This book discusses systematic designs of stable adaptive fuzzy logic
controllers employing hybridizations of Lyapunov strategy-based
approaches/H∞ theory-based approaches and contemporary
stochastic optimization techniques. The text demonstrates how candidate
stochastic optimization techniques like Particle swarm optimization
(PSO), harmony search (HS) algorithms, covariance matrix adaptation
(CMA) etc. can be utilized in conjunction with the Lyapunov theory/H∞
theory to develop such hybrid control strategies. The goal of developing
a series of such hybridization processes is to combine the strengths of
both Lyapunov theory/H∞ theory-based local search methods and
stochastic optimization-based global search methods, so as to attain
superior control algorithms that can simultaneously achieve desired
asymptotic performance and provide improved transient responses. The
book also demonstrates how these intelligent adaptive control algorithms
can be effectively utilized in real-life applications such as in
temperature control for air heater systems with transportation delay,
vision-based navigation of mobile robots, intelligent control of robot
manipulators etc.