Design of generalized minimum variance controllers for nonlinear multivariable systems

The design and implementation of Generalized Minimum Variance control laws for nonlinear multivariable systems that can include severe nonlinearities is considered. The quadratic cost index minimised involves dynamically weighted error and nonlinear control signal costing terms. The aim here is to show the controller obtained is simple to design and implement. The features of the control law are explored. The controller obtained includes an internal model of the process and in one form is a nonlinear version of the Smith Predictor

Nonlinear generalized minimum variance control under actuator saturation

A new Generalized Minimum Variance control law has been derived recently for the controlof nonlinear multivariable systems. In this paper we restrict our interest tosingle-input, single-output plants with input nonlinearities in the form of hard actuatorlimits. Since in real systems saturation always exists in some form, e.g. as a result ofvalve opening limits or finite power supply, this is a natural case to consider. One ofthe well-known problems associated with input saturation is the integral windupphenomenon, which occurs whenever the controller includes integral action. In this paper,we show that the classical form of the 'anti-windup' mechanism can be obtained withinthe Nonlinear GMV controller framework by a suitable selection of the design parameters.The advantage of the approach is that the anti-windup mechanism is obtained naturallyfrom the optimization problem. There is also the possibility that the technique can beextended for other specialized nonlinear compensation problems

State-dependent Kalman filters for robust engine control

Vehicle emissions variations impose significant challenges to the automotive industry. In these simulation studies, nonlinear estimation techniques based on state-dependent and extended Kalman filtering are developed for spark ignition engines to enhance robustness of the feedforward fuel controllers to changes in nominal system parameters and measurement errors. A model-based approach is used to derive the optimal filters. Numerical simulations indicate the superiority of estimation-based approaches to enhance robustness of in-cylinder air estimation which directly contributes to the precision of engine exhaust air-fuel ratio and, consequently the consistency of the tailpipe emissions. The results obtained are for an aggressive driving profile and are presented and discusse

Real-time predictive control for SI engines using linear parameter-varying models

As a response to the ever more stringent emission standards, automotive engines have become more complex with more actuators. The traditional approach of using many single-input single output controllers has become more difficult to design, due to complex system interactions and constraints. Model predictive control offers an attractive solution to this problem because of its ability to handle multi-input multi-output systems with constraints on inputs and outputs. The application of model based predictive control to automotive engines is explored below and a multivariable engine torque and air-fuel ratio controller is described using a quasi-LPV model predictive control methodology. Compared with the traditional approach of using SISO controllers to control air fuel ratio and torque separately, an advantage is that the interactions between the air and fuel paths are handled explicitly. Furthermore, the quasi-LPV model-based approach is capable of capturing the model nonlinearities within a tractable linear structure, and it has the potential of handling hard actuator constraints. The control design approach was applied to a 2010 Chevy Equinox with a 2.4L gasoline engine and simulation results are presented. Since computational complexity has been the main limiting factor for fast real time applications of MPC, we present various simplifications to reduce computational requirements. A benchmark comparison of estimated computational speed is included

Integrated Ecosystem Assessments: Developing the Scientific Basis for Ecosystem-Based Management of the Ocean

Integrated ecosystem assessments challenge the broader scientific community to move beyond the important task of tallying insults to marine ecosystems to developing quantitative tools that can support the decisions national and regional resource managers must make

Low-grade inflammation, diet composition and health: current research evidence and its translation

The importance of chronic low-grade inflammation in the pathology of numerous age-related chronic conditions is now clear. An unresolved inflammatory response is likely to be involved from the early stages of disease development. The present position paper is the most recent in a series produced by the International Life Sciences Institute's European Branch (ILSI Europe). It is co-authored by the speakers from a 2013 workshop led by the Obesity and Diabetes Task Force entitled ‘Low-grade inflammation, a high-grade challenge: biomarkers and modulation by dietary strategies’. The latest research in the areas of acute and chronic inflammation and cardiometabolic, gut and cognitive health is presented along with the cellular and molecular mechanisms underlying inflammation–health/disease associations. The evidence relating diet composition and early-life nutrition to inflammatory status is reviewed. Human epidemiological and intervention data are thus far heavily reliant on the measurement of inflammatory markers in the circulation, and in particular cytokines in the fasting state, which are recognised as an insensitive and highly variable index of tissue inflammation. Potential novel kinetic and integrated approaches to capture inflammatory status in humans are discussed. Such approaches are likely to provide a more discriminating means of quantifying inflammation–health/disease associations, and the ability of diet to positively modulate inflammation and provide the much needed evidence to develop research portfolios that will inform new product development and associated health claims

Robust nonlinear generalised minimum variance control and fault monitoring

The first part of this paper extends the Nonlinear Generalised Minimum Variance (NGMV) controller to improve the robustness of its control or set-point tracking performance. This is achieved by replacing the Kalman filter included in the original NGMV controller with an observer to minimise the effect of uncertainty, which includes unknown disturbance, modelling error, and faults. The observer design also allows the NGMV controller to be utilised in fault monitoring. More specifically, the second part of this paper obtains the observer gain by solving a multi-objective optimisation problem through the application of a genetic algorithm so that the residual signal becomes sensitive to faults and insensitive to any other uncertainty. The control and fault monitoring performance of the extended NGMV controllers is tested by application to a nonlinear tank model

Robust industrial control : optimal design approach for polynomial systems/ Grimble

xvi, 597 hal. : ill. ; app. ; 24 c

Robust industrial control : optimal design approach for polynomial systems/ Grimble

xvi, 597 hal. : ill. ; app. ; 24 c