Analysis and Synthesis of Control Systems via Risk Based Optimization

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Analysis and synthesis of control systems were investigated via risk based optimization from both theoretical and application viewpoints. In particular, rigorous stopping rules with risk assessment were derived for sequential randomized algorithms in stochastic approximation and optimization. A new scenario approach was also developed for optimization subject to robust and average constraints. Then, model invalidation with respect to noisy input-output data was established, and synthesis of control systems with variable sampling rate was considered via randomized algorithms.