Sparse optimal control of networks with multiplicative noise via policy gradient
This work addresses control challenges in complex dynamical networks affected by multiplicative noise, which is an incremental improvement in the domain of network control.
The paper tackled the problem of designing near-optimal sparse controllers for networks with multiplicative noise using policy gradient methods, achieving convergence to performant sparse mean-square stabilizing controllers in numerical experiments on a large networked system.
We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasingly important in emerging complex dynamical networks. Various regularization schemes are examined and incorporated into the optimization by the use of gradient, subgradient, and proximal gradient methods. Numerical experiments on a large networked system show that the algorithms converge to performant sparse mean-square stabilizing controllers.