Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modem power pla...Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modem power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network （NFGPC） is proposed in this paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained,展开更多
An AC servo system based on neuron control theory is presented. Experimental results show that the neuralcontrol mode doesn't need the Precise model of the system, therefore, it has many advantages, such as simple...An AC servo system based on neuron control theory is presented. Experimental results show that the neuralcontrol mode doesn't need the Precise model of the system, therefore, it has many advantages, such as simple designand high response performance. The simulation research of the AC servo system which is non-linear, time-varied.based on neuro-fuzzy controller is done. The results of the simulation show that the performances of the system areconsiderably improved and it is one of the novel pathways to realize intelligent control of servo system.展开更多
In unit steam-boiler generation, a coordinated control strategy is required to ensure a higher rate of load change without violating thermal constraints. The process is characterized by nonlinearity and uncertainty. W...In unit steam-boiler generation, a coordinated control strategy is required to ensure a higher rate of load change without violating thermal constraints. The process is characterized by nonlinearity and uncertainty. While neural networks can model highly complex nonlinear dynamical systems, they produce black box models. This has led to significant interest in neuro-fuzzy networks (NFNs) to represent a nonlinear dynamical process by a set of locally valid and simpler submodels. Two alternative methods of exploiting the NFNs within a generalised predictive control (GPC) framework for nonlinear model predictive control are described. Coordinated control of steam-boiler generation using the two nonlinear GPC methods show excellent tracking and disturbance rejection results and improved performance compared with conventional linear GPC.展开更多
The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner. NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system d...The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner. NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system development is given. The rule structure utilizes sigmoid functions to fuzzify the inputs, multiplication to combine the if Part of the rules and summation to integrate the fired rules. Expert knowledge from previous process Plans is used in determinning the initial network structure and parameters of the membership functions. A back-propagation (BP) training algorithm was developed to fine tune the knowledge to company standards using the input-output data from executions of previous plans. The method is illustrated by an industrial example.展开更多
基金This work was supported by the Natural Science Foundation of Beijing (No. 4062030)National Natural Science Foundation of China (No. 50576022,69804003)Scientific Research Common Program of Beijing Municipal Commission of Education (KM200611232007).
文摘Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modem power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network （NFGPC） is proposed in this paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained,
文摘An AC servo system based on neuron control theory is presented. Experimental results show that the neuralcontrol mode doesn't need the Precise model of the system, therefore, it has many advantages, such as simple designand high response performance. The simulation research of the AC servo system which is non-linear, time-varied.based on neuro-fuzzy controller is done. The results of the simulation show that the performances of the system areconsiderably improved and it is one of the novel pathways to realize intelligent control of servo system.
基金Supported by National Natural Science Foundation of P. R. China (69804003, 50576022)the Natural Science Foundation of Beijing (4062030)
文摘In unit steam-boiler generation, a coordinated control strategy is required to ensure a higher rate of load change without violating thermal constraints. The process is characterized by nonlinearity and uncertainty. While neural networks can model highly complex nonlinear dynamical systems, they produce black box models. This has led to significant interest in neuro-fuzzy networks (NFNs) to represent a nonlinear dynamical process by a set of locally valid and simpler submodels. Two alternative methods of exploiting the NFNs within a generalised predictive control (GPC) framework for nonlinear model predictive control are described. Coordinated control of steam-boiler generation using the two nonlinear GPC methods show excellent tracking and disturbance rejection results and improved performance compared with conventional linear GPC.
文摘The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner. NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system development is given. The rule structure utilizes sigmoid functions to fuzzify the inputs, multiplication to combine the if Part of the rules and summation to integrate the fired rules. Expert knowledge from previous process Plans is used in determinning the initial network structure and parameters of the membership functions. A back-propagation (BP) training algorithm was developed to fine tune the knowledge to company standards using the input-output data from executions of previous plans. The method is illustrated by an industrial example.
