25 results about "Linear model predictive control" patented technology

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 relates to an aiming point dynamic stabilization and automatic calibration method fusing inertial measurement unit data, and belongs to the technical field of aiming equipment dynamic control. The method comprises the following steps: acquiring attitude data of an inertial measurement unit, and constructing a multi-source data matrix in combination with real-time position data of an aiming point and environmental interference data; after time sequence synchronization and drift suppression are carried out on multi-source data, associated feature vectors are extracted through multi-modal feature fusion; inputting a self-adaptive extended state observer to estimate total disturbance and generate an anti-interference compensation amount, constructing a kinetic model in combination with a nonlinear model prediction controller, solving a multi-objective optimization problem, and generating an attitude compensation control sequence to realize dynamic stability of an aiming point; and finally, aiming deviation is calculated in real time to trigger double-closed-loop calibration, an inner ring corrects the drift error of the inertial measurement unit, and an outer ring optimizes parameters of the observer and the controller. According to the invention, environmental interference and inertial drift are effectively suppressed, the dynamic aiming stability is improved, and the long-term aiming reliability is guaranteed through closed-loop calibration.

The invention provides a dynamic risk perception prediction stability control system and method for a cold-chain logistics transport vehicle, and belongs to the technical field of vehicle control. Comprising three core modules: a physical-data mixed residual state predictor, a quantitative stability evaluation module based on a maximum Lyapunov index, and a self-adaptive nonlinear model prediction controller fusing predetermined performance control. And finally, the control instruction is distributed to the four in-wheel motors through a multi-target torque distributor. According to the method, the prediction precision is remarkably improved, continuous risk quantification is realized, and meanwhile, the control performance and the system robustness are synchronously improved.

The application discloses a stable domain constraint trajectory planning and control method for a modular distributed electric drive heavy load vehicle, comprising the following steps: constructing a nonlinear dynamics model of the modular distributed electric drive heavy load vehicle, fitting a tire lateral force model through a rational polynomial, and establishing a vehicle system state space expression based on the rational polynomial form; adopting a sum of squares planning method to estimate the stable domain of the vehicle under different vehicle speeds, road adhesion coefficients and front, middle and rear wheel turning angles, and analyzing the influence of each state quantity on the shape and boundary of the stable domain; embedding the stable domain constraint into a trajectory planning layer based on a quintic polynomial to generate an obstacle avoidance trajectory meeting the dynamics stability; designing a multi-axle proportional steering strategy based on a steady state assumption, and combining a nonlinear model predictive controller to realize trajectory tracking control, so that the stability and control feasibility of the vehicle in a high-speed dynamic obstacle avoidance process are ensured. Through construction of a high-speed dynamic obstacle avoidance simulation scene, the effectiveness, stability and control feasibility of the stable domain constraint trajectory planning and control framework are verified.

This invention discloses a data-driven modeling and model predictive control method for a robotic arm system. The method includes: collecting experimental data containing joint positions, joint velocities, and joint drive inputs; selecting a set of lifting functions to map the original state to a high-dimensional lifted state space; constructing a linear regression problem in the lifted state space based on adjacent time-state samples; obtaining a Koopman linear model by solving the least squares method with regularization; embedding the Koopman linear model into model predictive control under given joint reference trajectories; constructing a quadratic performance index dominated by joint position deviation; solving for the joint drive input at the current time-state through rolling optimization; and sending the solution to the robotic arm drive system for execution. The method proposed in this invention can obtain a linear model with high prediction accuracy relying solely on real experimental data. While maintaining the computational advantages of linear model predictive control, it is beneficial for achieving trajectory tracking control of the robotic arm within a larger working area.

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 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 embodiment of the invention discloses a plant protection unmanned aerial vehicle spraying control method based on drift risks. The method comprises the steps that multi-source sequence data are collected in real time and preprocessed; inputting the preprocessed multi-source sequence data into a spraying prediction model to obtain sprayed object settlement distribution data; a spraying prediction model is constructed based on a time pattern sparse attention mechanism and a long short-term memory (LSTM) network; and inputting the settlement distribution data, the unmanned aerial vehicle flight data, the unmanned aerial vehicle spraying data and the wind field information into an outer loop nonlinear model prediction controller, and carrying out iterative optimization on the unmanned aerial vehicle dynamics model based on the optimization objective function until the error between the settlement distribution data and the expected settlement distribution data satisfies the minimization of the optimization objective function. Outputting an expected position sequence of the unmanned aerial vehicle; and based on the expected position sequence of the unmanned aerial vehicle, performing tracking control on the spraying process of the unmanned aerial vehicle through an inner-loop nonlinear model prediction controller, and adjusting the spraying data of the unmanned aerial vehicle in real time until the current settlement distribution meets the expected settlement distribution data.

The invention discloses a flying height control and trajectory tracking method for an aerocar in a ground-air dual mode, and the method comprises the following steps: 1, building a four-rotor aerocar dynamics model in a flying state; 2, designing a height controller based on adaptive RBF neural network sliding mode control according to the four-rotor aerocar dynamics model established in the step 1, and controlling the flight height by dynamically adjusting the rotating speed of a motor; and step 3, in a ground driving state, establishing a vehicle trajectory tracking model, designing a linear model prediction control algorithm, and outputting a required front wheel turning angle to realize trajectory tracking control. According to the method, air-ground dual-mode control of the hovercar is innovatively provided, and operation and control of the hovercar are well achieved.

This invention discloses a nonlinear model predictive control method and system for wind turbine generator sets. The method includes the following steps: constructing a nonlinear wind turbine model; wherein the nonlinear wind turbine model includes a nonlinear aerodynamic model, a wind turbine drivetrain model, a wind turbine blade-tower coupling model, and an actuator response model; based on the nonlinear wind turbine model, using wind speed disturbance, rotational speed, pitch angle, tower displacement estimation, and tower velocity estimation as input variables, predicting the state of the wind turbine generator set; wherein the tower displacement estimation and tower velocity estimation are obtained through a nonlinear observer; the input variables of the nonlinear observer are the measured tower acceleration, wind speed disturbance, rotational speed, and pitch angle. This invention can effectively reduce the load on the wind turbine generator set's blade root and tower, which not only helps to reduce the weight of various major components and reduce the cost of the unit, but also improves the stability of the unit's operation and extends its service life.

A braking pressure control method for new energy vehicles based on the Koopman operator includes: establishing the dynamic equilibrium equations and nonlinear model of the braking system; constructing an observation function set, identifying the Koopman operator matrix using an extended dynamic mode decomposition algorithm, constructing a global linear prediction model, and correcting the Koopman operator matrix; constructing an augmented state equation in the discrete-time domain, designing a linear extended state observer based on an improved Kalman filter algorithm, and outputting a lumped disturbance estimate; injecting the lumped disturbance estimate into the prediction equation of the linear model predictive control, and obtaining the optimal control sequence by solving the objective function of a quadratic programming problem; converting the first control increment of the optimal control sequence into a motor drive signal, which is applied to the master cylinder motor of the electro-hydraulic braking system for physical pressure build-up. This invention can solve the problems of low hydraulic pressure control accuracy, poor anti-interference ability, and high computational load in existing technologies.

The application discloses a kind of dynamic obstacle avoidance method of machine arm based on attention mechanism and NMPC self-adaption, it is related to machine arm control technical field, the method includes: S1: system initialization, load workspace map or build map from zero, and based on space-time four-dimensional space-time, find a feasible trajectory from starting pose to target pose;S2: introduce attention mechanism, identify the key area in four-dimensional space, detect dynamic obstacle in environment, quantify the dynamic index of environment;S3: according to the dynamic index, adjust the objective function weight of nonlinear model predictive controller, optimize feasible trajectory based on nonlinear model predictive controller and double-level obstacle avoidance design;S4: determine whether the trajectory of machine arm movement can avoid obstacle, if so, machine arm executes this trajectory;If not, trajectory re-planning is carried out, return step S1.The application is to the prediction of dynamic obstacle movement trajectory in environment, and feedback to global trajectory planning by prediction information, efficient, smooth, safe operation in highly dynamic environment is realized.

The application discloses a bucket wheel machine multi-node cooperative control system and method based on wireless ad hoc network, which first utilizes multi-dimensional sensor data to solve the local high-order motion state, generates the local desired trajectory sequence containing future intention in the rolling prediction time domain and broadcasts it. The receiving end analyzes the adjacent node trajectory, combines the physical layer signal characteristics and data time effectiveness index to calculate the link reliability coefficient, performs the spatial geometric inflation processing based on uncertainty on the neighbor trajectory, and constructs the safety envelope dynamically matched with the current communication quality. On this basis, the nonlinear model prediction control solver is used to solve the local trajectory with the weighted neighbor trajectory set as the dynamic hard constraint, so as to output the execution action instruction capable of avoiding collision and maximizing operation continuity.

The invention discloses a quadruped robot arm-leg collaborative obstacle avoidance method and system based on a visual language large model and a diffusion strategy, and the method comprises the steps: obtaining multi-source sensor data, and constructing a semantic risk potential energy field containing an operation safety rule through a laser radar and the visual language large model; inputting the geometric occupancy information, the semantic risk potential energy field and the whole body state of the robot into a pre-trained conditional diffusion strategy model, and generating a full-dimensional state reference trajectory cluster through an inverse diffusion process; constructing an arm-leg collaborative nonlinear model predictive control problem based on the screened optimal trajectory, and introducing zero moment point constraint and angular momentum balance constraint to solve a whole-body control law; and the legs are driven to avoid obstacles, and inertia compensation is executed through the mechanical arms. According to the method, combined obstacle avoidance is realized through semantic understanding, a high-dimensional planning problem is solved by utilizing a generative strategy, the dynamic stability is improved through arm-leg dynamics coupling, and the method is suitable for intelligent supervision and operation of complex environments such as a transformer substation.

The application belongs to the technical field of helicopter control, and aims at solving the problem of poor generalization of the prediction model constructed for a turboshaft engine, and provides a turboshaft engine identification and prediction control method based on MRR-KELM, which adopts the MRR-KELM algorithm combined with the Huber loss function and the adaptive regularization strategy, suppresses the noise interference and the influence of abnormal values, greatly reduces the prediction error of the constructed turboshaft engine prediction model, and can realize dynamic mapping through the Gaussian kernel and adaptive adjustment of the working condition parameters; the algorithm introduces the regularization term and the robust loss function, combines the kernel function mapping capability, significantly improves the generalization performance and calculation efficiency of the model under complex working conditions such as small samples and noise interference, and further embeds the turboshaft engine prediction model into a nonlinear model predictive control framework, realizes high-precision stable control of the power turbine speed through rolling optimization and feedback correction.

The invention discloses a quadrotor unmanned aerial vehicle cooperative transportation control method based on a deep neural network, and belongs to the field of unmanned aerial vehicle cooperative transportation control. A kinetic equation capable of describing a quadrotor unmanned aerial vehicle cooperative transportation hanging rod-shaped load is constructed, and a nonlinear model prediction controller is designed for the kinetic equation; then designing a deep neural network based on the designed nonlinear model predictive controller, and training the neural network by using the collected flight data, so that the neural network can fit the control performance of the nonlinear model predictive controller; finally, the deep neural network controller has control performance similar to that of a nonlinear model predictive controller, and meanwhile the requirement for the calculated amount is greatly reduced.

The application belongs to a kind of inspection robot operation control method, for the low degree of autonomy of existing overhead transmission line inspection robot, poor operation positioning accuracy and control stability in disturbance environment such as wind disturbance and line vibration, and lack of efficient approach and soft operation ability for loose damper bolt, provide a kind of transmission line inspection robot anti-interference servo control operation method and related device, through bolt pose acquisition and operation arm rough positioning, realize the end tool fast approach;Again based on bolt point feature deviation, combined with disturbance observer and adaptive nonlinear model predictive control to complete anti-interference visual servo alignment;After alignment, along the Z axis linear approach of end tool coordinate system, and compensate the small pose deviation through flexible device, cooperate with torque feedback control screwing end, so as to improve the degree of automation, alignment stability and screwing reliability of bolt operation in disturbance environment.

The invention discloses an obstacle avoidance method and device and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring sensing data generated by a sensor, and performing hyper-ellipsoid modeling according to the sensing data to obtain a vehicle hyper-ellipsoid model and an obstacle hyper-ellipsoid model; according to the separation hyperplane theorem and the geometric parameters of the vehicle hyperellipsoid model and the obstacle hyperellipsoid model, collision avoidance conditions between the vehicle and the obstacle are constructed; and performing trajectory planning and tracking on the collision avoidance condition through a hierarchical nonlinear model prediction control structure, and generating a control instruction of the vehicle. Modeling is performed on the vehicle and the obstacle by adopting the hyper-ellipsoid, the path planning efficiency is remarkably improved, the conservative safety distance is reduced, the adaptability to complex geometrical shapes is improved, and the effect of considering the real-time performance of complex constraint optimization and high-precision trajectory tracking control is achieved by adopting a hierarchical nonlinear model prediction control structure.

The application discloses a metro pulse passenger flow-oriented escalator control method, equipment and medium, the method comprises the following steps: according to metro train operation data, construct the pulse passenger flow trigger sequence based on discrete events, calculate the continuous passenger flow injection curve arriving at the entrance of the escalator;The future dynamic load torque of the escalator is predicted by using PINN network;In combination with the predicted dynamic load torque, the minimum system operation energy consumption, mechanical wear and queuing congestion penalty are taken as the comprehensive target, the multi-objective nonlinear model predictive control optimization problem is constructed and solved, the optimal speed control instruction is generated and issued to the escalator frequency converter for execution.The application can accurately reconstruct the real-time quality distribution on the escalator, and calculate the future dynamic load torque accordingly, and the controller adjusts the motor operation accordingly, so that it always works at the optimal slip frequency point, reduces the energy consumption of the escalator, reduces the wear of parts, prolongs the service life.

The application discloses a metro pulse passenger flow-oriented escalator control method, equipment and medium, the method comprises the following steps: according to metro train operation data, construct the pulse passenger flow trigger sequence based on discrete events, calculate the continuous passenger flow injection curve arriving at the entrance of the escalator;The future dynamic load torque of the escalator is predicted by using PINN network;In combination with the predicted dynamic load torque, the minimum system operation energy consumption, mechanical wear and queuing congestion penalty are taken as the comprehensive target, the multi-objective nonlinear model predictive control optimization problem is constructed and solved, the optimal speed control instruction is generated and issued to the escalator frequency converter for execution.The application can accurately reconstruct the real-time quality distribution on the escalator, and calculate the future dynamic load torque accordingly, and the controller adjusts the motor operation accordingly, so that it always works at the optimal slip frequency point, reduces the energy consumption of the escalator, reduces the wear of parts, prolongs the service life.

This invention provides an automated operation system and method based on the collaboration of unmanned vehicles and tower cranes. The method involves a cloud platform issuing tasks; the unmanned vehicle guides its robotic arm to dynamically and precisely hook up a lifting device using a visual spatial pose estimation algorithm and Kalman filter trajectory prediction; the tower crane generates a global safe path based on a building information model and potential energy value, and performs dynamic obstacle avoidance using nonlinear model predictive control; simultaneously, a nonlinear feedback control law based on Lyapunov functions is used to dynamically fine-tune the mechanism speed to achieve active anti-sway compensation; finally, a blockchain subsystem generates a dynamic root hash value from the multi-dimensional interactive data and packages it onto the blockchain. This invention achieves seamless collaboration between ground and air equipment, completely eliminating the risks of manual hooking at heights, and greatly improving the safety, stability, and data traceability reliability of hoisting operations.

The present application belongs to the technical field of solid oxide fuel cell control and optimization, and particularly relates to a solid oxide fuel cell carbon deposition sensing model predictive control method and system. A six-dimensional measurement output vector of a system is collected in a sampling period, and algebraic loops in a measurement feedback channel are eliminated; thermal dynamic equations of each component of an auxiliary equipment system are embedded into a predictive trajectory linearized nonlinear model predictive control prediction model; an augmented cost function is constructed; and a burner exhaust temperature is taken as an input of a heat source side of each component of the auxiliary equipment system after being delayed by a second unit. The present application completely establishes MD / BR double-path carbon deposition dynamics and catalyst activity attenuation models, eliminates prediction deviation caused by neglecting BOP thermal inertia, and completely eliminates algebraic loops of the system by using a double-unit delay structure, thereby ensuring numerical stability of simulation and engineering implementation.

The present application relates to the straddle-type monorail pantograph active control method based on the prediction of the pantograph-catenary contact force, belongs to the pantograph active control field, and comprises the following steps: S1: obtaining the state characteristic data and the actual pantograph-catenary contact force data of the pantograph-catenary system in the running process of the straddle-type monorail train; S2: constructing the intelligent model of the straddle-type monorail pantograph-catenary system based on the ''mechanism-data'' double drive; S3: extracting the time sequence characteristics of the pantograph-catenary contact force, inputting into the IGWO-GRU neural network, and constructing the prediction model of the straddle-type monorail pantograph-catenary contact force; S4: constructing the nonlinear model predictive control framework of the straddle-type monorail pantograph; S5: using the sequential quadratic programming method (SQP) to perform online solution on the rolling optimization objective function; the first control increment of the optimal control increment sequence is used on the pantograph actuator, and the closed-loop correction mechanism is constructed. The present application reduces the pantograph-catenary contact force fluctuation degree, improves the current collection quality of the train, and improves the service life of the pantograph slide.

The invention discloses an electro-hydraulic system energy-saving control method based on double-layer optimization, and relates to the technical field of hydraulic system control, the method comprises the following steps: based on an operation principle of an electro-hydraulic system, constructing a control model of a valve-controlled hydraulic cylinder and a hydraulic pump; expressing a position tracking control problem of the valve-controlled hydraulic cylinder as a constraint-containing optimal control problem by using a linear model predictive control method; thirdly, an energy optimization control method is adopted, the output power of the pump serves as an optimization target, and a hydraulic pump energy optimal controller is constructed; finally, valve control and pump control are integrated into a double-layer optimization problem based on the Kuhntke condition; by solving a double-layer optimization problem, the control quantity of a valve and a pump at each moment is obtained, and real-time optimization control over the electro-hydraulic system is achieved; according to the method provided by the invention, the energy loss of the system can be reduced as much as possible while the control performance is ensured, so that high-precision and high-energy-efficiency control of the electro-hydraulic system is realized.

The invention relates to a loader cooperative driving control method based on SMC-NMPC, and belongs to the technical field of engineering machinery control. The method comprises the following steps: acquiring state information through a vehicle-mounted sensor, and establishing a nonlinear dynamic model; performing online optimization solution by using a nonlinear model predictive control layer to obtain an optimized sliding mode surface; the sliding mode control layer is based on a sliding mode surface design control law, obtains a total control quantity by integrating equivalent control and switching control, and drives a hydraulic execution mechanism. The problems of low control precision, slow response and poor stability of the loader caused by time lag and nonlinear characteristics of a hydraulic system under complex working conditions are effectively solved, the control precision, the anti-interference capability and the driving stability are improved, and the operation safety and efficiency are ensured.