Adaptive PID controller based on Lyapunov function neural network for time delay temperature control

2015 ◽  
Author(s):  
Muhammad Saleheen Aftab ◽  
Muhammad Shafiq
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Lyapunov Function ◽  
Temperature Control ◽  
Pid Controller

System of Annealing Furnace Temperature Control Based on Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 881-884
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Nonlinear Approximation ◽  
Annealing Furnace ◽  
Furnace Temperature ◽  
Control Precision ◽  
High Control ◽  
Adaptive Ability

In the thesis the adaptive ability of neural network strong and good nonlinear approximation ability, A controller is designed based on BP neural network by the adaptive ability of neural network strong and good nonlinear approximation ability in this paper, this method changed defect of the usual PID controller that parameters of annealing furnace condition are not easy set and the ability to adapt is poor. The new method is not only has good stability, but also has high control precision and strong adaptability.

Smith predictor based fractional-order integral controller for robust temperature control in a steel slab reheating furnace

2019 ◽  
Vol 41 (16) ◽  
pp. 4521-4534 ◽  
Author(s):  
Vicente Feliu-Batlle ◽  
Raul Rivas-Perez
Keyword(s):  
Time Delay ◽  
Fractional Order ◽  
Temperature Control ◽  
Pid Controller ◽  
Large Time ◽  
Smith Predictor ◽  
Reheating Furnace ◽  
Steel Slab ◽  
Integral Controller ◽  
Fractional Order Integral

In this paper, a new strategy for robust control of temperature in a steel slab reheating furnace with large time delay uncertainty based on fractional-order controllers combined with a Smith predictor is proposed. A time delay model of the preheating zone of this process is used, obtained from an identification procedure applied in a real industrial furnace. It is shown that this process experiences very large time delay changes. A fractional-order integral controller embedded in a Smith predictor structure (FI-SP) is designed, which is robust to changes in such time delay. Simulated results of a standard Proportinal Integral Derivative (PID) controller, a PID controller embedded in a Smith predictor and the proposed FI-SP controller are compared. Six performance indexes have been used in this comparison. The analysis of these indexes shows that the designed FI-SP controller exhibits the most robust behavior (lowest indexes averaged in all the range of time delay variation) for ranges that include large time delays. Then the robustness features of the FI-SP controller outperform the other integer order controllers in the time responses both to set-point changes and to step disturbances. Therefore, this controller guarantees the best accuracy of temperature control. The designed FI-SP guarantees system stability and robust performance for a high range of plant time delay uncertainties.

Comparison of Performance of Optimized Varela Immune Controller and PID Controller for Control of Time-Delay Process

2015 ◽  
Vol 18 ◽  
pp. 103-111
Author(s):  
Alexander Hošovský ◽  
Mária Tóthová ◽  
Kamil Židek
Keyword(s):  
Simulated Annealing ◽  
Time Delay ◽  
Time Constant ◽  
Temperature Control ◽  
Process Parameters ◽  
Pid Controller ◽  
Complex Structure ◽  
The Other ◽  
Nonlinear Controller ◽  
Long Time

Varela immune controller is a kind of nonlinear controller, which is said to have good anti-delay capabilities. We compare the performance of simulated annealing optimized improved Varela immune controller and optimized PID controller for controlling a process with very long time delay (approximation of biomass-fired boiler temperature control). The results confirm that Varela immune controller is indeed capable of stabilizing the process while being very robust even to extreme changes in process parameters (time constant and time delay). In addition to that, it is also found out that properly (optimally) tuned PID controller is capable of achieving similar performance. The problem of controller tuning is relevant for both controllers but there are no tuning rules for immune controllers, which might favor the use of conventional PID controller. On the other hand, Varela controller has greater flexibility due to its more complex structure, which might help to adapt it to some special kinds of processes.

Design of PID Temperature Control System Based on BP Neural Network

2014 ◽  
Vol 602-605 ◽  
pp. 1244-1247
Author(s):  
Zhi Yong Meng ◽  
Guo Qing Yu ◽  
Rui Jin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Simulation Analysis ◽  
Nonlinear Function ◽  
Temperature Control System ◽  
Bp Network ◽  
Pid Algorithm

Based on BP neural network PID controller has the ability to approximate any nonlinear function, can achieve real-time online tuning PID controller parameter . Through the system simulation analysis, simulation results show that the BP neural network tuning PID control than traditional PID algorithm and BP network algorithm has a greater degree of improvement, the system has better robustness and adaptability, its output can also achieve the desired control accuracy through online adjustments. Suitable for temperature control system.

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