Experimental Comparison of Two Integer Valued Iterative Learning Control Approaches at a Stator Cascade

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Florian Arnold ◽  
Karl Neuhäuser ◽  
Rudibert King

Abstract Experimental and simulative investigations have shown that active flow control (AFC) is an effective method to influence flow conditions within a compressor. This can be used for different cases like mitigating flow separation or to ensure a uniform flow throughout a compressor stage. Control performance can be improved by making use of a cyclic character found in the rotor/stator interaction or found in new gas turbine setups exploiting cycling combustion. To this end, iterative learning control (ILC) is applied. To achieve a fast actuation, irrespective of the implemented control method, solenoid valves should be installed instead of proportional valves. Unfortunately, the binary character of these valves does not allow the application of conventional control methods, e.g., real-valued ILC. This contribution presents two options to handle the binary control domain in the context of an ILC. Both approaches are tested in a simulation study first to analyze the behavior. Then they are applied to a real test rig featuring a linear stator cascade.

Author(s):  
Florian Arnold ◽  
Karl Neuhäuser ◽  
Rudibert King

Abstract Experimental and simulative investigations have shown that Active Flow Control is an effective method to influence flow conditions within a compressor. This can be used for different cases like mitigating flow separation or to ensure a uniform flow throughout a compressor stage. Control performance can be improved by making use of a cyclic character found in the rotor/stator interaction or found in new gas turbine setups exploiting cycling combustion. To this end, Iterative Learning Control (ILC) is applied. To achieve a fast actuation, irrespective of the implemented control method, solenoid valves should be installed instead of proportional valves. Unfortunately, the binary character of these valves does not allow the application of a conventional control methods, e.g., real-valued ILC. This contribution presents two options to handle the binary control domain in the context of an ILC. Both approaches are tested in a simulation study first to analyze the behavior. Then they are applied to a real test rig featuring a linear stator cascade.


Author(s):  
Wanqiang Xi ◽  
Yaoyao Wang ◽  
Bai Chen ◽  
Hongtao Wu

For the repetitive motion control, inaccurate model, and other issues of industrial robots, this article presents a novel control method that the proportion differentiation-type iterative learning parameters are self-tuning based on artificial bee colony algorithm. Considering the influence of the numerical value of iterative learning parameters on the control system, especially in the early iteration, the control effect is not satisfactory. Thus, the artificial bee colony algorithm is introduced in this article. Using bee colony as search unit, the parameters in iterative learning are optimized through the exchange of information and the survival of fittest between them. And then the optimized results are returned to iterative learning control algorithm. Finally, the digital simulation of a two-degrees-of-freedom manipulator and the experimental verification of a cable-driven robot with its first two joints are carried out. The results show that the iterative learning control based on the artificial bee colony algorithm has faster convergence and better control effect than the iterative learning control with fixed parameters.


Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 24 ◽  
Author(s):  
Jian Dong ◽  
Bin He

Due to the under-actuated and strong coupling characteristics of quadrotor aircraft, traditional trajectory tracking methods have low control precision, and poor anti-interference ability. A novel fuzzy proportional-interactive-derivative (PID)-type iterative learning control (ILC) was designed for a quadrotor unmanned aerial vehicle (UAV). The control method combined PID-ILC control and fuzzy control, so it inherited the robustness to disturbances and system model uncertainties of the ILC control. A new control law based on the PID-ILC algorithm was introduced to solve the problem of chattering caused by an external disturbance in the ILC control alone. Fuzzy control was used to set the PID parameters of three learning gain matrices to restrain the influence of uncertain factors on the system and improve the control precision. The system stability with the new design was verified using Lyapunov stability theory. The Gazebo simulation showed that the proposed design method creates effective ILC controllers for quadrotor aircraft.


2004 ◽  
Vol 126 (4) ◽  
pp. 916-920 ◽  
Author(s):  
Huadong Chen ◽  
Ping Jiang

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.


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