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

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.

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 application belongs to the field of trajectory tracking control of quadrotor unmanned aerial vehicles, and specifically provides a sparse nonlinear model predictive control method (FS-NMPC) based on differential flatness, to overcome the problem of insufficient control performance of the existing nonlinear model predictive control method (NMPC), and the application has good tracking accuracy and higher calculation efficiency. The application discretizes the differential flatness model of the quadrotor unmanned aerial vehicle through sparse discrete points in the prediction view, so that the number of optimization variables and matrix equations in the nonlinear model predictive control is greatly reduced; in summary, the application has significant advantages in calculation efficiency and good tracking performance.

This invention discloses a method and system for suppressing drill string stick-slip vibration based on nonlinear model predictive control, comprising the following steps: S1: Establishing a drill string dynamics model and constructing a nonlinear model predictive control controller based on the drill string dynamics model; the nonlinear model predictive control controller includes a prediction model, an objective function, and constraints, wherein the prediction model is a nonlinear state-space form of the drill string dynamics model; S2: Acquiring downhole data of a target well with a duration greater than a duration threshold and its corresponding rotary table control torque, and using an optimization algorithm to correct the drill string dynamics model and the prediction model; S3: Predicting the downhole state at the current moment based on the corrected drill string dynamics model, and using the downhole state at the current moment as the input of the nonlinear model predictive control controller, outputting the control torque at the current moment. This invention can suppress drill string stick-slip vibration faster and more accurately than traditional sliding mode control.

The application relates to the technical field of oil and gas field development engineering, and discloses an oil reservoir injection-production structure self-adaptive optimization and recovery efficiency intelligent prediction system, which comprises a physical operation subsystem, a data transmission network and a central control processing subsystem. An active excitation generation module constructs an orthogonal pseudo-random binary sequence mapping to be applied to a water injection well as a flow instruction; a multi-frequency response decomposition module identifies a formation connectivity coefficient and a lag time from a pressure response by using cross-correlation operation; a flow pipe network evolution module constructs a one-dimensional flow pipe network based on the connectivity coefficient, deduces an oil-water interface and an instantaneous water cut; and an optimization control module optimizes a water injection sequence based on a nonlinear model predictive control strategy, and updates a reference water injection amount by using a first value feedback as a target to maximize a net present value. Through a closed-loop feedback mechanism, the application realizes real-time identification of parameters and automatic optimization of an injection-production structure under continuous production.

This invention relates to the field of unmanned aerial vehicle (UAV) control technology and includes the following steps: S1, establishing a discrete nonlinear dynamic model with disturbances based on quaternions; S2, designing a lightweight nonlinear model predictive controller based on real-time iteration of sequential quadratic programming; S3, constructing a lightweight MLP disturbance observer to achieve real-time estimation of six-dimensional disturbances; and S4, designing a position-torque dual-layer feedforward compensation strategy. This invention significantly reduces the online computation of NMPC by combining the SQP_RTI real-time iterative framework with a hot-start and matrix pre-storage mechanism, enabling millisecond-level real-time operation on the airborne platform. It employs a 26-dimensional feature fusion MLP network to achieve joint estimation of disturbance forces and torques, eliminating dependence on precise model parameters. The dual-layer separate feedforward design, which corrects the reference trajectory at the position layer and directly compensates for motor speed at the torque layer, avoids coupling interference from a single compensation method, achieving high-precision trajectory tracking even under external disturbances such as wind fields and sudden load changes.

This invention discloses a method for controlling the movement of a humanoid robot climbing stairs, comprising: acquiring environmental perception data in front of the robot and robot body state information; constructing a local terrain representation and performing a treadability assessment to obtain a treadable area; segmenting the support plane based on the treadable area and constructing a structured safety support surface; generating a torso reference trajectory, foot landing point, and foot swing trajectory based on the structured safety support surface; constructing constraints on the treadable domain of the support foot and obstacle avoidance constraints on the swinging foot, and introducing nonlinear model predictive control; further combining whole-body control and joint hybrid control to output execution commands, thereby realizing continuous and stable stair climbing movement of the humanoid robot in a stair environment, improving foot landing safety, swinging obstacle avoidance ability, and movement stability.

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 provides a floating wind turbine nonlinear model predictive control variable pitch load reduction method and system, and belongs to the technical field of deep-sea wind power generation control. The method comprises the following steps: obtaining wind turbine state quantity measurement data and historical control input data, constructing a nonlinear candidate function library, solving a coefficient matrix, and constructing an SINDy prediction model capable of representing sparse nonlinear dynamics of the system; the system matrix is loaded into the controller as an internal prediction model, and a future wind speed sequence is input into the controller as a known disturbance; in the controller, the SINDy prediction model and a multi-objective optimization function are used to predict and rollingly optimize the system output in a future time domain under the premise of meeting the physical constraints of the system, and the optimal independent variable pitch control quantity at the current time is calculated; the output of a variable gain PI controller of the unified variable pitch part is calculated, and is superimposed with the optimal independent variable pitch control quantity at the current time to form a complete control signal, which is transmitted to the wind turbine as a pitch angle reference.

This invention discloses a motion control method for humanoid robots based on ground contact detection, applied to bipedal humanoid robots with a floating base structure. The method includes: constructing a model predictive controller based on a center-of-mass dynamics model; constructing a ground contact probability model; adaptively adjusting the current gait phase based on ground contact detection results; and constructing an inverse dynamics quadratic programming problem including generalized acceleration, foot contact force, and joint driving torque, solving for the joint torque command under conditions satisfying dynamic constraints, foot non-slip constraints, and friction cone constraints. By introducing ground contact detection into a nonlinear model predictive controller, designing a reasonable center-of-mass dynamics model, and adding necessary equality and inequality constraints, this invention enables adaptive adjustment of the humanoid robot in scenarios with unknown foot contact states, improving the stability and robustness of the humanoid robot's walking in complex terrain conditions.

The application provides a microbial fuel cell operation optimization method and system based on NMPC, and belongs to the field of microbial fuel cell control. The method comprises the following steps: a dynamic model of the microbial fuel cell is established, and system operation data are collected in real time; based on the collected operation data, the equivalent internal resistance and the current operation mode of the system are determined; in a prediction time domain, the dynamic model is discretized by adopting a fourth-order Runge-Kutta method, and future state variables are recursively predicted; a multi-objective optimization function of nonlinear model predictive control is constructed based on the predicted values; an improved chaotic particle swarm optimization algorithm is adopted to solve the multi-objective optimization function, so that an optimal control input sequence in the prediction time domain is obtained; the first control amount in the optimal control sequence is extracted as the actual control input at the current time, and the above process is repeatedly executed at the next sampling time to realize rolling optimization closed-loop control. The application significantly improves the comprehensive operation performance of the MFC system.

This invention provides a method and system for reversing path tracking control of an articulated vehicle based on predictive control, relating to the field of motion control technology for autonomous vehicles. The method includes: constructing a hardware platform for bidirectional movement of the articulated steering vehicle, using the original front axle of the articulated vehicle as the de facto rear axle; constructing a mathematical model of the reversing condition; and performing predictive control on the articulated steering vehicle based on the hardware platform and the mathematical model of the reversing condition to complete the reversing path tracking control of the articulated vehicle based on predictive control. This invention, by setting up a hardware platform, establishing a mathematical model of the articulated vehicle under reversing conditions, and designing a multi-predictive-point nonlinear model predictive control algorithm, can solve the problem that existing model predictive control-based articulated vehicle path tracking control systems cannot complete reversing path tracking control. Compared to the technical solution of adding a positioning system to the rear axle, the cost is lower.

The application discloses a kind of based on non-inertial system's rotor unmanned aerial vehicle model predictive control method and device, the method includes: according to the relationship between inertial system, non-inertial system and rotor unmanned aerial vehicle system, the dynamic model of rotor unmanned aerial vehicle under non-inertial system is established;According to the dynamic model, the nonlinear model predictive controller of rotor unmanned aerial vehicle is designed;Using sequential quadratic programming algorithm, the relative pose given by motion capture system or CREPES is combined with the state information collected by inertial measurement unit on non-inertial system to solve the optimization problem in the nonlinear model predictive controller, the total thrust of rotor unmanned aerial vehicle and three-axis angular velocity control quantity are obtained, so as to realize the flight control of rotor unmanned aerial vehicle under non-inertial system.

The application discloses an actuator failure active inhibition method and system based on digital twinning, and belongs to the technical field of actuator system reliability control and data processing. First, a dynamic knowledge graph of key failure thresholds of an EHA is constructed by using multi-physical field coupling failure mechanism data, and a dynamically variable system-level comprehensive failure boundary is determined. Second, a high-fidelity digital twin model of the EHA covering multiple scales is established. Then, a safety margin index of the current operating state is calculated based on real-time state and the comprehensive failure boundary. Finally, a multi-objective cost function is constructed by using the index, the high-fidelity digital twin model and a nonlinear model predictive control framework, an optimal control sequence is solved by rolling, and corresponding hierarchical failure active inhibition actions are triggered. Through multi-dimensional state dynamic perception and predictive regulation, the application actively avoids the multi-mode competitive failure risk in extreme working conditions, and significantly improves flight safety and task continuity.

This invention belongs to the field of intelligent control and state estimation technology for unmanned aerial vehicles (UAVs), and discloses a UAV trajectory tracking control method based on PSO-STEKF and NMPC under strong interference environments. The method constructs a nonlinear discrete state-space model of the UAV, establishes the nonlinear model predictive control (NMPC) objective cost function and control input constraints; utilizes the particle swarm optimization algorithm (PSO) to optimize the fading factor in the strong tracking extended Kalman filter (STEKF) online, obtaining a posterior state estimate; uses the posterior state estimate as the initial state of the NMPC optimization problem, and continuously solves for the optimal control input sequence before applying the first control input. This method can improve the state estimation accuracy, trajectory tracking performance, and system robustness under strong interference environments.

The present application belongs to the technical field of unmanned aerial vehicle system control and simulation, and relates to a rotor unmanned aerial vehicle cooperative obstacle avoidance control method based on nonlinear model predictive control. The present application realizes control and online quadratic optimization of lift and three-axis torque received by the unmanned aerial vehicle under the constraint conditions of controlled variables and state variables, and realizes obstacle avoidance trajectory tracking of the unmanned aerial vehicle. Specifically, the high-order system of the unmanned aerial vehicle is reduced in order to process the established high-order system of the rotor unmanned aerial vehicle into a low-order system, so as to ensure real-time performance of subsequent control; an obstacle avoidance path is planned, a feasible trajectory of the rotor unmanned aerial vehicle is planned in real time under the scene with multiple random static obstacles; and a trajectory tracking control is performed, wherein the controller adopts a nonlinear model predictive controller, the optimal control input of the current control period is determined by solving a quadratic programming problem with constraints and is applied to the nonlinear system, so as to realize accurate tracking of the feasible reference path.

This invention discloses an adaptive terrain-following flight control method and system for railway unmanned aerial vehicles (UAVs) that integrates multi-source terrain data and a data-driven model. The method utilizes an extended Kalman filter to estimate the change in rotational inertia online, dynamically correcting a preset initial dynamic model to obtain a dynamic baseline model. Simultaneously, it integrates multi-source terrain data to generate a terrain-following flight reference altitude surface that considers the track's geometric characteristics, using the track centerline as a constraint. Vertical and horizontal trajectories are planned based on the surface curvature, and dynamic yaw angles are calculated. A Gaussian process-enhanced prediction model is constructed, incorporating aerodynamic parameter perturbation learning and L1 adaptive laws into the nonlinear model predictive controller. An inner and outer loop cascaded architecture is used to solve for total lift and three-axis torque control quantities. Flight is driven by a pseudo-inverse assignment matrix mapped to rotor speed commands, and a safe distance is monitored in real time to trigger replanning. This method solves the problems of model mismatch and environmental adaptation in complex environments, improving flight safety and trajectory tracking accuracy.

The application belongs to the field of unmanned aerial vehicle path planning, and relates to an unmanned aerial vehicle path planning method and system based on improved bidirectional RRT and model predictive control. A kinematics and dynamics constraint model is established, and a bidirectional RRT is used to generate a global reference path, so that a global path meeting the physical constraints of the unmanned aerial vehicle is obtained, and the problems of traditional path zigzagging or being unable to track are avoided. A nonlinear model predictive controller is introduced with the global path as a tracking target and an obstacle repulsive potential penalty term, so that the local obstacle avoidance control quantity meeting the physical constraints is generated in real time while the global path is tracked, the cooperation and unification of global guidance and dynamic obstacle avoidance are realized, and the local obstacle avoidance deviating from the task direction is avoided. A global and local collaborative planning mechanism is constructed, a local reference path is regenerated with the current position as a starting point and connected to the global path when a feasible trajectory cannot be found within the preset time of the nonlinear model predictive controller, the problems of traditional replanning lag or failure are solved, and the success rate of the unmanned aerial vehicle path planning is improved.

This invention relates to a digital twin and virtual-real synchronous control method for the fluidized bed granulation process of traditional Chinese medicine (TCM) in the field of TCM pharmaceutical technology. The method includes: constructing a digital twin model; deploying multi-source sensors to collect data in real time; updating the model state through data fusion; predicting particle quality through rolling time-domain simulation; solving for optimal commands using nonlinear model predictive control; and issuing commands to form a virtual-real synchronous closed loop. The physical model employs CFD-DEM coupling, simulating particle aggregation, breakage, and solidification through particle identification and a multi-sphere model; correcting the initial NMPC value based on online detection of extract properties through feedforward compensation; and constructing a multi-model library and achieving adaptive model switching based on operating condition identification. This invention achieves a leap from offline pre-simulation to online control of digital twins, solving technical problems such as difficulty in modeling particle growth, large fluctuations in physical properties, and adaptation to diverse varieties in the TCM granulation process, significantly improving the real-time performance, accuracy, and adaptability of the control.

This invention discloses a multi-objective dynamic collaborative optimization method for industrial circulating water systems based on Bi-LSTM-PINNs-ADMM. Addressing the challenges of high energy consumption, strong coupling among multiple devices, and stringent safety constraints in industrial circulating water systems, this method constructs an optimization architecture of "global multi-objective programming + distributed collaborative iteration." The method utilizes Bi-LSTM combined with an attention mechanism to predict heat load and wet-bulb temperature; constructs a physical information neural network (PINNs) model based on mechanism and data fusion; employs the NSGA-III algorithm to solve for the Pareto optimal solution set for energy efficiency and safety at the global level; utilizes the alternating direction multiplier method (ADMM) to decouple the global objectives and distribute them to distributed device agents; and finally, achieves millisecond-level closed-loop command execution through nonlinear model predictive control (NMPC). This invention solves the problems of model inaccuracy, poor global coordination, and difficulty in balancing multi-objective conflicts in traditional control methods under non-stationary operating conditions, significantly improving the system's energy efficiency and operational stability.

The application discloses a kind of plant cell suspension culture intelligent regulation system and method;The system acquires the parameter in bioreactor in real time by multi-parameter online monitoring module, and the algorithm based on extended Kalman filter (EKF) is used in soft measurement and state estimation module to estimate key state quantity;Cultivation stage automatic identification module is automatically divided according to the logic of multi-criterion fusion;Hierarchical intelligent regulation decision module adopts nonlinear model predictive control algorithm to optimize cell growth trajectory in exponential growth phase, and adopts fuzzy adaptive PID cascade control algorithm to implement accurate regulation to pH, dissolved oxygen and temperature in secondary metabolism induction period, while automatically calculating glucose flow rate according to mixed feeding law.The application realizes the intelligent precision management and control of whole process of plant cell suspension culture, effectively improves the accumulation efficiency and batch repeatability of secondary metabolites.

This invention discloses a nonlinear model predictive control method for obstacle avoidance during NRHO rendezvous, belonging to the field of spacecraft rendezvous control technology. Addressing the simultaneous needs of rendezvous mission and dynamic debris avoidance during NRHO rendezvous under a three-body or high-fidelity ephemeris dynamics environment, modeling and control design are conducted within this high-fidelity ephemeris dynamics environment. A three-dimensional mapping method suitable for non-convex obstacle avoidance domains with apertures in three-dimensional space is constructed, mapping the non-convex feasible domain induced by obstacles in the original space to a convex feasible domain in convex space. A nonlinear model predictive control problem with obstacle avoidance constraints is constructed within the mapped space, unifying rendezvous terminal accuracy, control cost, thrust constraints, and obstacle avoidance constraints into a single optimization framework. This achieves unified closed-loop control of rendezvous and docking and dynamic debris avoidance under a three-body or high-fidelity dynamics background.

The present application relates to a kind of unmanned motorcycle trajectory tracking nonlinear model predictive control method.The method includes: 14 degrees of freedom motorcycle dynamics model is built, including tire force, suspension dynamics and roll dynamics;Based on target trajectory, generate double preview point reference path, and dynamically calculate reference roll angle to adapt to bend curvature;Introduce Lyapunov function derivative constraint in NMPC optimization problem, to ensure roll stability;Real-time trajectory segmentation optimization strategy is used to reduce computational complexity;Through multi-objective cost function, generate optimal handlebar angle and rear wheel driving torque, provide technical support for autonomous stable driving of unmanned motorcycle in complex scene.