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

The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input parameter is a controlled variable. This software tool is further operable to determine relationships between the variable inputs and controlled variables. A control system that provides variable inputs to and acts on controller outputs from the software tools tunes one or more manipulated variables to achieve a desired controlled variable, which in the case of a particle accelerator may be realized as a more efficient collision.

The invention belongs to the technical field of a path planning for unmanned vehicles, and especially relates to a dynamic trajectory planning method for an unmanned vehicle based on local optimum. The method is characterized by, according to different positions and corresponding different operating conditions of obstacle vehicles on the road, selecting an optimal reference trajectory and carryingout dynamic trajectory planning; analyzing unmanned vehicle lane change intention generation and lane change executable conditions, and according to the prediction of the position and speed of surrounding obstacle vehicles, carrying out fitting to obtain a locally optimal lane change trajectory at the initial moment when obstacle avoidance lane change is decided, and serving the optimal trajectory as a local reference trajectory; and generating a trajectory cluster that can be driven by the unmanned vehicles and combining designed velocity distance and cost functions with a loss function, andselecting an optimal trajectory from the trajectory cluster through nonlinear model predictive control. The method can realize obstacle avoidance, lane change and overtaking in various complex conditions, and also takes into account the comfort of passengers in the unmanned vehicles and road driving efficiency and the like.

The invention provides a sewage disposal process multi-purpose control method based on nonlinear model prediction aiming at the characteristics that a sewage disposal process is high in nonlinearity, strong in coupling, large in time varying and lag, severe in uncertainty and the like. Control of concentration of dissolved oxygen (DO) and nitric nitrogen (SNO) in the sewage disposal process is achieved. According to the control method, a sewage disposal process prediction model is established, a nonlinear model prediction control method is used for multipurpose control, and accordingly control effect is improved, the dissolved oxygen and the nitric nitrogen can meet expected requirements quickly and accurately, and the problem that self-adaptive capacity is bad based on switch control and PID control currently is solved. Experiment results show that the concentration of the dissolved oxygen and the nitric nitrogen can be quickly and accurately controlled through the method, strong self-adaptive capacity is achieved, quality and efficiency of sewage treatment can be improved, sewage treatment cost is lowered, and sewage treatment plant high-efficiency and stable operation is facilitated.

The invention provides an autonomous driving path tracking control method of a crawler-type mobile robot. With the autonomous driving path tracking control method of the crawler-type mobile robot adopted, the accuracy and reliability of the path tracking of the mobile robot can be improved. The method comprises the following steps that: the current pose information, mass center speed information and driving wheel rotating speed information of the crawler-type mobile robot are acquired; the pose information of the crawler-type mobile robot in a prediction time domain is determined according to the obtained information through the kinematics and dynamics models of the crawler-type mobile robot, and the determined pose information is compared with expected path information, so that pose deviation in the prediction time domain can be obtained; with minimizing the sum of predicted pose deviation, predicted control quantity increment and predicted course angular velocity change in a path tracking process adopted as an objective, the optimization function of nonlinear model prediction control can be determined; the optimization function is minimized through constraint conditions, so that an optimal control sequence can be obtained; and therefore, the crawler-type mobile robot tracks an expected path according to the output pose information of the optimal control sequence so as to autonomously travel.

The invention relates to the field of vehicle control, in particular to a nonlinear model prediction control method of regenerative braking of an electric vehicle. The method adopts a nonlinear model prediction control strategy of a rapid system, on the basis of establishing an electric vehicle regenerative braking nonlinear model, subsystem operating state signals, acquired by sensors, of the entire vehicle, wheels, suspensions and motors in the braking process of the electric vehicle are acquired, and multiple purposes, namely controlling speed accurately, guaranteeing vehicle stability, improving driving smoothness and regenerative braking energy recovering efficiency, in the braking process are controlled coordinately. The control system is mainly applied into the driving and braking processes of traditional vehicles and electric vehicles, especially the multipurpose controlling process of strong nonlinear systems.

The invention discloses an adaptive segmentation multistage linear spectrum generalized standard control missdistance reentry guidance method. The guidance method is applied to a high-lift-drag-ratio reentry aircraft and can satisfy multiple terminal constraints and process constraints. The method combines basic ideas of nonlinear model prediction control, a multistage linear pseudo spectrum method and calculus of variations, is greatly different from a Yu method in that a processing object is reduced-order nonlinear reentry dynamics taking a coupling effect into consideration, and also takes terminal heights and trajectory inclination angle constraints into consideration apart from considering a terminal position requirement and a roll angle requirement.

The invention discloses a vehicle longitudinal follow control method in expressway heavy truck queue driving. The vehicle longitudinal follow control method comprises the steps that based on a nonlinear model predictive control theory, according to obtained current road information and taking into account constraint of a physical actuator, the situation that vehicles in a queue is controlled to maintain the same speed with adjacent front vehicles and to maintain the desired vehicle distance is taken as the control target, and the control quantity is optimized and calculated and acts on a controlled vehicle; and based on a nonlinear dynamic equation of the vehicle, the state of the adjacent front vehicles at the next moment is predicted and serves as a part of a tracked and controlled target, and the speed deviation between the controlled vehicle and the front vehicles due to vehicle-mounted controller delay, transmission system delay and communication delay is reduced effectively; andtherefore, the vehicle distance can be controlled at a smaller numerical value in the travelling process of the vehicle queue, and the vehicle longitudinal follow control method indirectly improves the overall fuel economy of the vehicle queue according to the relationship between the air resistance and the vehicle distance in the travelling process of the vehicle.

The invention relates to changeable structure non linear model predictive controller. It is made up of online real time configuration machine, non linear model predictive control calculator, control action output processor, controlled variable constrained optimizer, and operational variable constrained optimizer. The online real time configuration machine can self organize current proper controller structure by which the non linear time lag calculating and on line real time predictive calculating can be processed. It has the features of multi predictive time domain online correction, single value predictive control, state feedback, non linear weighting, multi-cycle control etc, forms multi variable changeable structure non linear model predictive controller and control system on model and structure to adapt controlled process, make it always be in optimize running state.

The invention relates to a vehicle active safety control method, and particularly relates to a vehicle lateral stability nonlinear integration control method. Firstly, a simplified vehicle dynamics model is built; then, design of a nonlinear model predictive controller is carried out, expected yaw angle velocity information is inputted to the nonlinear controller module, according to the value of the expected yaw angle velocity and sideslip angle velocities of a front wheel and a rear wheel of the vehicle and the yaw angle velocity fed back in real time, a model predicative control method is used for predicting future dynamic performance of the system, optimization is carried out at the same time, an additional yaw moment and the optimized steering wheel angle information are decided and outputted to an execution mechanism corresponding to the vehicle, and the vehicle is kept in a yaw stability state. The method of the invention is successfully realized by the controller through FPGA full hardware, the FPGA uses a parallel hardware calculation method for acquiring the optimized control sequence in a limited sampling time domain, requirements of high real-time performance and miniaturization on the vehicle-mounted nonlinear model predictive controller can be met, and the calculation performance of the control system is improved.

The present invention provides novel techniques for controlling batch reaction processes. In particular, a parametric hybrid model may be used to parameterize inputs and outputs of batch reaction processes. The parametric hybrid model may include an empirical model, a parameter model, and a dynamic model. Critical quality parameters, which are correlated with, but not the same as, end-of-batch quality values for the batch reaction processes may be monitored during cycles of the batch reaction processes. The quality parameters may be used to generate desired batch trajectories, which may be used to control the batch reaction processes during the cycles of the batch reaction processes.

The invention discloses a trajectory tracking control method and system based on longitudinal and transverse coordination. The trajectory tracking control method comprises the steps of information perception, trajectory planning, control layer modeling and driving execution. According to information perception, traffic environment information and vehicle state information of the intelligent vehicle are collected in real time; according to path planning, an expected path is planned according to information perception data; according to control layer modeling, a longitudinal and transverse coordination strategy is established according to a preview principle, an expected longitudinal and transverse control instruction is processed and the expected longitudinal and transverse control instruction is converted into an executable control instruction physical value; and according to driving execution, an execution mechanism of the vehicle is executed according to the physical value of the control instruction to realize the overall control of the vehicle. According to the method, a simple two-wheel dynamic model and kinematics are adopted for modeling the unmanned vehicle, feedback and feedforward steering control is designed through a backstepping method principle according to road information and vehicle motion characteristics, and compared with traditional PID control and nonlinearmodel prediction control, the accuracy and the real-time performance are improved.

The invention relates to the field of the gait optimization control for a biped robot and particularly relates to a model predictive control-based trust region-SQP method for the gait optimization of the biped robot. Based on the nonlinear model predictive control technology, the trust region-SQP method is adopted to realize the superlinear convergence rate and the global convergence fast-solving optimal control, so that the optimal movement gait of the biped robot is realized. Since the nature of an objective function is poor, a significant amount of time is spent in determining a searching step length. Therefore, the real-time performance is invalid. According to the technical scheme of the invention, based on the nonlinear model predictive control technology, a robot dynamics model is converted into a non-linear optimization model, and the trust region-SQP method having fast convergence characteristics is provided. In this way, the optimal control, which realizes the real-time performance, is solved. The method overcomes the defect that the real-time performance cannot be realized during the solving process of a traditional controller. The method can be applied to multi-degree-of-freedom biped robots and provides references for the real-time control solutions of multi-degree-of-freedom biped robots.

The invention discloses a non-linear model predictive control method of a piezoelectric actuator. The method comprises the following steps that a multi-layer neurons network is used for carrying out modeling on the piezoelectric actuator to obtain a neuron network model of the piezoelectric actuator; a non-linear model predictive controller is arranged and optimized; based on the neuron network model of the piezoelectric actuator and the non-linear model predictive controller, displacement of the piezoelectric actuator is controlled in real time. According to the method, based on the related content of the optimization theory, the displacement of the piezoelectric actuator is controlled in real time through the neuron network model and the non-linear model predictive controller, the inherent delay problem of the piezoelectric actuator is solved, and real-time tracking control over displacement of the piezoelectric actuator is achieved.

A carrier-based aircraft autonomous landing method based on explicitly nonlinear model predictive control comprises a first step of simplifying a longitudinal motion nonlinear equation according to a flying condition of an aircraft; a second step of determining a control quantity and a controlled quantity; a third step of determining a relative order of each output quantity; a fourth step of, as K is a diagonal matrix composed of Ki, solving for the value of Ki through a first row of a matrix (as shown in the description); a fifth step of carrying out Taylor expansion for a command signal yDi, and obtaining an all-order derivative function of the command signal; a sixth step of carrying out integration to obtain the control quantity; and a seventh step of obtaining a carrier-based aircraft autonomous landing longitudinal control law based on the explicitly nonlinear model predictive control. The invention enables a carrier-based aircraft to quickly track the command signal autonomously, avoids online optimization process, saves a lot of time, and saves hardware resources. The explicitly nonlinear model predictive control can be widely applied to the nonlinear control fields of robots, aviation, aerospace, industrial production and so on.

The invention discloses a design of a nonlinear predictive controller for a permanent magnet synchronous motor with a disturbance observer, and belongs to the technical field of a high-performance motor driving control system. According to the invention, firstly, all model errors and external disturbances are considered under a dq coordinate system, and a nonlinear mathematical model of the PMSM (Permanent Magnet Synchronous Motor) is constructed; secondly, predictive controllers of an outer speed loop and an inner current loop are respectively designed on the basis of the model, and a disturbance observer is designed when there is a control device limitation. The design disclosed by the invention overcomes a defect that the system has limitations for processed variables and greatly depends on electric parameters of the motor on the aspect of the limiting current through designing the nonlinear model predictive controller with a cascade structure and designing the anti-saturation disturbance observer, and the disturbance is considered and compensated in predictive controller, thereby enhancing the robustness of the permanent magnet synchronous motor control system. Experiments showthat the method can enable the system to output a more accurate tracking reference trajectory, and it is considered that the current limitation can keep high robustness when there is a model parameter error or load change at the same time.

A controller includes circuitry configured to detect an occurrence of a transient instability event at a multi-machine power system (MMPS) based on one or more sensed operational parameters at one or more energy generation devices. Excitation voltage input values to the one or more energy generation devices are determined over a predetermined prediction horizon based on minimizing a predetermined cost function bound by one or more constraints. Control signals are output to one or more actuators associated with the energy generation devices based on the excitation voltage input values to reduce a length of time of the transient instability event.

The invention discloses an aero-engine direct thrust control method based on nonlinear model prediction control. Thrust directly serves as the control target in the method, and the situation that parameters which cannot be measured serve as the control target in a traditional control method is avoided. An online sliding window deep neural network serves as a prediction model, a deep learning structure is adopted in the model, the model precision can be improved, training data are selected online on the basis of a sliding window method, and sensibility for training data noise is lowered. Compared with current popular control methods, through the provided method, the acceleration time is shortened by 0.425 seconds, and the response speed is increased by about 1.14 times.