Identification of linear systems with input and output noise: the Koopmans-Levin method

1985 ◽  
Vol 132 (1) ◽  
pp. 30 ◽  
Author(s):  
K.V. Fernando ◽  
H. Nicholson
Keyword(s):  
Linear Systems ◽  
Input And Output ◽  
Output Noise

Bayesian robot system identification with input and output noise

2011 ◽  
Vol 24 (1) ◽  
pp. 99-108 ◽  
Author(s):  
Jo-Anne Ting ◽  
Aaron D’Souza ◽  
Stefan Schaal
Keyword(s):  
System Identification ◽  
Robot System ◽  
Input And Output ◽  
Output Noise

scholarly journals A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems

2008 ◽  
Vol 223 (3) ◽  
pp. 309-321 ◽  
Author(s):  
O F Lutfy ◽  
Mohd S B Noor ◽  
M H Marhaban ◽  
K A Abbas
Keyword(s):  
Linear Systems ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Proportional Integral Derivative ◽  
Scaling Factors ◽  
Inference System ◽  
Input And Output ◽  
Neuro Fuzzy ◽  
Non Linear ◽  
Non Linear Systems

This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, with real-coding operators, is used to adjust all of the ANFIS parameters, which include the input and output scaling factors, the centres and widths of the input membership functions (MFs), and the consequent parameters. To show the effectiveness of this controller and its learning method, several non-linear plants, including the CSTR (continuous stirred tank reactor), have been selected to be controlled by this controller through simulation. Moreover, this controller's robustness to output disturbances has also been tested and the results clearly indicated the remarkable performance of this controller and its learning algorithm. In addition, the result of comparing the performance of this controller with a genetically tuned classical PID controller has shown the superiority of the PID-like ANFIS controller.

scholarly journals MODELING PREDICTIVE TRACKING CONTROL FOR MAX-PLUS LINEAR SYSTEMS IN MANUFACTURING

2020 ◽  
Vol 3 (2) ◽  
pp. 133-147
Author(s):  
Lathifatul Aulia ◽  
Widowati Widowati ◽  
R. Heru Tjahjana ◽  
Sutrisno Sutrisno
Keyword(s):  
Linear Systems ◽  
Production System ◽  
Tracking Control ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Process Flow ◽  
Sequence Of Events ◽  
Input And Output ◽  
Event System ◽  
Event Systems

Discrete event systems, also known as DES, are class of system that can be applied to systems having an event that occurred instantaneously and may change the state. It can also be said that a discrete event system occurs under certain conditions for a certain period because of the network that describes the process flow or sequence of events. Discrete event systems belong to class of nonlinear systems in classical algebra. Based on this situation, it is necessary to do some treatments, one of which is linearization process. In the other hand, a Max-Plus Linear system is known as a system that produces linear models. This system is a development of a discrete event system that contains synchronization when it is modeled in Max-Plus Algebra. This paper discusses the production system model in manufacturing industries where the model pays the attention into the process flow or sequence of events at each time step. In particular, Model Predictive Control (MPC) is a popular control design method used in many fields including manufacturing systems. MPC for Max-Plus-Linear Systems is used here as the approach that can be used to model the optimal input and output sequences of discrete event systems. The main advantage of MPC is its ability to provide certain constraints on the input and output control signals. While deciding the optimal control value, a cost criterion is minimized by determining the optimal time in the production system that modeled as a Max-Plus Linear (MPL) system. A numerical experiment is performed in the end of this paper for tracking control purposes of a production system. The results were good that is the controlled system showed a good performance.

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