Reverse engineered MPC for tracking with systems that become uncertain

A constrained model predictive control technique for tracking is proposed for systems whose models become uncertain (for example after a sensor failure). A linear time invariant robust controller with integral action is used as a baseline and ``reverse engineered'' into the form of a reduced order observer, steady state target calculator and control gain, based on a nominal model, augmented with integrating disturbance states. Constraints are enforced by optimising over perturbations to the nominal control action. Clean transition between a nominal, high performance mode of operation when parameters are known, to a safe and recursively feasible robust mode when parameters are unknown can be facilitated by using the same steady state target in both cases.The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement number 314 544, project ``RECONFIGURE''.European Control Conference 2014, Strasbour

Assessing the influence of behavioural parameterisation on the dispersal of larvae in marine systems

Predicting dispersal and quantifying ecological connectivity are increasingly referenced as fundamental to understanding how biodiversity is structured across space and time. Dispersal models can provide insight, but their predictions are influenced by our capacity to simulate the biology and physics known to influence dispersal. In a marine context, vertical swimming behaviour is considered important in influencing the spatial organisation of species across seascapes, but the mechanisms underpinning these movements remain unresolved, making it unclear how best to incorporate behaviour within models. Here, using a 3-D hydrodynamic model coupled with a Lagrangian particle tracker, we show how different modelled larval behaviours, alongside spatial and temporal hydrodynamic changes, influence larval dispersal predictions. Additionally, we compare the application of a novel approach of reverse-engineered larval swimming behaviour against two commonly modelled behaviours: passive dispersal and tidal vertical migration (TVM). We used statistical models (LME and GAM) to test the effects of change in tidal state conditions, season, and planktonic larval duration in conjunction with behavioural parameters on dispersal. For shorter PLDs (i.e., 1 day), we find that passive models match ‘behaving’ model outputs, but for longer PLDs, excluding behaviour leads to overestimates of dispersal; an effect that increases with time. Our results highlight the sensitivity of biophysical models to behavioural inputs, specifically how vertical migration behaviour can significantly reduce dispersal distance - especially for species with longer planktonic durations. This study demonstrates the disproportionate effects that even a single behaviour - vertical swimming - can have on model predictions, our understanding of ecosystem functioning, and ultimately, the ecological coherence of marine systems

Advancing multiple model-based control of complex biological systems: Applications in T cell biology

Activated CD4+ T cells are important regulators of the adaptive immune response against invading pathogens and cancerous host cells. The process of activation is mediated by the T cell receptor and a vast network of intracellular signal transduction pathways, which recognize and interpret antigenic signals to determine the cell\u27s response. The critical role of these early signaling events in normal cell function and the pathogenesis of disease ultimately make them attractive therapeutic targets for numerous autoimmune diseases and cancers. Scientists increasingly rely on predictive mathematical models and control-theoretic tools to design effective strategies to manipulate cellular processes for the advancement of knowledge or therapeutic gain. However, the application of modern control theory to intracellular signal transduction is complicated by a unique set of intrinsic properties and technical limitations. These include complexities in the signaling network such as crosstalk, feedback and nonlinearity, and a dearth of rapid quantitative measurement techniques and specific and orthogonal modulators, the major consequences of which are uncertainty in the model representation and the prevention of real-time measurement feedback. Integrating such uncertainties and limitations into a control-theoretic approach under practical constraints represents an open challenge in controller design. The work presented in this dissertation addresses these challenges through the development of a computational methodology to aid in the design of experimental strategies to predictably manipulate intracellular signaling during the process of CD4+ T cell activation. This work achieves two main objectives: (1) the development of a generalized control-theoretic tool to effectively control uncertain nonlinear systems in the absence of real-time measurement feedback, and (2) the development and calibration of a predictive mathematical model (or collection of models) of CD4+ T cell activation to help derive experimental inputs to robustly force the system dynamics along prescribed trajectories. The crux of this strategy is the use of multiple data-supported models to inform the controller design. These models may represent alternative hypotheses for signaling mechanisms and give rise to distinct network topologies or kinetic rate scenarios and yet remain consistent with available data. Here, a novel adaptive weighting algorithm predicts variations in the models\u27 predictive accuracy over the admissible input space to produce a more reliable compromise solution from multiple competing objectives, a result corroborated by several experimental studies. This dissertation provides a practical means to effectively utilize the collective predictive capacity of multiple prediction models to predictably and robustly direct CD4 + T cells to exhibit regulatory, helper and anergic T cell-like signaling profiles through pharmacological manipulations in the absence of measurement feedback. The framework and procedures developed herein are expected to widely applicable to a more general class of continuous dynamical systems for which real-time feedback is not readily available. Furthermore, the ability to predictably and precisely control biological systems could greatly advance how we study and interrogate such systems and aid in the development of novel therapeutic designs for the treatment of disease

Energy Efficient Control of Hydrostatic Drive Transmissions: A Nonlinear Model-Based Approach

The high standard of living in industrial countries is based on the utilization of machines. In particular, the tasks performed with hydraulic work machines (HWMs) are essential in numerous industrial fields. Agriculture, mining, and construction are just a few examples of the lines of business that would be inconceivable today without HWMs. However, rising oil prices and competing technologies are challenging the manufacturers of these machines to improve their fuel economy.Despite the fact that energy efficiency research of hydraulic systems has been active for more than a decade, there seems to be a significant gap between industry and academia. The manufacturers of HWMs have not adopted, for example, novel system layouts, prototype components, or algorithms that require powerful control units in their products.The fuel economy of HWMs can be increased by utilizing system information in control algorithms. This cost-effective improvement enables operation in challenging regions and closer to the operating boundaries of the system. Consequently, the information about the system has to be accurate. For example, reducing the rotational speed of the engine has proven effective in improving the energy efficiency, but it increases the risk of even stalling the engine, for instance in situations where the power generation cannot meet the high transient demand. If this is considered in the controller with low uncertainty, fuel economy can be improved without decreasing the functionality of the machine.This thesis studies the advantages of model-based control in the improvement of the fuel economy of HWMs. The focus is on hydrostatic drive transmissions, which is the main consumer of energy in certain applications, such as wheel loaders.We started by developing an instantaneous optimization algorithm based on a quasi-static system model. The control commands of this fuel optimal controller (FOC) were determined based on cost function, which includes terms for fuel economy, steady-state velocity error, and changes in the control commands.Although the use of quasi-static models is adequate for steady-state situations, the velocity tracking during transients and under load changes has proven to be inadequate. To address this issue, a high-performance velocity-tracking controller was devised. Full state feedback was assumed, and we resorted to a so-called D-implementation, which eliminates, for example, the need for the equilibrium values of pressure signals. The nonlinearities of the system were considered with the state-dependent parameters of the linear model.In the next step, a nonlinear model predictive controller combined fuel economy control and velocity tracking. To the best of the author’s knowledge, this is the first time that the model predictive control scheme has been utilized with such a detailed system model that also considers the hydraulic efficiencies and torque generation of the engine. This enables utilizing the controller as a benchmark of control algorithms for non-hybrid hydrostatic drive transmissions that do not require information about the future.The initial tests of all the controllers were conducted with a validated simulation model of a research platform machine, a five-ton municipal tractor. In addition, the FOC and velocity-tracking controller were implemented into the control system of the machine. The practical worth of the FOC was proven with a relatively unique field experiment set-up that included, for example, an online measurement system of fuel consumption and autonomous path following. The fuel economy improved up to 16.6% when compared with an industrial baseline controller. The devised velocity-tracking concept was also proven as a significant reduction of error was observed in comparison with classic literature solutions, namely state feedback and proportional-integral-derivative controllers