Robust stabilization of feedback linearizable time-varying uncertain nonlinear systems
Automatica (Journal of IFAC) - Special issue on robust control
A Monte Carlo approach to the analysis of control system robustness
Automatica (Journal of IFAC) - Special issue on robust control
Design of “softer” robust nonlinear control laws
Automatica (Journal of IFAC)
Probabilistic robustness analysis: explicit bounds for the minimum number of samples
Systems & Control Letters
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Expert Systems with Applications: An International Journal
Predicting numbers of performance failures in the manufacture of dynamic systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Probabilistically-robust performance optimization for controlled linear stochastic systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Engineering Applications of Artificial Intelligence
Hi-index | 22.15 |
Probabilistic robustness analysis and synthesis for nonlinear systems with uncertain parameters are presented. Monte Carlo simulation is used to estimate the likelihood of system instability and violation of performance requirements subject to variations of the probabilistic system parameters. Stochastic robust control synthesis searches the controller design parameter space to minimize a cost that is a function of the probabilities that design criteria will not be satisfied. The robust control design approach is illustrated by a simple nonlinear example. A modified feedback linearization control is chosen as controller structure, and the design parameters are searched by a genetic algorithm to achieve the tradeoff between stability and performance robustness.