Summary In this work, we study the mixed H2false/H∞ control for Markov jump linear systems with hidden Markov parameters. The hidden Markov process is denoted by false(normalθfalse(kfalse),trueθ^false(kfalse)false), where the nonobservable component θ(k) represents the mode of operation of the system, whereas trueθ^false(kfalse) represents the observable component provided by a detector. The goal is to obtain design techniques for mixed H2false/H∞ control problems, with the controllers depending only on the estimate trueθ^false(kfalse), for problems formulated in 3 different forms: (i) minimizing an upper bound on the H2 norm subject to a given restriction on the H∞ norm; (ii) minimizing an upper bound on the H∞ norm, while limiting the H2 norm; and (iii) minimizing a weighted combination of upper bounds of both the H2 and H∞ norms. We propose also new conditions for synthesizing robust controllers under parametric uncertainty in the detector probabilities and in the transition probabilities. The so‐called cluster case for the mixed H2false/H∞ control problem is also analyzed under the detector approach. The results are illustrated by means of 2 numerical examples.

No abstract

DO ANDROIDS DREAM OF ELEC-tric sheep? Science fiction books, movies, and TV shows have long described humanoid robots interacting with-even passing forreal humans. But how close are we today to that sort of seamlessness between robots and humans?In exploring that question, we have developed a methodology to provide the basic mechanisms that support humanoid robot cognition. Involved are mechanisms for attention control and pattern categorization (see the "Attention" sidebar), and constructing attentional maps of the environmental context that underlies behavior. Our system architecture supports several types of sensors, and we have experimented with many facets of the eventual integrated platform for learning control, acquired representations, and visual behavior. [1][2][3][4] Our goal is to develop a robotic system capable of interacting with its environment and eventually with humans. We're also interested in the relationships between visual, haptic (touch-based), proprioceptive (simuli arising from within), and motor systems in humans. To this end, we constructed a robot, Magilla, consisting of a stereo head and two robotic arms (with attached graspers). To explore the potential for rich and varied interactions with the world, we connect our robot's cognitive organization and development to that of a human's-a methodology missing in the approaches the AI community usually employs. Moreover, by grounding our systems in the natural world (that humans share) and by providing flexible and redundant means of interacting within this domain, we postulate that our robot will develop cognitive structures more like those of humans and, as a consequence, will interact with humans more constructively. Learning from childrenAgain, one of our main objectives in constructing an anthropomorphic torso is to study the relationship between visual and haptic sensory systems and their development in humans.The development of robot programs is an incremental search for strategies that exploit the intrinsic dynamics of the robot-world interaction. We interpret intrinsic dynamics fairly broadly as any kinematic, dynamic, perceptual, or motor synergy that produces characteristic and invariant temporal sequences of observable state variables.Humanoid robots are simply too complex to use traditional approaches from robotics and computer vision. The range of interaction possible and the required kinds of perceptual distinction necessary challenge commonly used methodologies for control and programming. Consequently, we have adopted an incremental and automatic approach to programming, modeled after the sensorimotor development of human children in the first two years of life. Genetically mediated maturational mecha- HUMANOID ROBOTS PROMISE TO LEAD US TOWARD MORE EFFECTIVE AND INFORMATIVE INTERACTIONS IEEE INTELLIGENT SYSTEMSnisms focus the infant on simple problems first and subsequently enrich these policies by including additional motor and perceptual systems. 5 Infants are constantly learning about the capabilities of their motor sy...

No abstract

No abstract