This paper presents a methodology for computation of artificial vector fields that allows a robot to converge to and circulate around generic curves specified in $n$ -dimensional spaces. These vector fields may be directly applied to solve several robot-navigation problems such as border monitoring, surveillance, target tracking, and multirobot pattern generation, with special application to fixed-wing aerial robots, which must keep a positive forward velocity and cannot converge to a single point. Unlike previous solutions found in the literature, the approach is based on fully continuous vector fields and is generalized to time-varying curves defined in $n$-dimensional spaces. We provide mathematical proofs and present simulation and experimental results that illustrate the applicability of the proposed approach. We also present a methodology for construction of the target curve based on a given set of its samples.

This note deals with the model-reference control of timed event graphs using the dioid algebra and the residuation theory. It proposes a control structure based on a precompensator and a feedback controller to improve the controlled system performance. It is shown that this approach always leads to an optimal behavior of the closed-loop system. An example is given to illustrate the proposed approach.

This paper describes a robust version to the TEAM 22 benchmark optimization problem and presents the methodology WCSA (worst case scenario approximation) to solve this problem and other similar cases. The robust multi-objective TEAM 22 model was built from its classical configuration by assuming the imprecisions in the design space. General and specific robust optimization formulas were developed to elaborate WCSA approach. WCSA adds an uncertainty parameter in the objective and constraint functions to perform the role of the system's imprecisions. A multi-objective genetic algorithm approach was chosen to deal with the robust formulation and to find out the set of robust minimizers that matches with the problem requirements. The behavior of the robust Pareto front is also examined.