53 results about "Nonlinear predictive control" patented technology

A method is disclosed for controlling a methanol carbonylation process for producing acetic acid. The method includes the steps of monitoring the rate of production of the acetic acid; reducing the production rate in response to a change in a process condition or a process equipment condition; after the production rate is reduced, controlling the process at the reduced production rate; and increasing the production rate after the condition change has been addressed until at least the production rate returns to a normal operating range; wherein during at least one of the steps of reducing the production rate, controlling the process at the reduced production rate, and increasing the production rate until the production rate returns to a normal operating range, the process is controlled by nonlinear multivariable control based on a model of the process.Also disclosed is a process for producing acetic acid, in which at least a reaction section of the process is controlled using a multivariable nonlinear predictive controller based on a nonlinear model of the process. Control of the process is based on the same model during normal operations, during process-upset conditions and also during a recovery period after the upset has been addressed.

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 present invention provides a closed loop neural stimulation simulation system for a Parkinson state. The method comprises a basal nucleus- thalamus loop, a DAC, an ADC, a non-linear autoregressive Volterra model and a non-linear model prediction controller, which are connected to each other. The system comprises a basal nucleus-thalamus loop that is established by an FPGA, a DAC, an ADC, a non-linear autoregressive Volterra model and a non-linear model prediction controller. The DAC and the ADC are respectively connected to an input end and an output end of the basal nucleus-thalamus loop, and are used for acquiring input and output data of the basal nucleus-thalamus loop. The effects of the system are as follows: a basal nucleus-thalamus loop modeling method driven by physiological data is proposed, so that modeling mismatching and uncertainty that exist in conventional physiological modeling can be effectively dealt with; a non-linear prediction control policy based on the model is capable of automatically and optimally adjusting a clinical state; and an FPGA hardware technology is used as an implementation means, and the characteristics of an FPGA parallel operation, a high operation speed and high calculation precision are fully exerted, and the response speed and control effect of a control system are improved.

The invention discloses a combustion optimization control method for a boiler. The combustion optimization control method is characterized by comprising the following steps: sampling a combustion nonlinear system of the boiler to obtain input / output data at the current moment; training the real-time sampled input / output data by an online incremental learning fuzzy neural network, building an online incremental learning predicting model of the combustion nonlinear system of the boiler; performing a nonlinear prediction control algorithm on the online incremental learning predicting model for realizing the optimization and the control of the combustion process of the boiler. According to the combustion optimization control method for the power station boiler of the online incremental learning fuzzy neural network, the nonlinear optimization problem in the predication control algorithm is solved by utilizing a particle swarm optimization algorithm through the online identification of the boiler combustion optimization model; the real-time optimization and control of the boiler combustion process are realized.