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For a partially unknown linear systems, we present a systematic control design approach based on generated data from measurements of closed-loop experiments with suitable test controllers. These experiments are used to improve the achieved performance and to reduce the uncertainty about the unknown parts of the system. This is achieved through a parametrization of auspicious controllers with convex relaxation techniques from robust control, which guarantees that their implementation on the unknown plant is safe. This approach permits to systematically incorporate available prior knowledge about the system by employing the framework of linear fractional representations.
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The problem of exploration in unknown environments continues to pose a challenge for reinforcement learning algorithms, as interactions with the environment are usually expensive or limited. The technique of setting subgoals with an intrinsic reward allows for the use of supplemental feedback to aid agent in environment with sparse and delayed rewards. In fact, it can be an effective tool in directing the exploration behavior of the agent toward useful parts of the state space. In this paper, we consider problems where an agent faces an unknown task in the future and is given prior opportunities to ``practice on related tasks where the interactions are still expensive. We propose a one-step Bayes-optimal algorithm for selecting subgoal designs, along with the number of episodes and the episode length, to efficiently maximize the expected performance of an agent. We demonstrate its excellent performance on a variety of tasks and also prove an asymptotic optimality guarantee.
This work investigates the prediction performance of the kriging predictors. We derive some error bounds for the prediction error in terms of non-asymptotic probability under the uniform metric and $L_p$ metrics when the spectral densities of both the true and the imposed correlation functions decay algebraically. The Matern family is a prominent class of correlation functions of this kind. Our analysis shows that, when the smoothness of the imposed correlation function exceeds that of the true correlation function, the prediction error becomes more sensitive to the space-filling property of the design points. In particular, the kriging predictor can still reach the optimal rate of convergence, if the experimental design scheme is quasi-uniform. Lower bounds of the kriging prediction error are also derived under the uniform metric and $L_p$ metrics. An accurate characterization of this error is obtained, when an oversmoothed correlation function and a space-filling design is used.
The goal of this paper is to make Optimal Experimental Design (OED) computationally feasible for problems involving significant computational expense. We focus exclusively on the Mean Objective Cost of Uncertainty (MOCU), which is a specific methodology for OED, and we propose extensions to MOCU that leverage surrogates and adaptive sampling. We focus on reducing the computational expense associated with evaluating a large set of control policies across a large set of uncertain variables. We propose reducing the computational expense of MOCU by approximating intermediate calculations associated with each parameter/control pair with a surrogate. This surrogate is constructed from sparse sampling and (possibly) refined adaptively through a combination of sensitivity estimation and probabilistic knowledge gained directly from the experimental measurements prescribed from MOCU. We demonstrate our methods on example problems and compare performance relative to surrogate-approximated MOCU with no adaptive sampling and to full MOCU. We find evidence that adaptive sampling does improve performance, but the decision on whether to use surrogate-approximated MOCU versus full MOCU will depend on the relative expense of computation versus experimentation. If computation is more expensive than experimentation, then one should consider using our approach.
Systematic design and verification of advanced control strategies for complex systems under uncertainty largely remains an open problem. Despite the promise of blackbox optimization methods for automated controller tuning, they generally lack formal guarantees on the solution quality, which is especially important in the control of safety-critical systems. This paper focuses on obtaining 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 a distribution-free bound on the probability of the closed-loop performance measures. To mitigate the computational complexity of the data-driven scenario optimization method, we restrict ourselves to a discrete set of candidate tuning parameters. We propose to generate these candidates using constrained Bayesian optimization run multiple times from different random seed points. We apply the proposed method for tuning an economic nonlinear model predictive controller for a semibatch reactor modeled by seven highly nonlinear differential equations.
The combination of machine learning with control offers many opportunities, in particular for robust control. However, due to strong safety and reliability requirements in many real-world applications, providing rigorous statistical and control-theoretic guarantees is of utmost importance, yet difficult to achieve for learning-based control schemes. We present a general framework for learning-enhanced robust control that allows for systematic integration of prior engineering knowledge, is fully compatible with modern robust control and still comes with rigorous and practically meaningful guarantees. Building on the established Linear Fractional Representation and Integral Quadratic Constraints framework, we integrate Gaussian Process Regression as a learning component and state-of-the-art robust controller synthesis. In a concrete robust control example, our approach is demonstrated to yield improved performance with more data, while guarantees are maintained throughout.
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