Performance evaluation of multiplexed model predictive control for a large airliner in nominal and contingency scenarios

Model predictive control allows systematic handling of physical and operational constraints through the use of constrained optimisation. It has also been shown to successfully exploit plant redundancy to maintain a level of control in scenarios when faults are present. Unfortunately, the computational complexity of each individual iteration of the algorithm to solve the optimisation problem scales cubically with the number of plant inputs, so the computational demands are high for large MIMO plants. Multiplexed MPC only calculates changes in a subset of the plant inputs at each sampling instant, thus reducing the complexity of the optimisation. This paper demonstrates the application of multiplexed model predictive control to a large transport airliner in a nominal and a contingency scenario. The performance is compared to that obtained with a conventional synchronous model predictive controller, designed using an equivalent cost function. © 2012 AACC American Automatic Control Council).

Optimal constraint tightening policies for robust variable horizon model predictive control

This paper develops a technique for improving the region of attraction of a robust variable horizon model predictive controller. It considers a constrained discrete-time linear system acted upon by a bounded, but unknown time-varying state disturbance. Using constraint tightening for robustness, it is shown how the tightening policy, parameterised as direct feedback on the disturbance, can be optimised to increase the volume of an inner approximation to the controller's true region of attraction. Numerical examples demonstrate the benefits of the policy in increasing region of attraction volume and decreasing the maximum prediction horizon length. © 2012 IEEE.

Assessing boost-assist options for turbocharged engines using 1-D engine simulation and model predictive control

Delivering acceptable low end torque and good transient response is a significant challenge for all turbocharged engines. As downsized gasoline engines and Diesel engines make up a larger and larger proportion of the light-duty engines entering the market, the issue takes on greater significance. Several schemes have been proposed to improve torque response in highly boosted engines, including the use of electrical assist turbochargers and compressed air assist. In this paper we examine these methods with respect to their effectiveness in improving transient response and their relative performance along with some of the practical considerations for real world application. Results shown in this paper are from 1-D simulations using the Ricardo WAVE software package. The simulation model is based on a production light-duty Diesel engine modified to allow the introduction of compressed air at various points in the air-path as well as direct torque application to the turbocharger shaft (such as might be available from an electrical assist turbocharger). Whilst the 1-D simulation software provides a suitable environment for investigating the various boost assistance options, the overall air path performance also depends upon the control system. The introduction of boost assistance complicates the control in two significant ways: the system may run into constraints (such as compressor surge) that are not encountered in normal operation and the assistance introduces an additional control input. Production engine controllers are usually based on gain-scheduled PID control and extensive calibration. For this study, the non-linear nature of the engine together with the multiple configurations considered and the slower than real-time execution of 1-D models makes such an approach time consuming. Moreover, an ad-hoc approach would leave some doubt as to the fairness of comparisons between the different boost-assist options. Model Predictive Control has been shown to offer a convenient approach to controlling the 1-D simulations in a close to optimal manner for a typical Diesel VGT-EGR air path configuration. We show that the same technique can be applied to all the considered assistance methods with only modest calibration effort required. Copyright © 2012 SAE International.

Output disturbance rejection using parallel model predictive control

The solution time of the online optimization problems inherent to Model Predictive Control (MPC) can become a critical limitation when working in embedded systems. One proposed approach to reduce the solution time is to split the optimization problem into a number of reduced order problems, solve such reduced order problems in parallel and selecting the solution which minimises a global cost function. This approach is known as Parallel MPC. The potential capabilities of disturbance rejection are introduced using a simulation example. The algorithm is implemented in a linearised model of a Boeing 747-200 under nominal flight conditions and with an induced wind disturbance. Under significant output disturbances Parallel MPC provides a significant improvement in performance when compared to Multiplexed MPC (MMPC) and Linear Quadratic Synchronous MPC (SMPC). © 2013 IEEE.

Fault tolerant control using Gaussian processes and model predictive control

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control. © 2013 IEEE.

Material model of lung parenchyma based on living precision-cut lung slice testing.

We describe a novel constitutive model of lung parenchyma, which can be used for continuum mechanics based predictive simulations. To develop this model, we experimentally determined the nonlinear material behavior of rat lung parenchyma. This was achieved via uni-axial tension tests on living precision-cut rat lung slices. The resulting force-displacement curves were then used as inputs for an inverse analysis. The Levenberg-Marquardt algorithm was utilized to optimize the material parameters of combinations and recombinations of established strain-energy density functions (SEFs). Comparing the best-fits of the tested SEFs we found Wpar = 4.1 kPa(I1-3)2 + 20.7 kPa(I1 - 3)3 + 4.1 kPa(-2 ln J + J2 - 1) to be the optimal constitutive model. This SEF consists of three summands: the first can be interpreted as the contribution of the elastin fibers and the ground substance, the second as the contribution of the collagen fibers while the third controls the volumetric change. The presented approach will help to model the behavior of the pulmonary parenchyma and to quantify the strains and stresses during ventilation.

Modelling and predictive control: Enabling technologies for reconfiguration

An approach to reconfiguring control systems in the event of major failures is advocated. The approach relies on the convergence of several technologies which are currently emerging: Constrained predictive control, High-fidelity modelling of complex systems, Fault detection and identification, and Model approximation and simplification. Much work is needed, both theoretical and algorithmic, to make this approach practical, but we believe that there is enough evidence, especially from existing industrial practice, for the scheme to be considered realistic. After outlining the problem and proposed solution, the paper briefly reviews constrained predictive control and object-oriented modelling, which are the essential ingredients for practical implementation. The prospects for automatic model simplification are also reviewed briefly. The paper emphasizes some emerging trends in industrial practice, especially as regards modelling and control of complex systems. Examples from process control and flight control are used to illustrate some of the ideas.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. ©2010 IEEE.

A model of driver steering control incorporating the driver's sensing of steering torque

Steering feel, or steering torque feedback, is widely regarded as an important aspect of the handling quality of a vehicle. Despite this, there is little theoretical understanding of its role. This paper describes an initial attempt to model the role of steering torque feedback arising from lateral tyre forces. The path-following control of a nonlinear vehicle model is implemented using a time-varying model predictive controller. A series of Kalman filters are used to represent the driver's ability to generate estimates of the system states from noisy sensory measurements, including the steering torque. It is found that under constant road friction conditions, the steering torque feedback reduces path-following errors provided the friction is sufficiently high to prevent frequent saturation of the tyres. When the driver model is extended to allow identification of, and adaptation to, a varying friction condition, it is found that the steering torque assists in the accurate identification of the friction condition. The simulation results give insight into the role of steering torque feedback arising from lateral tyre forces. The paper concludes with recommendations for further work. © 2011 Taylor & Francis.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for sigma point placement, potentially causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. © 2011 Elsevier B.V.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Predictive control of a Boeing 747 aircraft using an FPGA

New embedded predictive control applications call for more eficient ways of solving quadratic programs (QPs) in order to meet demanding real-time, power and cost requirements. A single precision QP-on-a-chip controller is proposed, implemented in afield-programmable gate array (FPGA) with an iterative linear solver at its core. A novel offline scaling procedure is introduced to aid the convergence of the reduced precision solver. The feasibility of the proposed approach is demonstrated with a real-time hardware-in-the-loop (HIL) experimental setup where an ML605 FPGA board controls a nonlinear model of a Boeing 747 aircraft running on a desktop PC through an Ethernet link. Simulations show that the quality of the closed-loop control and accuracy of individual solutions is competitive with a conventional double precision controller solving linear systems using a Riccati recursion. © 2012 IFAC.