김수연 교수

Abstract Safety is a critical factor in reinforcement learning (RL) in chemical processes. In our previous work, we had proposed a new stability‐guaranteed RL for unconstrained nonlinear control‐affine systems. In the approximate policy iteration algorithm, a Lyapunov neural network (LNN) was updated while being restricted to the control Lyapunov function, and a policy was updated using a variation of Sontag's formula. In this study, we additionally consider state and input constraints by introd

Abstract We propose a new reinforcement learning approach for nonlinear optimal control where the value function is updated as restricted to control Lyapunov function (CLF) and the policy is improved using a variation of Sontag's formula. The practical asymptotic stability of the closed‐loop system is guaranteed during the training and at the end of training without requiring an additional actor network and its update rule. For a single‐layer neural network (NN) with exact basis functions, the a

In recent years, more stringent regulatory standards (EURO 6 emission standards) with a real driving test have been adopted for diesel vehicles. To meet the new regulations, a lean NOx trap (LNT) followed by a urealess selective catalytic reduction [passive SCR (pSCR)], i.e., LNT-pSCR, has been proposed as one of the promising aftertreatment systems for light-duty vehicles. In this brief, we propose hybrid nonlinear model predictive control (NMPC) that determines the optimal timing of rich mode

The water supply network has a complex structure especially in cities with high population density. A damage to the water pipe can occur in the form of a leakage or a burst and the technique for early detection of the occurrence and for the exact determination of the location is required. In this paper, we propose a novel method that can detect the leakage of the water supply network using the pressure data. After the noise is eliminated using the Kalman Filter, the mean of normal state pressure

Nonlinear Model Predictive Control (NMPC) is an optimization-based control strategy that directly incorporates nonlinear dynamic models and has desirable stability and robustness properties. State estimation is an essential counterpart to NMPC and Moving Horizon Estimation (MHE) is also an optimization-based strategy that directly incorporates the nonlinear dynamics and constraints. However, NMPC and MHE are challenged by the computational expense of solving NLPs at each time step. For NMPC, thi