38 results about "Linear model predictive control" patented technology

A non-linear dynamic predictive device (60) is disclosed which operates either in a configuration mode or in one of three runtime modes: prediction mode, horizon mode, or reverse horizon mode. An external device controller (50) sets the mode and determines the data source and the frequency of data. In the forward modes (prediction and horizon), the data are passed to a series of preprocessing units (20) which convert each input variable (18) from engineering units to normalized units. Each preprocessing unit feeds a delay unit (22) that time-aligns the input to take into account dead time effects. The output of each delay unit is passed to a dynamic filter unit (24). Each dynamic filter unit internally utilizes one or more feedback paths that provide representations of the dynamic information in the process. The outputs (28) of the dynamic filter units are passed to a non-linear approximator (26) which outputs a value in normalized units. The output of the approximator is passed to a post-processing unit (32) that converts the output to engineering units. This output represents a prediction of the output of the modeled process. In reverse horizon mode, data is passed through the device in a reverse flow to produce a set of outputs (64) at the input of the predictive device. These are returned to the device controller through path (66). The purpose of the reverse horizon mode is to provide information for process control and optimization. The predictive device approximates a large class of non-linear dynamic processes. The structure of the predictive device allows it to be incorporated into a practical multivariable non-linear Model Predictive Control scheme, or used to estimate process properties.

The object of the invention is a humanoid robot (100) with a body (190) joined to an omnidirectional mobile ground base (140), equipped with : - a body position sensor, a base position sensor and an angular velocity sensor to provide measures, - actuators (212) comprising at least 3 wheels located in the omnidirectional mobile base, - extractors (211) for converting sensored measures into useful data, - a supervisor (500) to calculate position, velocity and acceleration commands from the useful data, - means for converting commands into instructions for the actuators, characterized in that the supervisor comprises: - a no-tilt state controller (501), a tilt state controller (502) and a landing state controller (503), each controller comprising means for calculating, position, velocity and acceleration commands based on a double point-mass robot model with tilt motion and on a linear model predictive control law, expressed as a quadratic optimization formulation with a weighted sum of objectives, and a set of predefined linear constraints.

The object of the invention is a humanoid robot (100) with a body (190) joined to an omnidirectional mobile ground base (140), and equipped with: - a body position sensor and a base position sensor to provide measures, - actuators (212) comprising at least 3 wheels (141) located in the omnidirectional mobile base, - extractors (211) for converting the measures into useful data, - a controller to calculate position, velocity and acceleration commands from the useful data using a robot model and pre-ordered position and velocity references, - means for converting the commands into instructions for the actuators, characterized in that the robot model is a double point-mass model, and in that the commands are based on a linear model predictive control law with a discretized time according to a sampling time period and a number of predicted samples, and expressed as a quadratic optimization formulation with: - a weighted sum of objectives, - a set of predefined linear constraints.

The invention provides a path tracking control method of a handling robot based on a neural network, which can improve the real-time performance of nonlinear model predictive control. The method includes: using nonlinear model predictive control to generate a training sample set, wherein the training sample includes: state variables and control variables of the handling robot; constructing a neural network model; using the obtained training sample set to train the constructed neural network model , to obtain a trained neural network model; wherein, in the path tracking control process, the trained neural network model outputs control variables, so that the handling robot performs path tracking according to the control variables output by the neural network model. The invention relates to the field of autonomous driving control of mobile robots.

The invention discloses a nonlinear predictive control method based on a multi-core support vector machine for a tank reactor, which belongs to the field of industrial automatic control. The control method mainly includes modeling and predictive control closed-loop design based on the multi-core support vector machine. The vector machine model is composed of the dynamic part based on the linear kernel support vector machine in series with the static part based on the spline kernel support vector machine; the multi-core support vector machine model is established according to the static and dynamic input and output data of the tank reactor. The future reference trajectory of the tank reactor temperature can be changed through the inverse based on the spline kernel support vector machine, and the nonlinear predictive control can be changed into a linear predictive control for the linear kernel support vector machine model, and then obtained according to the predictive control mechanism. The multi-step predictive control has an analytical solution of the optimal control law in a unified form, which acts on the reactor to make the temperature close to the set value to complete the control cycle.

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 invention discloses a method for compensating digital control delay of a magnetic bearing switch power amplifier, which is a method capable of compensating the digital control delay of a magnetic bearing switch power amplifier. The method comprises the steps of setting asymmetric factors of asymmetric sampling resistor networks according to a linear model predictive control theory on the basis of measuring the digital control delay of a switch power amplifier system and equivalent inductance and resistance of a magnetic bearing winding, and finally setting the asymmetric sampling resistance networks according to the asymmetric factors. The method for compensating the digital control delay of the magnetic bearing switch power amplifier belongs to the technical field of magnetic bearing control and can be applied to the fast response and high-stable control of a high-speed magnetic suspension rotor system.