Variable-structure controls are normally understood to be controls that have sliding modes and robustness as their main objective. In addition to sliding-mode controls, there are also variable-structure controls, which were developed for the purpose of intentionally precluding sliding modes and achieving high regulation rates and short settling times. Two types of such controls may be distinguished, variable-structure controls that switch between different parameters and a systematic further development of them called "soft variable-structure controls" that continuously vary controllers' parameters or structures and achieve nearly time-optimal control performance. This paper surveys soft variable-structure controls, compares them to other controls, taking a submarine dive-control as an example, and presents an outlook on their auspicious further development.

No abstract

No abstract

In this paper, we shall present a mathematical deÿnition of recurrent fuzzy systems and begin to systematically investigate the underlying theory involved. Unlike static fuzzy systems, recurrent fuzzy systems are equipped with time-delayed feedback of their output and allow representing knowledge-based dynamic processes that may be stated in the form of "if :::, then :::" rules. We study their relationship to automata and show that they have an automaton-like behavior when appropriately designed. In other cases, recurrent fuzzy system may exhibit chaotic behavior. We present su cient conditions for the occurrence of chaos in recurrent fuzzy systems that can easily be checked solely on the basis of the qualitative, linguistically formulated models. We also discuss the extent to which state graphs may be used for describing the behaviors of recurrent fuzzy systems.

We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. We first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the immanent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) -a generalization of the common Time-To-Collision (TTC) in arbitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.

This paper proposes a biologically inspired and technically implemented sound localization system to robustly estimate the position of a sound source in the frontal azimuthal half-plane. For localization, binaural cues are extracted using cochleagrams generated by a cochlear model that serve as input to the system. The basic idea of the model is to separately measure interaural time differences and interaural level differences for a number of frequencies and process these measurements as a whole. This leads to two-dimensional frequency versus time-delay representations of binaural cues, so-called activity maps. A probabilistic evaluation is presented to estimate the position of a sound source over time based on these activity maps. Learned reference maps for different azimuthal positions are integrated into the computation to gain time-dependent discrete conditional probabilities. At every timestep these probabilities are combined over frequencies and binaural cues to estimate the sound source position. In addition, they are propagated over time to improve position estimation. This leads to a system that is able to localize audible signals, for example human speech signals, even in reverberating environments.

In this article we present novel formation control laws based on artificial potential fields and consensus algorithms for a group of unicycles enabling arbitrary formation patterns for these nonholonomic vehicles. Given connected and balanced graphs we are able to prove stability of the rendezvous controller by applying the LaSalle-Krasovskii invariance principle. Further, we introduce obstacle avoidance, enabling a reactive behavior of the robotic group in unknown environments. The effectiveness of the proposed controllers is shown using computer simulations and finally, a classification w.r.t. existing solutions is done.

Unlike static f uzzy systems, recurrent f uzzy systems are equipped with f eedback loops and thus exhibit dynamic behaviors. The dynamics of a recurrent f uzzy system is largely determined by its rule base. The dynamic behavior of a signiÿcant subclass of recurrent f uzzy systems may be immediately deduced f rom their rule base, without need f or analyzing their mathematical description. Their equilibrium points may be readily identiÿed and their stability behaviors investigated based on their rule base. The investigations involved lead to convergence theorems and other statements that preclude chaotic dynamics.