Palo Alto Research Center (PARC)
3333 Coyote Hill Drive, Palo Alto, CA 94304 Tel: 650-812-4310 E-mail: hhindi@parc.com URL:http://www2.parc.com/isl/members/hhindi
Optimization: convex, multiobjective, robust, and dynamic;
convex relaxations; decomposition methods; interior point algorithms;
planning and scheduling;
Control: robust, distributed, optimal, networked,
multivariable, nonlinear;
subspace methods;
system identification; uncertainty models;
model reduction;
Networks: routing and scheduling; flexible reconfigurable
manufacturing systems; distributed control
algorithms; congestion
control;
Sensors: active and distributed sensing, and Markov Chain models
Signal Processing: Kalman filtering; subspace methods for
system identification and control; clustering algorithms
Industry Applications: Distributed, networked control
systems for material handling; flexible manufacturing
systems; control of high speed systems (particle accelerators,
computer hard-disk drives, nanodevices); physical modeling;
identification; uncertainty modeling; robust, optimal,
multivariable control; time-optimal control; embedded control
software;
A Matrix Formalism for Landau Damping
SLAC-PUB-7978, Oct 1998. 7pp.
Talk given at 14th Advanced ICFA Beam Dynamics Workshop:
Beam Dynamics Issues for e+ e- Factories (ICFA 97), Frascati,
Italy, 20-26 Oct 1997.
PEP-II: RF and Feedback R & D
SLAC-PUB-5979, LBL-33525, Nov 1992. 4pp.
Presented at Particles & Fields 92: 7th Meeting of the
Division of Particles Fields of the APS (DPF 92), Batavia, IL,
10-14 Nov 1992.
Local Analysis of Perturbed Linear Systems with Application to
Saturating Control Systems
PhD Thesis, Stanford University, EE Dept., March 2000.
The goal of this thesis was to develop a set of tools for computing
guaranteed regions of attraction, bounds on L_2 disturbance
rejection and on L_2 gain, for linear systems perturbed by sector
bounded nonlinearities, in particular, saturation nonlinearities.
This allows more daring and better specified designs.
Efficient Linear Matrix Inequality (LMI) global analysis techniques
were extended to the problem of local stability and performance
analysis. The issue of computing optimal Lyapunov functions for local
analysis was addressed and used to point out the advantages and
limitations of the new techniques. The result is a set of tools that
are: computationally efficient, simple to implement, easy to
understand, not overly conservative, able to handle multiple
saturation nonlinearities and both continuous- and discrete-time
systems. Two applications for the tools are presented: control systems
with saturation, and distortion analysis in power amplifiers with
saturation.