Output-feedback adaptive model predictive control for ramp metering: a set-membership approach

Abstract: Ramp metering, which regulates the flow entering the freeway, is one of the most effective freeway traffic control methods. This paper introduces an output-feedback adaptive approach to ramp metering that combines model predictive control (MPC) with set-membership parameter and state estimation. The set-membership estimator is based on a mixed-monotone embedding of underlying traffic dynamics. The embedding is also used as the modeling basis for MPC optimization. For a freeway stretch with unknown parameters and partial measurement on the freeway mainline, we provide sufficient conditions on the control horizon, cost functions, terminal sets of MPC, and inflow demand at the ramps such that the queue lengths in the closed-loop system remain bounded. The sufficient condition on the demand matches the necessary condition, thereby proving maximal throughput under the proposed controller. The result is strengthened to input-to-state stability when model parameters and demand are known. The stability analysis is conducted for the case of constant demand and unbounded on-ramps. The closed-loop trajectory data generated by the proposed controller is shown to facilitate finite time estimation of free-flow model parameters, i.e., free-flow speed and turning ratios. Simulation results illustrate stability of the closed-loop system under the proposed controller with time-varying demand and few mainline measurements, for which the system becomes unstable under a well-known approach from the literature. This indicates that the proposed controller renders higher throughput than the well-known approach, possibly using more computing resources.