Related papers: Nonlinear Moving Horizon Estimation and Model Pred…
We propose and analyze a real-time model predictive control (MPC) scheme that utilizes stored data to improve its performance by learning the value function online with stability guarantees. For linear and nonlinear systems, a learning…
There are many situations for which an unmanned ground vehicle has to work with only partial observability of the environment. Therefore, a feasible nonholonomic obstacle avoidance and target tracking action must be generated immediately…
Human beings can utilize multiple balance strategies, e.g. step location adjustment and angular momentum adaptation, to maintain balance when walking under dynamic disturbances. In this work, we propose a novel Nonlinear Model Predictive…
In this paper we present a framework for risk-averse model predictive control (MPC) of linear systems affected by multiplicative uncertainty. Our key innovation is to consider time-consistent, dynamic risk metrics as objective functions to…
The fact that a proper HVAC control strategy can reduce the energy consumption of a building by up to 45% has driven significant research in demand-based HVAC control. This paper presents a novel framework for modeling and analysis of…
This article presents a Visual Servoing Nonlinear Model Predictive Control (NMPC) scheme for autonomously tracking a moving target using multirotor Unmanned Aerial Vehicles (UAVs). The scheme is developed for surveillance and tracking of…
Moving horizon estimation (MHE) offers benefits relative to other estimation approaches by its ability to explicitly handle constraints, but suffers increased computation cost. To help enable MHE on platforms with limited computation power,…
This paper proposes a novel orientation-aware model predictive control (MPC) for dynamic humanoid walking that can plan footstep locations online. Instead of a point-mass model, this work uses the augmented single rigid body model (aSRBM)…
How much energy, money, and emissions can advanced control of heating and cooling equipment save in real buildings? To address this question, researchers sometimes control a small number of thermal zones within a larger multi-zone building,…
A fast inverse heat conduction model (IHCM) is developed for estimating unknown properties of multi-layer composites considering internal heat generation. This work builds on the validated analytical forward models presented in Part I.…
Residential electric water heaters have significant load shifting capabilities due to their thermal heat capacity and large energy consumption. Model predictive control (MPC) has been shown to be an effective control strategy to enable…
As off-the-shelf (OTS) autopilots become more widely available and user-friendly and the drone market expands, safer, more efficient, and more complex motion planning and control will become necessary for fixed-wing aerial robotic…
Energy consumed in buildings takes significant portions of the total global energy usage. A large amount of building energy is used for heating, cooling, ventilation, and air-conditioning (HVAC). However, compared to its importance,…
Active feedback stabilization of the dominant resistive wall mode (RWM) for an ITER H-mode scenario at high plasma pressure using infinite-horizon model predictive control (MPC) is presented. The MPC approach is closely-related to…
This paper describes the design and implementation of linear controllers with a switching condition for a nonlinear hot air blower system (HABS) process trainer PT326. The system is interfaced with a computer through a USB based data…
Most modern HVAC systems suffer from two intrinsic problems. First, inability to meet diverse comfort requirements of the occupants. Second, heat or cool an entire zone even when the zone is only partially occupied. Both issues can be…
A standard way of finding a feedback law that stabilizes a control system to an operating point is to recast the problem as an infinite horizon optimal control problem. If the optimal cost and the optmal feedback can be found on a large…
Due to changes in frequency and intensity of extreme weather events, such as heatwaves and storms, power systems around the globe are having to deal with increased imbalance between demand and supply and additional risk of loss of supply,…
Model predictive control (MPC) has proven useful in enabling safe and optimal motion planning for autonomous vehicles. In this paper, we investigate how to achieve MPC-based motion planning when a neural state-space model represents the…
Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying as needed the horizon length of Model Predictive Control (MPC). Its goal is to achieve stabilization with horizons as small as possible so that MPC can be used on…