LTV-MPC Approach for Automated Vehicle Path Following at the Limit of Handling

Abstract

In this paper, a linear time-varying model predictive controller (LTV-MPC) is proposed for automated vehicle path-following applications. In the field of path following, the application of nonlinear MPCs is becoming more common; however, the major disadvantage of this algorithm is the high computational cost. During this research, the authors propose two methods to reduce the nonlinear terms: one is a novel method to define the path-following problem by transforming the path according to the actual state of the vehicle, while the other one is the application of a successive linearization technique to generate the state–space representation of the vehicle used for state prediction by the MPC. Furthermore, the dynamic effect of the steering system is examined as well by modeling the steering dynamics with a first-order lag. Using the proposed method, the necessary segment of the predefined path is transformed, the linearized model of the vehicle is calculated, and the optimal steering control vector is calculated for a finite horizon at every timestep. The longitudinal dynamics of the vehicle are controlled separately from the lateral dynamics by a PI cruise controller. The performance of the controller is evaluated and the effect of the steering model is examined as well.

Sim-to-Real Application of Reinforcement Learning Agents for Autonomous, Real Vehicle Drifting

“…This paper presents novel results on applying reinforcement learning agents for autonomous drifting on a full-scale, real test vehicle. This is the same car that was used in [13,19]. To the authors' best knowledge, this is the first time such an agent was successfully deployed to perform autonomous drifting on a real vehicle.…”

“…Typically, most of these are rear-wheel drive (RWD) vehicles, so the chosen model is also RWD, but, at the same time, drift-like behavior is not impossible with front-wheel or four-wheel drive vehicles either. The vehicle model features presented throughout this section are validated based on previous works [13,19].…”

“…MPC (Model Predictive Control) has proven successful for both stabilization and track-following drift tasks [18,19]. It also performed well when simulating changing road conditions, thus indicating a good adaptive property.…”

Path Planning for Autonomous Vehicles in Unknown Dynamic Environment Based on Deep Reinforcement Learning

“…They use the global path as a general direction while dynamically avoiding obstacles and performing short-distance path planning with the assistance of local path planning [4]. Based on algorithmic principles, path planning methods can be classified into raster mapbased, sampling-based, and bionic heuristic-based approaches [5][6][7][8][9][10][11]. Map-based methods include algorithms, such as Dijkstra's algorithm, A*, and D*.…”

Robust MPC for polytopic uncertain systems via a high-rate network with the round-robin scheduling

“…Over the past several decades, model predictive control (MPC), also called receding horizon control (RHC) has attracted extensive research and applications, see ( Li et al, 2015 ; Dai et al, 2018 ; Domina & Tihanyi, 2022 ). At each time instant, multiple control moves are calculated by solving a fixed number of online optimization problems in the future time instants, but only the first action is executed.…”