Optimal Control Via Neural Networks: A Convex Approach

TL;DR

This paper introduces input convex neural networks that enable accurate modeling and convex control of complex systems, leading to improved performance and efficiency in control tasks like locomotion and HVAC management.

Contribution

It presents a novel class of input convex neural networks that facilitate convex optimization-based control for complex dynamical systems.

Findings

Achieved over 10% higher performance in MuJoCo locomotion tasks.

Reduced energy consumption by up to 20% in HVAC control.

Demonstrated the effectiveness of convex neural networks in various control applications.

Abstract

Control of complex systems involves both system identification and controller design. Deep neural networks have proven to be successful in many identification tasks, however, from model-based control perspective, these networks are difficult to work with because they are typically nonlinear and nonconvex. Therefore many systems are still identified and controlled based on simple linear models despite their poor representation capability. In this paper we bridge the gap between model accuracy and control tractability faced by neural networks, by explicitly constructing networks that are convex with respect to their inputs. We show that these input convex networks can be trained to obtain accurate models of complex physical systems. In particular, we design input convex recurrent neural networks to capture temporal behavior of dynamical systems. Then optimal controllers can be achieved via solving a convex model predictive control problem. Experiment results demonstrate the good potential of the proposed input convex neural network based approach in a variety of control applications. In particular we show that in the MuJoCo locomotion tasks, we could achieve over 10% higher performance using 5* less time compared with state-of-the-art model-based reinforcement learning method; and in the building HVAC control example, our method achieved up to 20% energy reduction compared with classic linear models.