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This paper investigates the application of multivariable PID (MPID) controllers to wastewater treatment plants (WWTPs) with pre-denitrifying process. WWTPs have usually a number of interacting control loops. The controller's parameters are often left unchanged since the plant commissioning and hence poor performance is often observed due to inevitable changes in the plant and its influent characteristics. The model-based MPID tuning methods are complicated, cumbersome and expensive, and require a high level of control expertise to design, and implement. In this paper, a simple and novel tuning method is proposed along with three existing methods, which require only step or frequency response tests. The performance of these methods is compared and the parameters are tuned using a proposed optimisation technique to meet standard quality effluent at a minimum cost. This is achieved using a nonlinear ASM1 model to determine the optimal values of the controller parameters. The results Show that by effective and proper tuning of the MPID controllers, the closed-loop performance of WWTP improves and it could potentially lead to energy saving and improvement in the removal of nitrogen.

Despite the closed-loop performance of centralized multi-variable controllers, the vast majority of control applications are still based on decentralized controllers. Because of their single-loop structure, decentralized controllers cannot suppress interactions of the system, which are only taken into account in the controller tuning phase. Therefore, it would be useful in many cases to tackle the undesirable effects of the interactions on the closed-loop system. A novel model predictive control scheme based on Nash optimality is presented to achieve this goal. In this algorithm the centralized optimization is decomposed into that of several small coupled optimization problems. The relevant computational convergence, the closed-loop performance, and the effect of communication failures on the closed-loop behaviour are analysed. The control problem is illustrated to verify the effectiveness and practicality of the proposed control algorithm.

This paper describes the development of a control and monitoring oriented model of a plastic film manufacturing process. The model is mainly derived from first-principles and has been implemented in the Matlab/Simulink dynamic simulation environment. The development of the model forms the first phase of a project that aims to develop a nonlinear sub-space based monitoring, fault detection and trouble shooting system for the film manufacturing process.

The vast majority of control applications are based on non-interacting decentralized control designs. Because of their single-loop structure, these controllers cannot suppress interactions of the system. It would be useful to tackle the undesirable effects of the interactions at the design stage. A novel model predictive control scheme based on Nash optimality is presented to achieve this goal. In this algorithm, the control problem is decomposed into that of several small-coupled mixed integer optimisation problems. The relevant computational convergence, closed-loop performance and the effect of communication failures on the closed-loop behaviour are analysed. Simulation results are presented to illustrate the effectiveness and practicality of the proposed control algorithm.

This is a list of recently published journal articles relevant to the fields of robust and nonlinear control.

In general, wastewater treatment plant (WWTP) consists of several stages before it is released to a receiving water body. There are, preliminary and primary treatment (mechanical treatment), a secondary treatment (biological treatment) and a tertiary treatment (chemical treatment). In this chapter, since the work involve of identification and control design of activated sludge process to improve the performance of the system, and most of the control priorities are centred on the biological treatment process, only the secondary treatment will be highlighted.

This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms.

This paper reports on the development of a first principle model of a plastic film manufacturing process, which is implemented in the Matlab/Simulink dynamic simulation environment. The real-life cross directional controller that is incorporated into the model is also described with a few performance results. Development of the model and implementation of the controller aim at developing models for performance monitoring and diagnosis of the film manufacturing process.

In a steel plant, reheating furnaces are used for heating the steel slabs to a temperature of approximately 1200degC before rolling. Reheating furnaces consumes a lot of energy in steel plants. For competitive advantage it is important to improve the heating quality of slab and reduce the energy consumption as much as possible. This paper explores the potential of nonlinear model predictive control techniques to improve the temperature control of the metal slabs in a hot mill reheat furnace, and particularly whether or not these control techniques can be exploited to reduce the energy consumption. An example of actual furnace operation is presented to show the effectiveness of the proposed scheme.

This paper investigates the application of multivariable PID controllers to a wastewater treatment process. Four multivariable PID control schemes are investigated. The methods considered are those suggested by Davison, Penttinen-Koivo, Maciejowski and a new method proposed in this paper. All of the methods are suitable for multi-input multi-output (MIMO) control loops that experience loop interaction. The methods, furthermore, requires only simplistic plant models. The performance of the methods is assessed using a nonlinear benchmark model, and the optimal tuning values are determined using an optimisation method. The simulation results show the significance of the study and lead to the conclusion that the proposed method yields somewhat better results than other methods with respect to decoupling capabilities and closed-loop performance.

This paper explores the use of a multivariate statistics approach to monitor and diagnose the performance of a steam heated drying process. The results presented in this paper are a first stage investigation into the capabilities and limitations of these methods when applied to this particular process. The results show that, despite the non-linear behaviour of the drier process, the model predictions correspond well with the process measurements. The results also indicate ability to detect process failures, however, accurate diagnosis of the failures remains a subject for future studies

A toy helicopter makes for an impressive classroom control demonstration. However the challenge to obtain a robust, stable, multivariable controller is nontrivial given the nonlinear nature of the helicopter, the stochastic nature of the disturbances, and the performance limitations of PCs. This paper applies a model predictive controller using the Mathworks xPC target to obtain significantly improved results over that achievable using classical control algorithms.

Concerns the development of a dynamic model of a 5-ply board machine producing packaging board. Three models were developed in decreasing order of complexity. First a detailed dynamic model of the short circulations was built using Simons IDEAS, which is a special purpose dynamic simulation package intended for the pulp and paper industry. This full model contained a comprehensive differential and algebraic equation set so consequently quickly became too unwieldy for the intended uses of troubleshooting, aiding retrofits and model-based control studies. A slightly simplified version of this model was re-engineered in Simulink, dropping the algebraic constraint equations and implementing the transport delays in a more elegant manner. Finally transfer function models, suitable for online control and optimisation tasks were identified from production data with a structure given by semi-rigorous reasoning. All three models were adjusted and subsequently validated against almost one year of plant operating data. This paper highlights the difficulties in validating a large industrial system with widely varying time constants, dubious transducers and the ubiquitous noise. We also discuss the necessary, but delicate, task of performing model reduction; contrast different simulation tools used at the various levels of model hierarchy and finally the paper demonstrates the use of simplified models to improve the performance of the adaptive IMC controller for the basis weight in the finished board.