Modeling and Identification of Real-Time Processes Based on Nonzero Setpoint Autotuning Test

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Prasenjit Ghorai ◽  
Somanath Majhi ◽  
Saurabh Pandey
Keyword(s):  
Real Time ◽  
Dead Time ◽  
System Modeling ◽  
Model Parameters ◽  
Relay Feedback ◽  
Unknown Parameters ◽  
Real Time System ◽  
Nyquist Plots ◽  
Function Models ◽  
Analytical Expressions

The paper presents a real-time system modeling and identification scheme for estimation of plant model parameters using a single asymmetrical relay test. A modified set of analytical expressions for unknown plant models under nonzero setpoint and non-negative relay settings is derived. Thereafter, the unknown parameters of three different stable plant models are identified as first-order plus dead time, overdamped, and critically damped second-order plus dead time. The well-known examples from literature are included to show the accuracy of the proposed method through computer simulations. Yokogawa distributed control system centum CS3000 is considered as a design platform for an experimental setup for the realization of asymmetrical relay feedback test. Finally, the transfer function models derived from successive identification of plant dynamics are compared with the literature through Nyquist plots.

scholarly journals Real-Time System Modeling and Verification Through Labeled Transition System Analyzer

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 26314-26323
Author(s):  
Yilong Yang ◽  
Quan Zu ◽  
Wei Ke ◽  
Miaomiao Zhang ◽  
Xiaoshan Li
Keyword(s):  
Real Time ◽  
System Modeling ◽  
Transition System ◽  
Time System ◽  
Real Time System ◽  
Labeled Transition System

scholarly journals A Time Refinement Framework Based on iUML-B State Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Han Peng ◽  
Xiaoli Zhang ◽  
Guozhen Cao ◽  
Zhouzhou Liu ◽  
Yuejuan Jing ◽  
...  
Keyword(s):  
Real Time ◽  
Timed Automata ◽  
System Modeling ◽  
Research Work ◽  
Time Constraints ◽  
Formal Modeling ◽  
Time System ◽  
Time Model ◽  
Real Time System ◽  
Time Systems

Event-B is a formal modeling language that is very suitable for software engineering, but it lacks the ability of modeling time. Researchers have proposed some methods for modeling time constraints in Event-B. The limitations with existing methods are that, first of all, the existing research work lacks a systematic time refinement framework based on Event-B; secondly, the existing methods only model time in the Event-B framework and cannot be smoothly converted to automata-based models such as timed automata that facilitate the verification of time properties. These limitations make it more difficult to model and verify real-time systems with Event-B because it is very time-consuming to prove time properties in the Event-B framework. In this paper, we firstly proposed a systematic time refinement framework to express and refine time constraints in Event-B. Secondly, we also proposed various vertical refinement patterns and horizontal extension patterns to guide modelers to refine the Event-B real-time model step by step. Finally, we use a real-time system case to demonstrate the practicality of our method. The experimental results show that the proposed method can make the real-time system modeling in Event-B more convenient and the models are easier to convert to the timed automata model, thereby facilitating the verification of various time properties.

Adaptive Extended Kalman Filter for Nonlinear System with Noise Compensating Technology

2014 ◽  
Vol 631-632 ◽  
pp. 121-124
Author(s):  
Zhou Sheng Ma ◽  
Wen Bing Fan
Keyword(s):  
System Modeling ◽  
Computation Method ◽  
Model Parameters ◽  
Model Errors ◽  
Unknown Parameters ◽  
Noise Compensation ◽  
Adaptive Estimators ◽  
Compensation Technique ◽  
Adaptive Extended Kalman Filter ◽  
Noise Statistics

This paper is concerned with the development of new adaptive nonlinear Kalman estimators which incorporate nonlinear model errors and noise statistical characteristic errors. With the adoption of fictitious noise compensation technique and actual non-divergent computation method, the new filters are aimed at compensating the nonlinear dynamics as well as the system modeling errors by adaptively estimating the noise statistics and unknown parameters. The performance of the proposed adaptive estimators is demonstrated using six-state with varying model parameters as a simulation example.

Sequential Optimal Design of Neurophysiology Experiments

2009 ◽  
Vol 21 (3) ◽  
pp. 619-687 ◽  
Author(s):  
Jeremy Lewi ◽  
Robert Butera ◽  
Liam Paninski
Keyword(s):  
Mutual Information ◽  
Real Time ◽  
Design Procedure ◽  
Adaptive Methods ◽  
Low Rank ◽  
High Dimensional ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Desktop Computer ◽  
History Effects

Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are high-dimensional systems, optimizing neurophysiology experiments requires computing high-dimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model (GLM) that we want to fit to the neuron's activity. We rely on important log concavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only low-rank matrix manipulations and a two-dimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making real-time adaptive experimental design feasible even for high-dimensional stimulus and parameter spaces. For example, we require roughly 10 milliseconds on a desktop computer to optimize a 100-dimensional stimulus. Despite using some approximations to make the algorithm efficient, our algorithm asymptotically decreases the uncertainty about the model parameters at a rate equal to the maximum rate predicted by an asymptotic analysis. Simulation results show that picking stimuli by maximizing the mutual information can speed up convergence to the optimal values of the parameters by an order of magnitude compared to using random (nonadaptive) stimuli. Finally, applying our design procedure to real neurophysiology experiments requires addressing the nonstationarities that we would expect to see in neural responses; our algorithm can efficiently handle both fast adaptation due to spike history effects and slow, nonsystematic drifts in a neuron's activity.

Seamless Implementation of a Telephone Switching System Based on Formal Specifications in RTPA

2012 ◽  
Vol 4 (2) ◽  
pp. 81-104 ◽  
Author(s):  
Yingxu Wang ◽  
Yousheng Tian
Keyword(s):  
Real Time ◽  
Interface Design ◽  
Process Algebra ◽  
System Modeling ◽  
Conceptual Modeling ◽  
Formal Specifications ◽  
Formal Models ◽  
Switching System ◽  
Time System ◽  
Real Time System

The Telephone Switching Systems (TSS) is a typical real-time system that is highly complicated in design and implementation. In order to deal with the extreme complexity in real-world settings, a suitable and efficient mathematical means is required beyond any programming language. To this purpose, an efficient and precise denotational mathematics known as the Real-Time Process Algebra (RTPA) and the RTPA methodology for system modeling are introduced. Empirical experimental results are reported in this paper on the implementation of TSS based on formal models of the system in RTPA. Three phases of experiments are designed on TSS conceptual modeling, system interface design, and programming implementation and testing. All groups in the experiments with 7 to 8 members have been able to efficiently understood, design, and implement the TSS system in a simplified version in four weeks, which has been estimated as a 10+ person-year project in the industry. The efficiency and expressiveness of RTPA are empirically demonstrated base on the case studies in the experiments.

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