Adaptive control of three &amp;#x2014; tank &amp;#x2014; system: Comparison of two methods

Kubalcik, Marek; Bobál, Vladimír

doi:10.1109/med.2008.4601981

Cited by 6 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mean square errors (MSE) for both ANN and WNN are calculated using (11). The MSEs were calculated for the training, validation and the test data.…”

Section: Modelling Resultsmentioning

confidence: 99%

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

Owa

Sharma

Sutton

2014

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Abstract:In this paper, a novel real time non-linear model predictive controller (NMPC) for a multi-variable coupled tank system (CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output (MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings, interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation (RTO) of the manipulated variable at every sampling time. A novel wavelet neural network (WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.

show abstract

“…The mean square errors (MSE) for both ANN and WNN are calculated using (11). The MSEs were calculated for the training, validation and the test data.…”

Section: Modelling Resultsmentioning

confidence: 99%

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

Owa

Sharma

Sutton

2014

Int. J. Autom. Comput.

View full text Add to dashboard Cite

show abstract

“…To validate the proposed technique, an experimental nonlinear MIMO three-tank system is used to implement the proposed technique as it is a common benchmark that simulates many industrial processes, for example, wastewater treatment stations, petroleum refining plants and so forth. Other uses include testing and validating different control techniques and strategies [34].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications

et al. 2020

View full text Add to dashboard Cite

Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative (PID) controller is a common industrial controller known for its simplicity and robustness. Adapting the parameters of the PID considering system constraints is a challenging task. Both controllers, MPC and PID, merged in a hierarchical structure in this work to improve the industrial processes performance considering the operational constraints. The proposed control system is simulated and implemented on a three-tank benchmark system as a Multi-Input Multi-Output (MIMO) system. Since the main industrial goal of the proposed configuration is to be easily implemented using the available automation technology, PID controller is implemented in a PLC (Programable Logic Controller) controller as a lower controller level, while MPC controller and the adaptation mechanism are implemented within a SCADA (Supervisory Control And Data Acquisition) system as a higher controller level.

show abstract

“…6,15,23 However, PID controllers are not always able to provide good and acceptable results, especially when the system exhibits non-linearities. 16,23 Moreover, a PID controller will have more difficulties in maintaining small level fluid control because of its tendency to overshoot. 17 Most efficient process operations today require operating systems closer to the boundary of the admissible operating region, 18 and therefore, linear models are mostly insufficient to represent adequately the non-linear dynamics of the plant.…”

Section: Introductionmentioning

confidence: 99%

“…Over many years, classical control strategies such as the proportional–integral–derivative (PID) controller have been implemented, well established and stable. 6,15,23 However, PID controllers are not always able to provide good and acceptable results, especially when the system exhibits non-linearities. 16,23 Moreover, a PID controller will have more difficulties in maintaining small level fluid control because of its tendency to overshoot.…”

Section: Introductionmentioning

confidence: 99%

A novel real-time non-linear wavelet-based model predictive controller for a coupled tank system

Owa

Sharma

Sutton

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

This article presents the design, simulation and real-time implementation of a constrained non-linear model predictive controller for a coupled tank system. A novel wavelet-based function neural network model and a genetic algorithm online non-linear real-time optimisation approach were used in the non-linear model predictive controller strategy. A coupled tank system, which resembles operations in many chemical processes, is complex and has inherent non-linearity, and hence, controlling such system is a challenging task. Particularly important is low-level control where often instability and oscillatory responses are observed. This article designs a wavelet neural network with high predicting precision and time-frequency localisation characteristics for an online prediction model in the non-linear model predictive controller to show the effectiveness of this approach in controlling the liquid at low level. To speed up the training process, a fast global search stochastic non-linear conjugate wavelet gradient algorithm is initially used to train the wavelet neural network structure before the genetic algorithm optimisation technique is utilised to tune adaptively the wavelet neural network parameters. The non-linear model predictive controller algorithm is tested for both approaches: first, in a simulation using identified models, and second, in a real-time practical application to a single-input single-output system coupled tank system. The results show an excellent control performance with respect to mean square error and average control energy values obtained.

show abstract

Adaptive control of three — tank — system: Comparison of two methods

Cited by 6 publications

References 8 publications

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

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

Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications

A novel real-time non-linear wavelet-based model predictive controller for a coupled tank system

Contact Info

Product

Resources

About