A wavelet neural network based non-linear model predictive controller for a multi-variable coupled tank system

Owa, Kayode; Sharma, Sanjay; Sutton, Robert I.

doi:10.1007/s11633-014-0825-2

Cited by 18 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, let us mention some investigations completed in [35], where a new NMPCnonlinear model-predictive controller, working in RT, to regulate a multi-degrees-offreedom CTS, is elaborated. The researchers applied a DAQ device NI-6009 coupled with LabVIEW to acquire streams of data (a multi-input control of levels in both tanks) from sensors in RT.…”

Section: Labview Virtual Instrumentsmentioning

confidence: 99%

“…A coupled or cascaded two-tank dynamical system, which is investigated in [17,[66][67][68][69] (state-space approach, PI, PID), [11,12,23,25,30,36] (transfer function approach), [35,70,71] (FL, PSO, NN), [72] (autoregressive model), [73] (multidimensional regularization), [74] (model-predictive control of a geometry-varying conical tank), and [75,76] (SMC); 3.…”

Section: Mathematical Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

Olejnik

Awrejcewicz

2022

Machines

View full text Add to dashboard Cite

In this paper, a unique overview of intelligent machines and mathematical methods designed and developed to measure and to control the water level in industrial or laboratory setups of coupled and cascaded configurations of tanks is made. A systematized and concise overview is made of the mechatronic systems used in the measurement, identification, and control of the water level enumerates, the software used in the associated scientific research, modern techniques and sensors, and mathematical models, as well as analysis and control strategies. The broad overview of applications of the last decade is finalized by a proposition of a control system that is based on a parameter estimation of a new experimental setup, an integral dynamic model of the system, a modern mechatronic machine such as the Watson-Marlow peristaltic pump, the Anderson Negele sensor of level, the NI cRIO-9074 controller, and LabVIEW virtual instrumentation. The results of real experimental tests, exploiting a hybrid proportional control, being improved by a numerically predicted water level, are obtained using a few tools, i.e., the static characteristics, the classical step response, and a new pyramid-shaped step function of a discontinuous path-following reference input, being introduced to evaluate the effectiveness and robustness of the regulation of the level height.

show abstract

Section: Labview Virtual Instrumentsmentioning

confidence: 99%

Section: Mathematical Modelingmentioning

confidence: 99%

Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

Olejnik

Awrejcewicz

2022

Machines

View full text Add to dashboard Cite

show abstract

“…Consequently, wavelet neural networks have been widely used in identification and predictive control of nonlinear systems. [38][39][40][41][42] In this study, we use a wavelet neural network with feedforward component and model predictive controller for online nonlinear system identification and control. The feedforward component drastically reduces the number of hidden layer nodes and, consequently, reduces the training time of the WNN while maintaining satisfactory identification performance.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang and Benveniste 37 proposed WNNs as an alternative to neural networks for system identification and demonstrated that WNNs typically have fewer nodes than other artificial neural networks. Consequently, wavelet neural networks have been widely used in identification and predictive control of nonlinear systems 38‐42 . In this study, we use a wavelet neural network with feedforward component and model predictive controller for online nonlinear system identification and control.…”

Section: Introductionmentioning

confidence: 99%

Networked model predictive control using a wavelet neural network

Khodabandehlou

Fadali

2020

Adv Control Appl

View full text Add to dashboard Cite

This study proposes using a wavelet neural network (WNN) with a feedforward component and a model predictive controller (MPC) for online nonlinear system identification and control over a communication network. The WNN performs the online identification of the nonlinear system. The MPC uses the model to predict the future outputs of the system over an extended prediction horizon and calculates the optimal future inputs by minimizing a controller cost function. A computationally efficient formulation for the controller is presented to reduce the computational complexity of the MPC for online implementation and Lyapunov theory is used to prove the stability of the MPC. The methodology is applied to the online identification and control of an unmanned autonomous vehicle. Simulation results show that the MPC with an extended prediction horizon can effectively control the system in the presence of fixed or random network delay.

show abstract

“…In addition, both tank groups are cross coupled. Many of the methods used for systems in a SISO structure cannot be used effectively to the MIMO systems due to the coupled structure of them . Therefore, the SMC method alone is not sufficient for this experimental study.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode PI control with backstepping approach for MIMO nonlinear cross‐coupled tank systems

Aksu

Çoban

2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary The control of tank systems in industrial applications is an important issue for monitoring the chemical processes involved in the manufacture and delivery of product. The most important reason to control the tank systems is to keep the liquid level in the tanks constant and at the desired level for a specified period of time. In this study, the sliding mode control (SMC) with a repetitive approach called backstepping that is insensitive to uncertainties in system parameters and input disturbances is proposed and experimentally applied to a quadruple, cross‐coupled, uncertain, nonlinear, and multiple‐input/multiple‐output tank system. A proportional‐integral (PI) control is used to reduce the steady‐state error caused by the parameter variations and external noises. The traditional way of introducing PI usually leads to sliding surfaces. In this paper, the PI action is introduced to the control signal. The proposed backstepping sliding mode PI control (BSMPIC) is applied to such a complex tank system for the first time. The experimental results are compared with those of the SMC, sliding mode PI control, and backstepping sliding mode control to see the effect of the proposed BSMPIC on the system. As a result of the comparison, it is observed that less overshoot and tracking error, better tracking performance, and faster rise time in the transient regime is obtained by the BSMPIC.

show abstract

A wavelet neural network based non-linear model predictive controller for a multi-variable coupled tank system

Cited by 18 publications

References 24 publications

Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

Networked model predictive control using a wavelet neural network

Sliding mode PI control with backstepping approach for MIMO nonlinear cross‐coupled tank systems

Contact Info

Product

Resources

About