Explicit/multi-parametric moving horizon estimation and model predictive control & their application to small unmanned aerial vehicles
Moving horizon estimation (MHE) is a class of estimation methods in which the system state and disturbance estimates are obtained by solving a constrained optimization problem. The main advantage of MHE is that information about the system can be explicitly considered in the form of constraints and...
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Imperial College London
2011
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| Online Access: | http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.537727 |
| Summary: | Moving horizon estimation (MHE) is a class of estimation methods in which the system state and disturbance estimates are obtained by solving a constrained optimization problem. The main advantage of MHE is that information about the system can be explicitly considered in the form of constraints and hence improve the estimates. In stochastic systems the estimation error will inevitably be non-zero and the controller needs to explicitly account for it to prevent constraint violations. In order for the controller to be robustified against the estimation error, bounds on the error need to be known. These bounds can be calculated if the dynamics that govern the estimation error are known. This work presents those dynamics for the unconstrained and the constrained case of the moving horizon estimator with a linear time-invariant model, and also discusses how the bounds on the estimation error can be obtained with set-theoretical methods. Those bounds are then used for robust output-feedback model predictive control (MPC). The MHE and the MPC are derived explicitly through multi-parametric programming. The complete framework is demonstrated using simultaneous MHE and tubebased MPC. The possibility of solving MPC explicitly is very appealing for flight control of small unmanned aerial vehicles (UAVs) because the behaviour of the controller is known in advance and can be guaranteed. Flight control is a challenging task that involves a multi-layer control structure where each decision influences the other layers and the overall performance. This work investigates the requirements on the different layers and their cross-effects. A linear model of the UAV is derived such that it captures the wind which is the most challenging disturbance for UAV flight. Particular focus is placed on the design of a model predictive controller as the autopilot and on in-flight wind estimation. |
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