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This article investigates the problem of using jerk (the rate-of-change of acceleration) motion control to move a given distance without overshoot. Distance, velocity, and acceleration are all brought to zero simultaneously. Although this problem has been identified in suspension control, it may also be of interest in mechatronics. In this research, particular controls were found that solved this problem. An algorithm was developed which is stable when used in a discretized form, as in a microprocessor continually updated with current state estimations. The control was found serendipitously. A wider range of controls is conjectured to provide the solution to the general minimum-time control problem under distance and jerk constraints. To describe these controls, they are here referred to as 'skim' controls. Also, a 'bangoff-bang' variant was developed and this was used in a simple test rig. The test developed in this research supports the utility and robustness of the control method.

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Abstract. Design and optimization of the flight controllers is a demanding task which usually requires deep engineering knowledge of intrinsic aircraft behavior. In this study, EAs are used to design a controller for recovery (landing) of a small fixed-wing UAV (Unmanned Aerial Vehicle) on a frigate ship deck. This paper presents an approach in which the whole structure of the control laws is evolved. The control laws are encoded in a way common for Genetic Programming. However, parameters are optimized independently using effective Evaluation Strategies, while structural changes occur at a slower rate. The fitness evaluation is made via test runs on a comprehensive 6 degree-of-freedom non-linear UAV model. The results show that an effective controller can be designed with little knowledge of the aircraft dynamics using appropriate evolutionary techniques. An evolved controller is demonstrated and a set of reliable algorithm parameters is identified.