SIMULATION-BASED SEARCH FOR HYBRID SYSTEM CONTROL AND ANALYSIS
Todd William Neller, Ph.D.
Stanford University, 2000
Adviser: Richard E. Fikes
This dissertation explores new algorithmic approaches to
simulation-based optimization, game-tree search, and tree search for
the control and analysis of hybrid systems. Hybrid systems are
systems that evolve with both discrete and continuous
behaviors. Examples of hybrid systems include diverse mode-switching
systems such as those we have used as focus problems: stepper motors,
magnetic levitation units, and submarine detection avoidance
scenarios. For hybrid systems with complex dynamics, the designer may
have little other than simulation as a tool to detect design flaws or
inform offline or real-time control. In approaching control and
analysis of such systems, we thus limit ourselves to a black-box
simulation of the system.
In our optimization research, we present a stepper motor control
design problem where the designer wishes to use simulation to
efficiently detect rare stall scenarios in the space of possible
system parameters and initial states if such scenarios exist. A
survey of global optimization techniques and extensions of such
techniques are made. We present novel information-based and
multi-level optimization methods. In extending game-tree search
for hybrid system control, we assume that controller action timing
discretization is given. We pose a magnetic levitation control
problem as an adversarial game for the purpose of robust control
synthesis. Assuming a given action parameter discretization, we
explore the use of game-graph (augmented cell-map) approximation and
alpha-beta pruning for fast adaptive online control. Assuming that
action parameter discretization must be computed dynamically in
search, we present a new application of information-based optimization
to alpha-beta search.
In extending tree search for hybrid system control, we assume that
action timing discretization is not given and must be computed
dynamically. We present a submarine detection avoidance problem as a
search problem for the purpose of fast, real-time tactical planning
assistance. Assuming discretized actions, new iterative refinement
techniques and a variant of best-first search are presented. Assuming
that neither action discretization nor action timing discretization is
given, we explore random, information-based, and dispersed dynamic
discretization of actions in iterative refinement searches.