Intelligent coordinated controller design for a 600MW supercritical boiler unit based on expanded-structure neural network inverse models

2016 ◽  
Vol 53 ◽  
pp. 194-201 ◽  
Author(s):  
Liangyu Ma ◽  
Kwang Y. Lee ◽  
Zhiyan Wang
Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 831
Author(s):  
Izzat Al-Darraji ◽  
Dimitrios Piromalis ◽  
Ayad A. Kakei ◽  
Fazal Qudus Khan ◽  
Milos Stojemnovic ◽  
...  

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.


2011 ◽  
Vol 467-469 ◽  
pp. 1505-1510
Author(s):  
Dan Liu ◽  
Ni Hong Wang ◽  
Gui Ying Li

This paper proposes a new method that it uses the neural network to construct the solution of the Hamiltion-Jacobi inequality (HJ), and it carries on the optimization of the neural network weight using the genetic algorithm. This method causes the Lyapunov function to satisfy the HJ, avoides solving the HJ parital differential inequality, and overcomes the difficulty which the HJ parital differential inequality analysis. Beside this, it proposes a design method of a nonlinear state feedback L2-gain disturbance rejection controller based on HJ, and introduces general structure of L2-gain disturbance rejection controller in the form of neural network. The simulation demonstrates the design of controller is feasible and the closed-loop system ensures a finite gain between the disturbance and the output.


Author(s):  
Xiong Yin ◽  
Kai Wen ◽  
Yan Wu ◽  
Lei Zhou ◽  
Jing Gong

Abstract In recent years, China ramped up imports of natural gas to satisfy the growing demand, which has increased the number of trade meters. Natural gas flowmeters need to be calibrated regularly at calibration stations to ensure their accuracy. Nowadays, the flow metrological calibration process is done by the operator manually in China, which is easy to be affected by personnel experience and proficiency. China is vigorously developing industry 4.0 and AI(artificial intelligence) technologies. In order to improve the calibration efficiency, a design scheme of intelligent controller for flow metrological calibration system is first proposed in this paper. The intelligent controller can replace the operator for process switching and flow adjustment. First, the controller selects the standard flowmeter according to the type of the calibrated flowmeter, and switches the calibration process. To accurately control the calibration flow for 180 seconds, the controller continuously adjusts the regulating valve with a sequence of commands to the actuator. These commands are generated by intelligent algorithm which is predefined in the controller. Process switching is operated automatically according to flowmeter calibration specifications. In order to reach the required flow point quickly, the flow adjustment is divided into two steps: preliminary adjustment and precise adjustment. For preliminary adjustment, a BP neural network will be built first using the field historical data and simulation results. This neural network describes the relationship between the valve-opening scheme and the calibration flow. Therefore, it could give a calibration flow as close as possible to the expected value during calibration. For precise adjustment, an adaptive PID controller is used. It could adjust the valve opening degree automatically to make sure the flow deviation meet the calibration requirements. Since the PID controller is a self-adaptive PID controller, the process of adjustment is very quick, which can reduce the calibration time largely. After each calibration, both the original neural network and the adaptive function of the controller will be updated to achieve the self-growth. With the information of the calibrated flowmeter, the entire calibration system can run automatically. The experiment in a calibration station shows that the intelligent controller can control the deviation of the flow value within 5% during 4∼5 minutes.


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