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

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

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

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

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

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

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

The invention discloses a shape adaptive planning and control method for a deformable unmanned aerial vehicle, and the method comprises the steps: carrying out the searching based on an occupied grid map of a scene through employing a variable-size kinematics A * path planning algorithm, and obtaining an initial path; by taking the initial path as an initial condition, shape-adaptive trajectory optimization in a continuous space-time space is carried out to obtain an optimized trajectory, and in the trajectory optimization process, the mass center position and deformation parameters of the unmanned aerial vehicle are optimized at the same time; and based on the optimized trajectory, on the basis of a nonlinear model predictive controller and by combining an incremental nonlinear dynamic inverse algorithm, calculating and compensating external force disturbance and external torque disturbance caused by deformation of the unmanned aerial vehicle and a load, so as to realize shape adaptive control of the deformable unmanned aerial vehicle.

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

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

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

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

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

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

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

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

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

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

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

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

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

The invention discloses a dynamic obstacle avoidance control method for an unmanned ship, and the method comprises the steps: multi-sensor data fusion and environment modeling, dynamic obstacle trajectory prediction, deep reinforcement learning path planning, nonlinear model prediction control, and final planning algorithm summarization. Obstacle avoidance control is carried out through multi-sensor data fusion and environment modeling, dynamic obstacle trajectory prediction, deep reinforcement learning path planning, nonlinear model prediction control and final planning algorithm summarization, and the method comprises multi-modal sensor fusion, LSTM trajectory prediction, deep reinforcement learning decision and nonlinear model prediction control. High-precision real-time obstacle avoidance under a complex water area is achieved, the whole method combines dynamic modeling and optimization control, the path safety, efficiency and energy utilization rate are remarkably improved, and the method is suitable for the fields of agricultural unmanned feeding, unmanned water quality intelligent detection and the like.

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

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

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

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

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

The invention discloses a slip rate control method for a turning working condition of a distributed driving vehicle, and belongs to the field of vehicle dynamics control. The method comprises the following steps: firstly, establishing a Brush tire model to accurately represent the mechanical properties of the tire, secondly, determining the optimal slip rate considering the tire slip angle by combining the normalized measurement of tire slip and the tire workload, introducing the road holding allowance, optimizing the optimal slip rate, and finally, determining the optimal slip rate based on nonlinear model predictive control. And the NMPC is used for realizing dynamic tracking and constraint control of the slip rate. By means of the method, it can be guaranteed that the vehicle tire force is maximized under the extreme working conditions such as sharp turning on the wet and slippery road surface, and meanwhile the safety of the vehicle is improved by reserving the road holding allowance.

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

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

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