Stability analysis tool for tuning unconstrained decentralized model predicitive controllers

Some processes are naturally suitable to be controlled in a decentralized framework: centralized control solutions are often infeasible in dealing with large scale plants and they are technologically prohibitive when the processes are too fast for the available computational resources. In these cases, the resulting control problem is usually split in many smaller subproblems and the global requirements are guaranteed by means of a proper coordination. The unconstrained decentralized case is here considered and a coordination strategy is proposed for improving the global control performances. This paper present a tool for setting up and tuning a nominally stable decentralized Model Predictive Controller. Numerical examples are proposed for testing and validating the developed technique

A framework for modelling, simulation and control of integrated urban wastewater system

This paper is concerned with the integrated modelling, and control of urban wastewater systems (UWS) comprising the wastewater treatment plants (WTP), receiving waters (river) and the sewer networks. A unified framework is developed and simple models are used and implemented in Matlab/Simulink to produce a toolbox. Novel linear and nonlinear control structures are then proposed to design integrated control systems to improve the river water quality. A case study is simulated and simulation results are presented to demonstrate the possible improvement that can be achieved using a holistic integrated control system approac

Wiener modelling and model predictive control for wastewater applications

The research presented in this paper aims to demonstrate the application of predictive control to an integrated wastewater system with the use of the wiener modeling approach. This allows the controlled process, dissolved oxygen, to be considered to be composed of two parts: the linear dynamics, and a static nonlinearity, thus allowing control other than common approaches such as gain-scheduling, or switching, for series of linear controllers. The paper discusses various approaches to the modelling required for control purposes, and the use of wiener modelling for the specific application of integrated waste water control. This paper demonstrates this application and compares with that of another nonlinear approach, fuzzy gain-scheduled control

Nonlinear model-based condition monitoring of advanced gas-cooled nuclear reactor cores

The graphite core is one critical component in gascooled nuclear reactors and it ages and degrades over time. As a result, the graphite core can dictate the life-time of a reactor in a nuclear power station. To support the safety cases and ensure the continued safe operation of an advanced gas-cooled reactor (AGR) nuclear plant, it is important to closely monitor the condition of its reactor graphite core to maintain the integrity throughout the life of the reactor. Toward this end, the fuel grab load trace (FGLT) measurements are currently used as main information sources to infer the core condition. Due to the fact that the FGLTs are masked by many effects in the refuelling process, the first principles models for nuclear refuelling process are promising to separate the information of interests to core condition from the masked FGLT measurements. To reliably and accurately obtain the unknown parameters existing in the developed first principles model for model-based condition monitoring of AGR nuclear graphite cores, this paper presents a nonlinear system identification approach. In this approach, a nonlinear first principles model is first developed to describe the refuelling process. A friction model is then investigated to mathematically deal with the frictional effects. The aerodynamic force is also modelled separately. Finally, the Trust- Region Reflective Newton method is used to find the optimal parameters in the nonlinear refuelling model. The real-world data from an AGR nuclear power plant is employed to demonstrate the effectiveness of the proposed nonlinear system identification approach for nonlinear model-based condition monitoring of graphite core

Effect of Freezing on Stress–Strain Characteristics of Granular and Cohesive Soils

This is the author accepted manuscript. The final version is available from ASCE via the DOI in this recordTo investigate the stress-strain behavior of frozen soils, a program of triaxial compression tests was designed and carried out on samples of unfrozen and frozen cohesive (CL) and granular (SP) soils and pure ice. The experiments involved study of the influence of freezing, temperature reduction and loading rate on the stress-strain characteristics of the frozen ground. The aim of this study is to assess the possibility of using the Artificial Ground Freezing (AGF) technique in the excavation and tunneling in Line 2 of the Tabriz Subway project. The results show that freezing of the CL soil has no significant effect on the type of soil behavior (strain-hardening), while, freezing of the SP soil changes its strain-hardening behavior to strain-softening. The effect of freezing on the increase in shear strength of the saturated SP soil is much greater than that of the saturated CL soil; however, the rate of increase in the shear strength due to freezing and temperature reduction is much larger for the CL soil. Freezing and reduction in temperature cause an increase in the elastic modulus of all the materials tested in the present study. Also, the shear strength and elastic modulus 26 of these materials increase with loading rate.Ministry of Science of Ira

Roll reduction and course keeping for the ship moving in waves with factorized NGMV control

A factorized Nonlinear Generalized Minimum Variance (NGMV) control law is developed for a combined roll and yaw motion compensation using rudders and fins. The nonlinear model used for control design includes the non-minimum phase interaction from rudder to roll motion, and the dynamics from fins to yaw motion. This controller is developed using the polynomial approach to ensure that the non-minimum phase system remains stable in closed-loop. The effectiveness of the approach is demonstrated on a simulated nonlinear ship model

A new approach to modeling the behavior of frozen soils

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordIn this paper a new approach is presented for modeling the behavior of frozen soils. A data-mining technique, Evolutionary Polynomial Regression (EPR), is used for modeling the thermo-mechanical behavior of frozen soils including the effects of confining pressure, strain rate and temperature. EPR enables to create explicit and well-structured equations representing the mechanical and thermal behavior of frozen soil using experimental data. A comprehensive set of triaxial tests were carried out on samples of a frozen soil and the data were used for training and verification of the EPR model. The developed EPR model was also used to simulate the entire stress-strain curve of triaxial tests, the data for which were not used during the training of the EPR model. The results of the EPR model predictions were compared with the actual data and it was shown that the proposed methodology can extract and reproduce the behavior of the frozen soil with a very high accuracy. It was also shown that the EPR model is able to accurately generalize the predictions to unseen cases. A sensitivity analysis revealed that the model developed from raw experimental data is able to extract and effectively represent the underlying mechanics of the behavior of frozen soils. The proposed methodology presents a unified approach to modeling of materials that can also help the user gain a deeper insight into the behavior of the materials. The main advantages of the proposed technique in modeling the complex behavior of frozen soil have been highlighted