Command Shaping for MIMO Nonlinear Systems Using Iterative Learning Control With Application to an RTP System

2000 ◽  
Author(s):  
Dick de Roover ◽  
Abbas Emami-Naeini ◽  
Jon L. Ebert ◽  
Robert L. Kosut
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Mimo Systems ◽  
Nonlinear Behavior ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Current Output ◽  
Command Shaping ◽  
Output Trajectory

Abstract Input command shaping for temperature control of fast-ramp RTP systems is investigated from an open-loop-input point of view, i.e., for a given desired temperature recipe a set of lamp command profiles is determined such that the resulting set of measured temperatures approaches the desired recipe as closely as possible. Because of the inherent nonlinear behavior of RTP systems, a command shaping method has been developed that iteratively modifies the optimal linear commands to compensate for the nonlinearities. This method, which has been derived from Iterative Learning Control (ILC), shapes the input commands iteratively so as to minimize the two-norm between a desired output trajectory and the simulated current output trajectory. The technique is applicable to MIMO systems and can handle constraints on the input commands. Application of this method to a fast-ramp oxidation (RTO) and fast-ramp spike anneal (RTA) process for a model of a generic RTP system demonstrates its usefulness for nonlinear systems.

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

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