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Systematic design and verification of advanced control strategies for complexsystems under uncertainty largely remains an open problem. Despite the promiseof blackbox optimization methods for automated controller tuning, theygenerally lack formal guarantees on the solution quality, which is especiallyimportant in the control of safety-critical systems. This paper focuses onobtaining closed-loop performance guarantees for automated controller tuning,which can be formulated as a black-box optimization problem under uncertainty.We use recent advances in non-convex scenario theory to provide adistribution-free bound on the probability of the closed-loop performancemeasures. To mitigate the computational complexity of the data-driven scenariooptimization method, we restrict ourselves to a discrete set of candidatetuning parameters. We propose to generate these candidates using constrainedBayesian optimization run multiple times from different random seed points. Weapply the proposed method for tuning an economic nonlinear model predictivecontroller for a semibatch reactor modeled by seven highly nonlineardifferential equations.

Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees