaptive inverse kinematics control algorithm based on reinforcement learning on the basis of the inverse kinematics model of the single-section soft arm; according to the method, an all-drive unmanned aerial vehicle platform dynamic model is established, a nonlinear model prediction control method based on an extended Kalman filter estimator is designed, lateral force input of the all-drive unmanned aerial vehicle platform can be restrained, and accurate tracking of the all-drive unmanned aerial vehicle platform under the condition of additional disturbance is guaranteed. Therefore, flexible control in the air in a limited environment is realized by using a motion control method and a soft arm inverse kinematics control algorithm of the all-wheel-driven unmanned aerial vehicle platform.

The invention discloses an unmanned ship dynamic obstacle avoidance control system and method based on stochastic nonlinear model predictive control, and adopts a control structure of a planning layer and a control layer to solve the problem of dynamic obstacle avoidance of an unmanned ship in a complex obstacle environment. The planning layer is based on a random nonlinear model predictive control technology, and firstly, random modeling is carried out on the unmanned ship and dynamic obstacles encountered by the unmanned ship in the sailing process; designing obstacle avoidance switching conditions according to the whole actual obstacle avoidance process; in an obstacle avoidance random nonlinear model prediction planning control algorithm, uncertainty propagation of a dynamic obstacle and an unmanned ship in a whole prediction time domain is evaluated through unscented transformation, then obstacle avoidance constraint is described in a chance constraint mode, a chance constraint expectation operator transformation algorithm and a unit jump function approximation algorithm are designed, and a chance constraint expectation operator transformation algorithm and a unit jump function approximation algorithm are designed. Opportunity constraints are converted into deterministic constraints, and then a nonlinear optimization problem is constructed for planning. And the control layer adopts a PI D algorithm to track a planning value so as to realize dynamic obstacle avoidance of the unmanned ship.

The invention relates to the technical field of automatic driving, in particular to an automatic driving system based on intelligent traffic, which comprises a multi-source heterogeneous sensing fusion unit, a group collaborative decision-making unit and a dynamic control optimization unit, the group collaborative decision-making unit carries out space-time alignment on multi-source heterogeneous data, constructs and updates a global environment perception knowledge graph, carries out modeling on vehicle trajectories based on a Monte Carlo tree search framework in combination with deep reinforcement learning and risk perception, and trains a deep Q network model by adopting federated learning and adversarial training. A traffic flow distribution scheme is generated by applying multi-agent deep reinforcement learning and a game theory, storage data is proved by using homomorphic encryption and zero knowledge, a dynamic control optimization unit predicts, controls and plans an acceleration curve through a nonlinear model, and a transverse path tracking error is controlled by fusing sliding mode control and a fuzzy logic algorithm. And the safety of automatic driving is improved.

The invention relates to a four-wheel independent steering and driving vehicle path tracking control method based on a compound control framework, which comprises the following steps of: 1, establishing a seven-degree-of-freedom dynamical model of a four-wheel independent steering and driving vehicle, and realizing high-performance path tracking by reasonably distributing input parameters; step 2, establishing a deep reinforcement learning transition model, defining a vehicle state and an input parameter of an auxiliary controller, and designing a system reward function to optimize path tracking control performance; 3, selecting a twin delay depth deterministic strategy gradient algorithm to realize deep reinforcement learning, reducing the over-estimation problem of the algorithm, and improving the learning stability; 4, a double-layer control framework is adopted, an upper-layer controller calculates generalized control quantity based on nonlinear model predictive control, and a lower-layer controller distributes the generalized control quantity to each wheel through sequential quadratic programming so as to solve the transverse and longitudinal coupling problem of the vehicle in path tracking control; and step 5, the auxiliary controller based on deep reinforcement learning carries out real-time compensation on the control variable according to the external disturbance and the vehicle state, so that the control precision and stability of path tracking are improved. By means of the method, stable control over four-wheel independent steering and driving vehicle path tracking in a complex environment is achieved, and the adaptive capacity of the system to external interference is enhanced.

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 relates to the technical field of automatic driving, and particularly discloses a low-delay moving target detection method and system for navigation, and the method comprises the steps: collecting an event flow through a vehicle-mounted event camera; adjusting the time window length of the event flow in real time according to the vehicle speed, the vehicle acceleration and the event density gradient to obtain an event image; inputting the event image into a pulse neural network to obtain an output feature map; detecting the feature map through a feature pyramid network and a detection head to obtain a motion trail flow of the target; and performing nonlinear model predictive control according to the motion trail flow to obtain an optimized motion trail flow of the target.

The invention discloses a full-automatic multi-component dispensing dynamic mixing proportion closed-loop control system, which relates to the technical field of semiconductor manufacturing, and comprises a multi-component feeding module, a dynamic proportioning execution module, an online detection module, an intelligent control module, a mixing reaction module, an anti-pollution module and a data management module, the device has the advantages that anti-sticking conveying and rapid switching of multi-form raw materials are achieved through a plurality of independent flow channels and inner wall diamond-like coatings of the multi-component feeding module in cooperation with a flow channel inlet vibrating screen and an outlet electromagnetic valve, a double-metering-pump parallel structure and a double-axial-flow type pulse damper are adopted in the dynamic matching execution module, and the dynamic matching efficiency is improved. And by combining a nonlinear model prediction controller of the intelligent control module and BP neural network adaptive compensation, the dynamic adjustment precision of the multi-component mixing ratio is remarkably improved, the mixing uniformity and purity reach the standard, the product scrapping caused by the ratio error is greatly reduced, and the photoresist production yield and stability are improved.

The invention relates to a four-rotor unmanned aerial vehicle pose control method based on a global sliding mode. The invention relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: carrying out the modeling of a dynamic model of a quad-rotor unmanned aerial vehicle, and obtaining the nonlinear state space description of the quad-rotor unmanned aerial vehicle; secondly, respectively designing global sliding mode control laws for the position subsystem and the attitude subsystem according to the nonlinear model; according to the output of a position subsystem controller, the expected acceleration is mapped into expected lift force and attitude angle by designing control distribution; and finally, constructing a nonlinear programming problem corresponding to a nonlinear model prediction controller by taking a previously designed global sliding mode control law as a contraction constraint, and solving the problem to obtain a control quantity. According to the method, the quadrotor unmanned aerial vehicle can effectively perform trajectory tracking under the conditions of output constraint and convergence guarantee, and the control precision, the response speed and the safety in the flight process are improved.

The invention relates to a fault-tolerant control technology, and aims to provide an AUV fault-tolerant control method based on near-end strategy optimization and tubular model prediction. Comprising the following steps: establishing an underwater robot mathematical model, and discretizing into a nominal system and a real system; nonlinear model predictive control is achieved for a nominal system, and a nominal control law is obtained through solution and rolling optimization; introducing a near-end strategy optimization algorithm based on tubular model predictive control, outputting an auxiliary feedback control law based on an actor and commentator network, and adding the auxiliary feedback control law with a nominal control law to obtain a fault-tolerant control law for the underwater robot propeller; keeping the parameters of the nominal control law unchanged; meanwhile, an auxiliary feedback control law is adaptively updated based on a strategy obtained through near-end strategy optimization training, and online fine adjustment of fault-tolerant control of the propeller is achieved. Influences of faults and delay of the propeller are passively processed by means of inherent robustness of the controller, the design of a fault device control law is simplified while the stability is guaranteed, and the conservative property of a classical tubular model is reduced.

The invention provides an autonomous exploration method and device for an indoor mobile robot. The method comprises the following steps: performing pose estimation on the indoor mobile robot based on point cloud data and sensor data fusion; processing the point cloud data by adopting a method of combining fan-shaped projection and binary Bayesian filtering, carrying out environment modeling and constructing a lightweight grid map; according to the environment information on the lightweight grid map, utilizing a ray edge grid analysis method, a Bayesian inference edge grid intersection segmentation method, a leading edge region segmentation method and a viewpoint sampling and evaluation method to detect a leading edge region of the explored region and segmenting the leading edge region into a plurality of sub-regions; generating an optimal viewpoint of each sub-region; exploring an unknown environment by using the optimal viewpoint and adopting a hierarchical exploration strategy combining global exploration and local exploration to obtain a planned path; and according to the planned path, generating a navigation trajectory by adopting a method of combining multi-constraint trajectory optimization and nonlinear model predictive control.

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 relates to a flight control technology of an unmanned aerial vehicle suspension load system, and provides a multi-target nonlinear model prediction control method for an unmanned aerial vehicle-load suspension system, so as to realize accurate track tracking, active suppression of lifting rope swinging and collaborative optimization of flight smoothness. According to the multi-state multi-target model prediction control method for the unmanned aerial vehicle-load suspension system, firstly, a nonlinear model prediction control equation is constructed; discretizing the continuous kinetic model by adopting a fourth-order Runge-Kutta method to obtain a high-precision state transition equation under discrete time as a dynamic constraint; forming an optimal control function of a flight nonlinear model predictive control equation of the unmanned aerial vehicle-load suspension system; adding thrust amplitude constraint and moment boundary constraint as inequality constraint conditions of a nonlinear model predictive control equation; and finally, multi-target optimal control of the unmanned aerial vehicle-load suspension system is realized. The method is mainly applied to design and manufacturing occasions of unmanned aerial vehicle hanging load systems.

The invention discloses a nonlinear model predictive control method for the strip steel temperature of a continuous annealing furnace, and relates to the technical field of metallurgical industry automation control, and the method comprises the following steps: collecting historical data in the production process of the continuous annealing furnace; a nonlinear autoregression exogenous input model based on Sigmoid-ARX is established; the model parameters are optimized through a Levenberg-Marquardt algorithm, and the model parameters are optimized through the Levenberg-Marquardt algorithm; based on the optimized Sigmoid-ARX model, designing a nonlinear model prediction controller, performing local linearization near a given working point, and converting a nonlinear optimization problem into a quadratic programming problem; by constructing the Sigmoid-ARX nonlinear model and the predictive control capability and system-level constraint processing characteristics of the model predictive control algorithm, the strip steel temperature predictive precision is improved, stable control over strip steel of different specifications in the transition stage is achieved by optimizing the control strategy, the standard deviation of the strip steel temperature and speed is reduced, and the stability of the strip steel temperature and speed is improved. The average speed of a production line is improved, and the yield is increased.