Abstract
We propose a two-layer scheme to control a set of vehicles moving in a formation.
The first layer, the trajectory controller, is a nonlinear controller since most vehicles are nonholonomic systems and require a nonlinear, even discontinuous, feedback to stabilize them. The trajectory controller, a model predictive controller, computes centrally a bang-bang control law and only a small set of parameters need to be transmitted to each vehicle at each iteration.
The second layer, the formation controller, aims to compensate for small changes around a nominal trajectory maintaining the relative positions between vehicles. We argue that the formation control can be, in most cases, adequately carried out by a linear model predictive controller accommodating input and state constraints. This has the advantage that the control laws for each vehicle are simple piecewise affine feedback laws that can be pre-computed off-line and implemented in a distributed way in each vehicle.
Although several optimization problems have to be solved, the control strategy proposed results in a simple and efficient implementation where no optimization problem needs to be solved in real-time at each vehicle.
Research supported by FCT/POCI 2010/FEDER through Project POCTI/MAT/ 61842/2004.
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Fontes, F.A.C.C., Fontes, D.B.M.M., Caldeira, A.C.D. (2009). Model Predictive Control of Vehicle Formations. In: Hirsch, M.J., Commander, C.W., Pardalos, P.M., Murphey, R. (eds) Optimization and Cooperative Control Strategies. Lecture Notes in Control and Information Sciences, vol 381. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88063-9_21
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