[show abstract][hide abstract] ABSTRACT: In this chapter we describe the block backstepping approach to flight control law design. Block backstepping is a Lyapunov
based technique for controller design which is particularly well suited to the rigid body control problem where the main means
of control is through the moments, which is the case in most aircraft. The resulting controller has semi-global (in the state
space) stabilising properties and has a moderate number of parameters that can be used for tuning. We illustrate the theory
by simulations of the ADMIRE model with a block backstepping controller in demanding manoeuvres such as high-alpha flight
and high-rate velocity vector rolls.
[show abstract][hide abstract] ABSTRACT: On-line trajectory optimization in three dimensional space is the main topic of the pa-per at hand. The high-level framework augments on-line receding horizon control with an off-line computed terminal cost that captures the global characteristics of the environment, as well as any possible mission objectives. The first part of the paper is devoted to the single vehicle case while the second part considers the problem of simultaneous arrival of multiple aerial vehicles. The main contribution of the first part is two-fold. Firstly, by augmenting a so called safety maneuver at the end of the planned trajectory, this paper extends previous results by ad-dressing provable safety properties in a 3D setting. Secondly, assuming initial feasibility, the planning method presented is shown to have finite time task completion. Moreover, a quantitative comparison between the two competing objectives of optimality and computa-tional tractability is made. Finally, some other key characteristics of the trajectory planner, such as ability to minimize threat exposure and robustness, are highlighted through simu-lations. As for the simultaneous arrival problem considered in the second part, by using a time-scale separation principle, we are able to adopt standard Laplacian control to a consensus problem which is neither unconstrained, nor first order.
[show abstract][hide abstract] ABSTRACT: In this paper we propose a way of increasing the efficiency of some direct Receding Horizon Control (RHC) schemes. The basic idea is to adapt the allocation of compu-tational resources to how the iterative plans are used. By using Gradual Dense-Sparse discretizations (GDS), we make sure that the plans are detailed where they need to be, i.e., in the very near future, and less detailed further ahead. The gradual transition in discretization density reflects increased uncertainty and reduced need for detail near the end of the planning horizon. The proposed extension is natural, since the standard RHC approach already contains a computational asymmetry in terms of the coarse cost-to-go computations and the more detailed short horizon plans. Using GDS discretizations, we bring this asymmetry one step further, and let the short horizon plans themselves be detailed in the near term and more coarse in the long term. The rationale for different levels of detail is as follows. 1) Near future plans need to be implemented soon, while far future plans can be refined or revised later. 2) More accurate sensor information is available about the system and its surroundings in the near future, and detailed planning is only rational in low uncertainty situations. 3) It has been shown that reducing the node density in the later parts of fixed horizon optimal control problems gives a very small reduction in the solution quality of the first part of the trajectory. The reduced level of detail in the later parts of a plan can increase the efficiency of the RHC in two ways. If the discretization is made sparse by removing nodes, fewer computations are necessary, and if the discretization is made sparse by spreading the last nodes over a longer time-horizon, the performance will be improved.