Browsing by Author "Johansen T."

Now showing 1 - 4 of 4
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    Approximate explicit constrained linear model predictive control via orthogonal search tree
    (2003-05-01) Johansen T.; Grancharova A.
    Solutions to constrained linear model predictive control problems can be precomputed offline in an explicit form as a piecewise linear state feedback on a polyhedral partition of the state-space, avoiding real-time optimization. We suggest an algorithm that will determine an approximate explicit piecewise linear state feedback by imposing an orthogonal search tree structure on the partition. This leads to a real-time computational complexity that is logarithmic in the number of regions in the partition, and the algorithm yields guarantees on the suboptimality, asymptotic stability and constraint fulfillment.
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    Approximate explicit model predictive control incorporating heuristics
    (2002-01-01) Grancharova A.; Johansen T.
    Explicit piecewise linear state feedback solutions to the constrained linear model predictive control problem have recently been characterized and computed numerically using multiparametric quadratic programming. The piecewise linear state feedback is defined on a polyhedral partitioning of the state space, which may be quite complex. Here we suggest an approximate multi-parametric quadratic programming approach, which has the advantages that the partition is structured as a binary search tree. This leads to real-time computation of the piecewise linear state feedback with a computational complexity that is logarithmic with respect to the number of regions in the partition. The algorithm is based on heuristic rules that are used to partition the state space and estimate the approximation error.
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    Distributed MPC-based path planning for UAVs under radio communication path loss constraints
    (2012-01-01) Grancharova A.; Grøtli E.; Johansen T.
    In this paper we address the Model Predictive Control (MPC)-based path planning problem for Unmanned Aerial Vehicles (UAVs). Our goal is to find trajectories that are safe with respect to grounding and collision, fuel efficient and satisfy criteria for communication such that the UAVs form a chain with a given radio communication capacity. A centralized MPC and a distributed MPC approaches to solve the path planning problem are proposed. Both approaches explicitly incorporate constraints on radio communication path losses, computed by using SPLAT!. In order to enhance the MPC problem computation, the terrain below each UAV and the communication path losses are approximated with linear functions. The control performance and the computational efficiency of the distributed MPC and the centralized MPC approaches are compared based on a simulation case study with two UAVs. © 2012 IFAC.
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    Distributed path planning for a UAV communication chain by dual decomposition
    (2012-01-01) Grancharova A.; Grøtli E.; Johansen T.
    In this paper, a distributed approach to Model Predictive Control (MPC)-based path planning for a UAV (Unmanned Aerial Vehicle) communication chain under radio path loss constraints is proposed. It is based on the dynamic dual decomposition method and reformulates the centralized path planning optimization problem with coupled constraints into an equivalent distributed path planning optimization problem, where the resulting sub-problems are completely decoupled. The MPC-based optimization sub-problems are computed autonomously within each UAV, using convex quadratic programming and gradient iterations, with the requirement that each UAV communicates its current measured position and the computed optimal velocity trajectory to its neighbouring UAVs. © 2012 IFAC.