An Analytical Safe Approximation to Joint Chance-Constrained Programming With Additive Gaussian Noises

We propose a safe approximation to joint chance-constrained programming, where the constraint functions are additively dependent on normally-distributed random vector. The is analytical, meaning that it requires neither numerical integrations nor sampling-based probability approximations. Under mild assumptions, standard nonlinear program. compare this new another analytical for programming based Boole's inequality and scenario approach through two examples representing constrained control of linear Gauss–Markov models. It shown our proposed has lower degree conservativeness compared these alternative approaches.