40 results about "Linear model predictive control" patented technology

The invention discloses an unmanned aerial vehicle trajectory planning and tracking method, system and device based on deep reinforcement learning and adaptive nonlinear model predictive control, and a medium. The method comprises the following steps: constructing various static multi-obstacle and dynamic multi-obstacle simulation environments; constructing a kinetic model of the unmanned aerial vehicle; constructing an adaptive nonlinear model predictive control (ANMPC) algorithm; constructing a reward function of the tracking performance of the unmanned aerial vehicle to the reference trajectory generated by the adaptive nonlinear model predictive control algorithm; constructing a network framework based on deep reinforcement learning and an adaptive nonlinear model predictive control algorithm; setting network parameters; training a network framework, and selecting an optimal weight file; outputting a test result; the system, the device and the medium are used for realizing the unmanned aerial vehicle trajectory planning and tracking method. The method can effectively cope with changes of targets and environments, shows strong obstacle avoidance capability and anti-interference performance when facing dynamic obstacles and wind noise interference, and embodies a high intelligent decision-making level.

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 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 discloses a robot formation control method based on double-layer nonlinear model predictive control, and relates to the technical field of model predictive control. In order to solve the defects that in the prior art, a pilot vehicle is excessively dependent, and a navigator is difficult to replace quickly during communication packet loss or fault, the technical scheme provided by the invention comprises the following steps: collecting a reference track and initializing control parameters and initial poses of all vehicles; controlling a pilot vehicle according to the reference trajectory and generating an expected formation matrix and an orientation compensation angle; converting the expected formation matrix and the orientation compensation angle into a global target pose of the following vehicle; controlling the following vehicle to perform nonlinear model prediction tracking based on the global target pose of the following vehicle; and when detecting that the pilot vehicle is abnormal, replacing a new pilot vehicle and updating the formation. The method is suitable for unmanned vehicle or multi-robot formation control tasks requiring high-reliability, low-delay switching and real-time formation keeping in a complex dynamic environment.

The invention provides a cooperative heave-rolling compensation method and system for a double-ship lifting arm system, and the method comprises the steps: building a double-ship lifting arm system dynamic model considering heave-rolling coupling motion, and enabling the model to comprise disturbance terms caused by ship motion; based on the kinetic model, generating a nominal trajectory under input saturation constraint and state constraint, and generating an expected trajectory through prediction control of a tightening constraint nonlinear model; for an actual disturbed system, designing a nonlinear robust model prediction controller, and driving an actual state to track the expected trajectory; and the stability of a closed-loop system is guaranteed through a terminal cost function and a terminal constraint set, and cooperative heaving-rolling compensation of the double-ship lifting arm is achieved. According to the method, through dynamic modeling, nominal track generation, robust control and stability guarantee, the double-ship lifting arm cooperative compensation heaving-rolling coupling motion is achieved, multi-degree-of-freedom oscillation can be effectively restrained under the five-level sea condition, and the operation stability and precision under the complex sea condition are improved.

The invention belongs to the technical field of helicopter control, and provides a turboshaft engine identification and prediction control method based on MRR-KELM in order to solve the problem that a prediction model constructed for a turboshaft engine is poor in generalization, an MRR-KELM algorithm is combined with a Huber loss function and a self-adaptive regularization strategy, noise interference and abnormal value influence are restrained, and the prediction model is optimized. The prediction error of the constructed turboshaft engine prediction model is greatly reduced, and adaptive adjustment can be performed through Gaussian kernel dynamic mapping and working condition parameters; according to the algorithm, a regularization item and a robust loss function are introduced, the kernel function mapping capability is combined, the generalization performance and the calculation efficiency of the model under the complex working conditions of small samples, noise interference and the like are remarkably improved, further, the turboshaft engine prediction model is embedded into a nonlinear model prediction control framework, and the prediction accuracy of the turboshaft engine is improved. And high-precision stable control over the rotating speed of the power turbine is achieved through rolling optimization and feedback correction.

The invention discloses a braking method and system of a single-pedal electric vehicle based on intention recognition, and relates to the technical field of electric vehicle braking control. According to the method, dual fuzzy control is designed for the target electric vehicle to determine the braking intensity, the braking intention of a driver is accurately reflected, then braking strategies under different working conditions are designed according to the braking intensity so as to meet the braking requirement of the driver, flexibility is high, and the use effect is good. When regenerative braking is adopted for the rear wheels, linear model predictive control is used for solving the longitudinal dynamic model of the whole vehicle, combined control is conducted on the acceleration, the jerk and the rear wheel slip rate, the requirement of a driver for the current dynamic property of the vehicle is met, it is guaranteed that the vehicle can achieve the ideal braking effect, and the driving safety is improved. Meanwhile, vertical vibration is restrained, the jolting feeling of a driver during riding is reduced, the riding comfort is improved, safety is considered, and the dangerous conditions such as wheel slipping or locking in the braking process are effectively prevented.

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 present invention discloses a quadrotor unmanned aerial vehicle (UAV) obstacle avoidance system based on adaptive nonlinear model predictive control, the system comprising a ground mission management system, a sensing system, an adaptive nonlinear model predictive obstacle avoidance system, and a power system. The present invention also discloses a quadrotor unmanned aerial vehicle (UAV) obstacle avoidance method based on adaptive nonlinear model predictive control, the method comprising the following steps: step 1, obtaining UAV target trajectory information and obstacle information; step 2, obtaining the UAV's real-time status; step 3, obtaining control instructions based on the information in steps 1 and 2. The quadrotor unmanned aerial vehicle (UAV) obstacle avoidance method based on adaptive nonlinear model predictive control provided by the present invention can simultaneously solve the two major problems of fault handling and obstacle avoidance in quadrotor unmanned aerial vehicle flight control, and significantly improve flight performance.

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 present invention discloses a regulation method for open channel gates based on a non-linear model predictive control algorithm, which relates to the technical field of water conveyance projects and includes: using the Saint-Venant equations as the prediction model of the non-linear model predictive control system, and constructing a water diversion outlet model and a gate control model; establishing a real-time state correction framework; substituting the corrected state variables into the real-time state correction framework, and combining with the regulation strategy to conduct water level prediction; establishing an optimization model with the minimization of the sum of squares of the measured water level deviation and the number of gate regulations as the objective, and using the zebra optimization algorithm to solve the regulation strategy; adopting a rolling optimization closed-loop control strategy to dynamically correct the water level prediction result according to the difference between the measured value and the predicted value, and execute the regulation strategy. The present invention realizes real-time tracking of flow rate changes, thereby improving the water level prediction accuracy of the model, reducing the number of control times of the canal pool gates at the same time, improving the gate control efficiency, and ensuring the stability of the canal pool water level and flow rate in the open channel irrigation system.

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.

The present invention relates to an integrated control method for long-distance pursuit and close-range rendezvous of small spacecraft, belonging to the field of aerospace engineering technology. This method considers the orbital dynamics equation of the small spacecraft and environmental disturbances, and establishes the dynamic models of the target spacecraft and the chasing spacecraft; designs a nonlinear model predictive control framework to achieve the motion control of the chasing spacecraft, so that the satellite can achieve rendezvous control with the chasing target while ensuring the actuator saturation constraint; and further uses the fuzzy logic method to fuzzily classify the relative distance between the two spacecraft, and uses the model predictive control plus fuzzy logic tuning parameter controller to control the chasing spacecraft, so that the spacecraft can automatically adjust the weights of position, speed and thrust at long and short distances, and achieve the integrated control of long-distance pursuit and close-range rendezvous of the spacecraft.

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 relates to a biped robot nonlinear model predictive controller FPGA heterogeneous acceleration method, which comprises the following steps: establishing an NMPC model according to a biped robot dynamic model, iteratively solving a quadratic programming form of the NMPC model, and completing a single iteration process: solving by a primitive-dual interior point method and solving a symmetric linear equation set by a minres algorithm; the process of solving the solution of the linear equation set constructed by using the KKT condition based on the quadratic programming form through the primitive-dual interior point method is configured to be executed by a CPU; the process of solving the symmetric linear equation set by the minres algorithm is configured to be executed by the FPGA. According to different calculation characteristics of a solution operator in a biped robot NMPC model, a logic reusable minres algorithm in the solution process is deployed to an FPGA calculation platform, and nonlinear operation is deployed to a CPU for implementation, so that the calculation power characteristic of a heterogeneous chip is fully utilized. According to the method, robot dynamics modeling, an efficient optimization solution algorithm and appropriate computing power resource allocation are considered, and the acceleration effect and verification of the algorithm can be ensured.

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.

The present invention relates to the technical field of UAV control, and particularly to a non-linear model predictive control method for a water-air amphibious UAV, comprising the following steps: constructing a UAV motion mathematical model based on the quadcopter octocopter UAV; constructing a cost function for predictive control of the quadcopter octocopter UAV based on the UAV motion mathematical model; constructing a LESO extended observer according to the torque of the axes or the water resistance of the axes of the quadcopter octocopter UAV in the space coordinate system; obtaining the operating state through the sensors of the quadcopter octocopter UAV, and determining the power distribution mode for each rotor motor of the quadcopter octocopter UAV; solving the cost function according to the power distribution mode and the LESO extended observer to obtain the control results for each rotor motor of the quadcopter octocopter UAV. The present invention optimizes the operating attitude of the water-air amphibious UAV and makes the operating performance more efficient.

The invention discloses a carry-scraper path tracking control method and device based on an improved NMPC control strategy. The method comprises the steps that a kinematics control system model of an underground carry-scraper is established; according to the kinematic control system model, designing a non-linear model predictive controller (NMPC); dynamically optimizing time domain parameters of a nonlinear model predictive controller (NMPC) by using a particle swarm optimization (PSO) algorithm based on the path reference point; the optimized time domain parameters are transmitted into the non-linear model prediction controller NMPC, and the control quantity is output to act on a steering mechanism of the carry-scraper to complete path tracking. The method optimizes and improves the time domain parameters of the NMPC by using the particle swarm optimization algorithm, can be widely applied to the path tracking control process of the articulated vehicle, effectively avoids steering delay, snakelike displacement and the like of the vehicle, greatly improves the path tracking control precision, is particularly suitable for the control process of an underground carry-scraper, and has a wide application prospect. And reliable technical support is provided for unmanned driving of the carry-scraper.

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 present invention belongs to the field of industrial automation and discloses a non - linear predictive function control method for polyolefin processes. First, the time constant and lag time of the process model are identified according to the step - response data of the non - linear chemical process collected. Then, the gain is deduced based on the mechanism model formula of the non - linear process. After that, a model predictive controller for the non - linear chemical process is designed. Finally, the obtained precise optimal control law is implemented in the controlled non - linear chemical process. The technical solution of the present invention is to establish a non - linear model predictive control method for chemical processes based on mechanism models through means such as step - response data acquisition of non - linear chemical processes, partial model parameter identification, gain formula derivation, and non - linear model predictive controller design, avoiding the prediction errors caused by model linearization in traditional model predictive control, ultimately achieving precise control of non - linear chemical processes and further enhancing the stability of non - linear chemical process production.