scholarly journals A continuous state space model of multiple service, multiple resource communication networks

1995 ◽  
Vol 43 (2/3/4) ◽  
pp. 477-484 ◽  
Author(s):  
S. Jordan
Keyword(s):  
State Space ◽  
Communication Networks ◽  
State Space Model ◽  
Space Model ◽  
Continuous State Space ◽  
Continuous State ◽  
Multiple Resource

Coupled Processor: A Second-Order Continuous-State-Space Model

1990 ◽  
Vol 4 (2) ◽  
pp. 277-298 ◽  
Author(s):  
Haruhisa Takahashi
Keyword(s):  
State Space ◽  
State Space Model ◽  
Second Order ◽  
Two Dimensional ◽  
Riemann Boundary Value Problem ◽  
Riemann Boundary ◽  
Space Model ◽  
Continuous State Space ◽  
Continuous State ◽  
Order Continuous

A second-order continuous-state-space model for two-dimensional queueing systems is developed in this article. A particular problem is treated but the results can apply to some other two-dimensional queueing problems directly. The generating function for the model is obtained by applying a Riemann boundary value problem and leads to a computationally feasible solution.

scholarly journals Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Gergely Takács ◽  
Tomáš Polóni ◽  
Boris Rohal’-Ilkiv
Keyword(s):  
Control System ◽  
Vibration Control ◽  
State Space ◽  
Real Time ◽  
Cantilever Beam ◽  
State Space Model ◽  
Model Parameters ◽  
Extended Kalman Filtering ◽  
Space Model ◽  
Continuous State

This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

Mobile Robot Navigation Using ARTQL Algorithm with Novelty Driven Mechanism

2013 ◽  
Vol 380-384 ◽  
pp. 1117-1120
Author(s):  
Qi Xiang Hu ◽  
Xin Yu Qu
Keyword(s):  
Mobile Robot ◽  
State Space ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
State Space Model ◽  
Mobile Robot Navigation ◽  
Learning Activity ◽  
Q Learning ◽  
Space Model ◽  
Continuous State

Q-learning algorithm is usually used for traditional mobile robot navigation. The traditional Q-learning methods have the problem of dimension disaster, which may be produced by applying Q-learning to intelligent system of continuous state-space. Besides, the learning activity and efficiency are low. In order to solve these problems, a new method called ARTQL is proposed, which combined ART2 network with the traditional Q-learning algorithm. Then, a learning mechanism called novelty driven is proposed to lead the ARTQL algorithm to learn more actively and efficiently. Through the ARTQL with novelty driven mechanism algorithm, Q-learning Agent in view of the duty which needs to complete to learn an appropriate incremental clustering of state-space model, so Agent can carry out decision-making and a two-tiers online learning of state-space model cluster in unknown environment, without any priori knowledge, through interaction with the environment unceasingly alternately to improve the control strategies, increase the learning accuracy, activity and efficiency. Finally through the mobile robot navigation simulation experiments, we show that, using the proposed algorithm, mobile robot can improve its navigation performance continuously by interactive learning with the environment with high autonomous.

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

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