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We provide a method for designing a quadratic pricing scheme to induce a desired local Nash equilibrium in an open-loop differential game with nonlinear dynamics. In addition, we present conditions when the induced equilibrium is a global equilibrium. The results are applied to the problem of inducing network managers to invest in security in a multi-network with an epidemic model of the spread of malware.

In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity. We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand to off-peak time. In contrast to most other mechanisms proposed in the literature, the key feature of our mechanism is its modest requirements on user preferences, i.e., it does not require exact knowledge of user responsiveness to rewards for shifting their demand from the peak to the off-peak time. Specifically, our mechanism utilizes a probabilistic reward structure for users who shift their demand to the off-peak time, and is robust to incomplete information about user demand and/or risk preferences. We approach the problem from the public good perspective, and demonstrate that the mechanism can be implemented via lottery-like schemes. Our mechanism permits to reduce the distribution losses, and thus improve efficiency of electricity distribution. Finally, the mechanism can be readily incorporated into the emerging demand response schemes (e.g., the time-of-day pricing, and critical peak pricing schemes), and has security and privacy-preserving properties.

Motivated by the study of complex motor control systems, we consider the identification and control of PieceWise Affine (PWA) systems and propose a novel data-driven framework that adaptively inverts the dynamics of such systems using noisy sampled data. First, we propose a novel PWA identification algorithm based on convex optimization applicable to both state–space and input/output models. Given a PWA model of the dynamics obtained from the identification algorithm, we consider the control of the resulting hybrid system where our goal is to find an input that reproduces a given reference trajectory or that extremizes a performance criterion. We demonstrate our proposed framework on a model of a jumping robot.

Blind system identification is known to be an ill-posed problem and without further assumptions, no unique solution is at hand. In this contribution, we are concerned with the task of identifying an ARX model from only output measurements. We phrase this as a constrained rank minimization problem and present a relaxed convex formulation to approximate its solution. To make the problem well posed we assume that the sought input lies in some known linear subspace.

The utility company has many motivations for modifying energy consumption patterns of consumers such as revenue decoupling and demand response programs. We model the utility company–consumer interaction as a reverse Stackelberg game and present an iterative algorithm to design incentives for consumers while estimating their utility functions. Incentives are designed using the aggregated as well as the disaggregated (device level) consumption data. We simulate the iterative control (incentive design) and estimation (utility learning and disaggregation) process for examples.