427 research outputs found
Robustly stable feedback min-max model predictive control
Published versio
Fault tolerant control using Gaussian processes and model predictive control
Abstract
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.This research was supported by EU Framework
Programme 7, project 314544, RECONFIGURE:
Reconfiguration of Control in Flight for Integral Global
Upset Recovery, as well as the China Scholarship Council
and the Cambridge Overseas Trust.This is the final published version. It first appeared at http://www.degruyter.com/view/j/amcs.2015.25.issue-1/amcs-2015-0010/amcs-2015-0010.xml
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Reconfigurable predictive control for redundantly actuated systems with parameterised input constraints
A method is proposed for on-line recon guration of the terminal constraint used to provide theoretical nominal stability
guarantees in linear model predictive control (MPC). By parameterising the terminal constraint, its complete reconstruction
is avoided when input constraints are modi ed to accommodate faults. To enlarge the region of feasibility of the
terminal control law for a certain class of input faults with redundantly actuated plants, the linear terminal controller
is de ned in terms of virtual commands. A suitable terminal cost weighting for the recon gurable MPC is obtained by
means of an upper bound on the cost for all feasible realisations of the virtual commands from the terminal controller.
Conditions are proposed that guarantee feasibility recovery for a de ned subset of faults. The proposed method is
demonstrated by means of a numerical example.The research leading to these results has received function
from the European Union Seventh Framework Programme
FP7/2007{2013 under grant agreement no. 314 544.This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S0167691114000127
Incorporating control performance tuning into economic model predictive control
This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ECC.2015.7330599Economic model predictive control (eMPC), where an economic objective is used directly as the objective function of the control system, has gained much popularity in recent literature. However, with a purely economic objective, the control designer has no influence over the control performance of the process. In this paper, we propose a means of tuning the objective function in order to give some level of control performance. Also, the stability proof for eMPC relies on some strict-dissipativity condition. We also show how this condition can be satisfied when the system is only dissipative with respect to the original objective function.O I. Olanrewaju is sponsored by the Federal Government of Nigeri
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A longitudinal flight control law based on robust MPC and H<inf>2</inf> methods to accommodate sensor loss in the RECONFIGURE benchmark
The feedback gains in state-of-the-art flight control laws for commercial aircraft are scheduled as a function of values such as airspeed, mass, and centre of gravity. If estimates of these are lost due to multiple simultaneous sensor failures, it is necessary for the pilot to either directly command control surface positions, or to revert to an alternative control law. This work develops a robust backup load-factor tracking control law, that does not depend on these parameters, based on application of theory from robust MPC and
H2 control. First the methods are applied with loss only of airdata, and subsequently also with loss of mass and CoG estimates. Local linear analysis indicates satisfactory performance over a wide range of operating points. Finally, the resulting control laws are demonstrated on the nonlinear RECONFIGURE benchmark, which is derived from Airbus's high delity, industrially-validated simulator, OSMA.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ifacol.2015.09.65
Soft Constraints and Exact Penalty Functions in Model Predictive Control
One of the strengths of Model Predictive Control (MPC) is its ability to incorporate constraints in the control formulation. Often a disturbance drives the system into a region where the MPC problem is infeasible and hence no control action can be computed. Feasibility can be recovered by softening the constraints using slack variables. This approach does not necessarily guarantee that the constraints will be satisfied, if possible. Results from the theory of exact penalty functions can be used to guarantee constraint satisfaction. This paper describes a method for computing a lower bound for the constraint violation penalty weight of the exact penalty function. One can then guarantee that the soft-constrained MPC solution will be equal to the hard-constrained MPC solution for a bounded subset of initial states, control inputs and reference trajectories
Predictive control for spacecraft rendezvous in an elliptical orbit using an FPGA
A field programmable gate array (FPGA)-based predictive controller for a spacecraft rendezvous man{\oe}uvre is presented. A linear time varying prediction model is used to accommodate elliptical orbits, and a variable prediction horizon is used to facilitate finite time completion of man{\oe}uvres. The resulting constrained optimisation problems are solved using a primal dual interior point algorithm. The majority of the computational demand is in solving a set of linear equations at each iteration of this algorithm. To accelerate this operation, a custom circuit is implemented, using a combination of Mathworks HDL Coder and Xilinx System Generator for DSP, and used as a peripheral to a MicroBlaze soft core processor. The system is demonstrated in closed loop by linking the FPGA with a simulation of the plant dynamics running in Simulink on a PC, using Ethernet.This work was supported by the Engineering and Physical Sciences Research Council (Grant EP/G030308/1) as well as industrial support from Xilinx, Mathworks and the European Space Agency.European Control Conference 2013 (ECC13), July 17-19, Zurich, Switzerlan
Sequential Monte Carlo Optimisation for Air Traffic Management
This report shows that significant reduction in fuel use could be achieved by
the adoption of `free flight' type of trajectories in the Terminal Manoeuvring
Area (TMA) of an airport, under the control of an algorithm which optimises the
trajectories of all the aircraft within the TMA simultaneously while
maintaining safe separation. We propose the real-time use of Monte Carlo
optimisation in the framework of Model Predictive Control (MPC) as the
trajectory planning algorithm. Implementation on a Graphical Processor Unit
(GPU) allows the exploitation of the parallelism inherent in Monte Carlo
methods, which results in solution speeds high enough to allow real-time use.
We demonstrate the solution of very complicated scenarios with both arrival and
departure aircraft, in three dimensions, in the presence of a stochastic wind
model and non-convex safe-separation constraints. We evaluate our algorithm on
flight data obtained in the London Gatwick Airport TMA, and show that fuel
saving of about 30% can be obtained. We also demonstrate the flexibility of our
approach by adding noise-reduction objectives to the problem and observing the
resulting modifications to arrival and departure trajectories
Model predictive control with prioritised actuators
This paper deals with the control of systems for which there is a clear distinction between preferred and auxiliary actuators, the latter to be used only when the control error is large. Explicit MPC and exact penalty functions are used to show how ℓasso-MPC can implement this idea. Two ℓasso-MPC versions are reviewed, that allow the designer to impose a certain nominal operations zone, namely, a neighbourhood of the set-point in which the auxiliary actuators are never used. For the sake of brevity, the required procedures are shown only for version 1, but it is also discussed how they can be extended to version 2. Limitations due to the presence of constraints are also formalised. The ℓasso-MPC version 1 can be used to embed an existing linear quadratic MPC, while ℓasso-MPC version 2 can be used to obtain multiple levels of priority. The paradigm is demonstrated for version 1 through the control of the linearised lateral dynamics of a Boeing 747. In particular, the approach uses the spoilers only when the control error is larger than a desired threshold.Research supported by the EPSRC grant “Control for Energy and
Sustainability”, EP/G066477/1.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ECC.2015.733059
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Designing output-feedback predictive controllers by reverse-engineering existing LTI controllers
An approach to designing a constrained output-feedback predictive controller that has the same small-signal properties as a pre-existing output-feedback linear time invariant controller is proposed. Systematic guidelines are proposed to select an appropriate (non-unique) realization of the resulting state observer. A method is proposed to transform a class of offset-free reference tracking controllers into the combination of an observer, steady-state target calculator and predictive controller. The procedure is demonstrated with a numerical example.This work was supported by Engineering and Physical Sciences Research Council grant EP/G030308/1, the European Space Agency and EADS Astrium.This is the author's version of an article that has been published in IEEE Transactions on Automatic Control. Changes were made to this version by the publisher prior to publication. The final version of record is available at: http://dx.doi.org/10.1109/TAC.2013.2258781 (c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
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