skip to main content

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Flatness-based Model Predictive Control for Autonomous Vehicle Trajectory Tracking
Model predictive control (MPC) has become more relevant to vehicle dynamics control due to its inherent capacity of treating system constraints. However, online optimization from MPC introduces an extensive computational burden for today’s onboard microprocessors. To alleviate MPC computational load, several methods have been proposed. Among them, online successive system linearization and the resulting linear time-varying model predictive controller (LTVMPC) is one of the most popular options. Nevertheless, such online successive linearization commonly approximates the original (nonlinear) system by a linear one, which inevitably introduces extra modeling errors and therefore reduces MPC performance. Actually, if the controlled system possesses the “differential flatness” property, then it can be exactly linearized and an equivalent linear model will appear. This linear model maintains all the nonlinear features of the original system and can be utilized to design a flatness-based model predictive controller (FMPC). CarSim-Simulink joint simulations demonstrate that the proposed FMPC substantially outperforms a classical LTVMPC in terms of the path-tracking performance for autonomous vehicles.  more » « less
Award ID(s):
1901632
PAR ID:
10190734
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference
Page Range / eLocation ID:
4146 – 4151
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ( , Dynamic Systems and Control Conference)
    null (Ed.)
    In modern high-performance aircraft, the Fuel Thermal Management System (FTMS) plays a critical role in the overall thermal energy management of the aircraft. Actuator and state constraints in the FTMS limit the thermal endurance and capabilities of the aircraft. Thus, an effective control strategy must plan and execute optimized transient fuel mass and temperature trajectories subject to these constraints over the entire course of operation. For the control of linear systems, hierarchical Model Predictive Control (MPC) has shown to be an effective approach to coordinating both short- and long-term system operation with reduced computational complexity. However, for controlling nonlinear systems, common approaches to system linearization may no longer be effective due to the long prediction horizons of upper-level controllers. This paper explores the limitations of using linear models for hierarchical MPC of the nonlinear FTMS found in aircraft. Numerical simulation results show that linearized models work well for lower-level controllers with short prediction horizons but lead to significant reductions in aircraft thermal endurance when used for upper-level controllers with long prediction horizons. Therefore, a mixed-linearity hierarchical MPC formulation is presented with a nonlinear upper-level controller and a linear lower-level controller to achieve both high performance and high computational efficiency. 
    more » « less
  2. ; ( , Advanced Control for Applications)

    This study proposes using a wavelet neural network (WNN) with a feedforward component and a model predictive controller (MPC) for online nonlinear system identification and control over a communication network. The WNN performs the online identification of the nonlinear system. The MPC uses the model to predict the future outputs of the system over an extended prediction horizon and calculates the optimal future inputs by minimizing a controller cost function. A computationally efficient formulation for the controller is presented to reduce the computational complexity of the MPC for online implementation and Lyapunov theory is used to prove the stability of the MPC. The methodology is applied to the online identification and control of an unmanned autonomous vehicle. Simulation results show that the MPC with an extended prediction horizon can effectively control the system in the presence of fixed or random network delay.

     
    more » « less
  3. ; ; ; ( , Journal of Dynamic Systems, Measurement, and Control)
    Model predictive control (MPC) has drawn a considerable amount of attention in automotive applications during the last decade, partially due to its systematic capacity of treating system constraints. Even though having received broad acknowledgements, there still exist two intrinsic shortcomings on this optimization-based control strategy, namely the extensive online calculation burden and the complex tuning process, which hinder MPC from being applied to a wider extent. To tackle these two drawbacks, different methods were proposed. Nevertheless, the majority of these approaches treat these two issues independently. However, parameter tuning in fact has double-sided effects on both the controller performance and the real-time computational burden. Due to the lack of theoretical tools for globally analyzing the complex conflicts among MPC parameter tuning, controller performance optimization, and computational burden easement, a look-up table-based online parameter selection method is proposed in this paper to help a vehicle track its reference path under both the stability and computational capacity constraints. matlab-carsim conjoint simulations show the effectiveness of the proposed strategy. 
    more » « less
  4. ; ; ( , 2020-2021 International Conference on High Performance Buildings)
    null (Ed.)
    Model predictive control (MPC) has been widely investigated for climate control of commercial buildings for both energy efficiency and demand flexibility. However, most MPC formulations ignore humidity and latent heat. The inclusion of moisture makes the problem considerably more challenging, primarily since a cooling and dehumidifying coil model which accounts for both sensible and latent heat transfers is needed. In our recent work, we proposed an MPC controller in which humidity and latent heat were incorporated in a principled manner, by using a reduced-order model of the cooling coil. Because of the highly nonlinear nature of the process in a cooling coil, the model needs to be modified based on certain weather/climatic conditions to have sufficient prediction accuracy. Doing so, however, leads to a mixed-integer nonlinear program (MINLP) that is challenging to solve. In this work, we propose an MPC formulation that retains the NLP (nonlinear programming problem) structure in all climate zones/weather conditions. This feature makes the control system capable of autonomous operation. Simulations in multiple climate zones and weather conditions verify the energy savings performance, and autonomy of the proposed controller. We also compare the performance of the proposed MPC controller with an MPC formulation that does not explicitly consider humidity. Under certain conditions, it is found that the MPC controller that excludes humidity leads to poor humidity control, or higher energy usage as it is unaware of the latent load on the cooling coil. 
    more » « less
  5. ; ; ; ( , The 28th International Symposium on Industrial Electronics (ISIE))
    In isolated power systems with low rotational inertia, fast-frequency control strategies are required to maintain frequency stability. Furthermore, with limited resources in such isolated systems, the deployed control strategies have to provide the flexibility to handle operational constraints so the controller is optimal from a technical as well as an economical point-ofview. In this paper, a model predictive control (MPC) approach is proposed to maintain the frequency stability of these low inertia power systems, such as microgrids. Given a predictive model of the system, MPC computes control actions by recursively solving a finite-horizon, online optimization problem that satisfies peak power output and ramp-rate constraints. MATLAB/Simulink based simulations show the effectiveness of the controller to reduce frequency deviations and the rate-of-change-of-frequency (ROCOF) of the system. By proper selection of controller parameters, desired performance can be achieved while respecting the physical constraints on inverter peak power and/or ramp-rates. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email