Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-6· 0 citations· 19 references
Abstract
Pure Pursuit (PP) is a widely adopted geometric path-tracking controller valued for its robustness, low computational cost, and ease of deployment. However, under aggressive driving conditions, tire slip and transient yaw dynamics alter the steering-to-curvature map, degrading tracking performance. This paper proposes a minimal modification of PP in which the nominal steering command is scaled by a single gain, increasing effective steering authority without introducing integral action, sideslip estimation, or additional dynamic compensation layers. The controller is evaluated in MATLAB/Simulink co-simulation with VI-CarRealTime using a high-fidelity model of the SGe-06 Formula Student vehicle developed at the University of Padua, with parameters tuned via NSGA-III by jointly optimizing RMS lateral error, RMS steering-rate demand, and lap time. The method is assessed under both ideal and realistic sensing conditions and compared against standard PP and a state-of-the-art sideslip-compensated approach. Results show that the gain-augmented formulation improves the trade-off between tracking accuracy, steering smoothness, and lap time in high-dynamic scenarios while preserving the simplicity and computational efficiency of Pure Pursuit.
In the field of autonomous driving, a trade-off exists between the physical consistency of signals required for trajectory planning and the low-latency response demanded by tracking control. To address this problem, a Robust Adaptive Extended Kalman Filter method based on Limited Memory and Maximum Correntropy (LM-MC-AEKF) is proposed. First, a Finite Impulse Response (FIR) structure is constructed by employing a fixed-length sliding window mechanism, aiming to reduce phase lag and meet the requirements for real-time tracking. Second, the Maximum Correntropy Criterion (MCC) based on a Gaussian kernel is utilized to replace the traditional Minimum Mean Square Error (MMSE) criterion, adaptively suppressing non-Gaussian sensor outliers to provide statistically robust and physically feasible state inputs for the planning layer. Furthermore, an adaptive update architecture for noise covariance driven by MCC-weighted innovation is designed to compensate for model parameter mismatches online. Joint simulations based on CarSim and Simulink indicate that the proposed method reduces the longitudinal velocity estimation error by approximately 95% compared to traditional filters, and compresses the yaw rate phase lag from 30.5° to 3.8°. Simulation results indicate that this method yields improved data support for upper-level trajectory planning, while mitigating the risk of lower-level control instability caused by perception delays, thereby improving the overall robustness of the system within the simulation boundaries.
Bo Wu, Qiliang Sun, Leilei Hao et al.· Proceedings of the Instituti...· 0 citations
This paper presents a Koopman-operator-based optimal control framework for high-speed lateral path tracking and validates it through closed-loop experiments on a full-size autonomous electric vehicle. The path-tracking error dynamics are identified from real driving data using extended dynamic mode decomposition with control. The lifting dictionary combines the measured physical states with thin-plate-spline radial basis functions, while maximum-absolute scaling is used to improve numerical conditioning. Road curvature and longitudinal velocity are included as measurable exogenous inputs. Because steady-state autonomous highway driving provides limited dynamic excitation, and because artificial steering excitation is unsafe in public traffic, the identification dataset combines autonomous lane-centering recordings with open-loop excitation maneuvers performed by a human driver. Two optimal controllers are evaluated: an infinite-horizon linear quadratic regulator and an input-constrained model predictive controller with curvature-and-velocity feedforward. Both Koopman-based controllers are compared with analytical single-track-model baselines using a common weight-mapping scheme. In highway tests at 80 km/h, the Koopman-based model predictive controller reduces the lateral-position root-mean-square error by up to 43.7% relative to the analytical model predictive controller, while the Koopman-based linear quadratic regulator reduces it by up to 19.2% relative to its analytical counterpart. Most of the improvement is obtained with 20 radial basis functions, after which the tracking performance saturates. The largest tested Koopman configuration runs in under $20~\mu $ s on an automotive-grade embedded target, demonstrating improved tracking accuracy without sacrificing real-time feasibility.
Oğuzhan Tezgelen, N. Seymen, C. Kasnakoǧlu· IEEE Access· 0 citations
This paper presents a hybrid, constraint-aware tracking architecture for quadrotor UAVs that explicitly targets the failure mode arising from the
combination
of actuator saturation and loss-of-effectiveness (LOE) faults. While model-based controllers augmented with adaptive terms can compensate for matched uncertainties locally, their performance can still collapse when the commanded trajectory becomes infeasible under post-fault authority reduction, leading to prolonged saturation and rapidly growing tracking errors. To address this issue, an policy-based adaptive reference governor with optional offline parameter tuning is introduced that shapes the commanded reference in real time, without modifying the stabilizing control law. The governor is implemented as a bounded, rate-limited first-order reference filter whose channel-wise time constants and rate limits are generated by a compact parametric policy driven by interpretable features: output-error norm, output-rate proxy, saturation ratio, and a fault indicator. The policy parameters may optionally be refined offline via a toolbox-free CEM-like random search, yielding a lightweight and reproducible parameter-tuning procedure that is reproducible and safety-oriented by construction. Nonlinear simulations under two regimes–(A) nominal operation without saturation/faults and (B) simultaneous rotor-speed saturation with an LOE fault–demonstrate the effectiveness of policy-based adaptive reference shaping in improving practical feasibility under constrained operation. In nominal conditions, the proposed governor improves practical performance by substantially reducing peak control magnitude, control energy, and control-rate activity while maintaining bounded tracking. In the stress regime, baseline configurations exhibit sustained saturation and divergence, whereas the RL-inspired governor maintains bounded tracking, eliminates time spent at saturation, and reduces error and effort metrics by orders of magnitude. These results indicate that embedding a low-dimensional parametric policy at the reference level provides an effective and safety-compatible mechanism for fault- and constraint-aware quadrotor tracking.
Sobhan Toulabi, Seyyede Marzieh Mousavi, F. F. Saberi· Scientific Reports· 0 citations
Active safety control under extreme driving conditions, such as transient drifting, is crucial for autonomous vehicles. However, standard Nonlinear Model Predictive Control (NMPC) frequently encounters infeasibility when strictly enforcing hard constraints under highly nonlinear dynamics, and traditional hybrid switching strategies often induce transient instability. Targeting distributed-drive vehicles, this paper proposes a unified, mode-free optimal control framework for full-condition path tracking based on the iterative Linear Quadratic Regulator (iLQR). First, by incorporating the distribution characteristics of tire slips under combined conditions, a continuous mapping relationship between tire forces and slip velocities across arbitrary slip states is established. This unifies the control problem at the fundamental dynamics level, thereby completely eliminating the need for controller switching. Second, a hierarchical constraint management strategy is introduced: soft constraints via penalty functions are employed during the optimization phase to ensure recursive feasibility under sudden disturbances, followed by strict dynamic hard constraints at the output level to guarantee actuator safety. Finally, the proposed framework is validated through CarSim-Simulink co-simulations and Hardware-in-the-Loop (HIL) tests under various road adhesion coefficients. The results demonstrate that, compared to standard NMPC, the proposed iLQR framework achieves high-precision path tracking across all conditions with millisecond-level computational efficiency. Furthermore, the actuator saturation trigger rate under extreme conditions is kept below 10.2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10.2\%$$\end{document}, exhibiting outstanding actuator protection, dynamic smoothness, and high engineering practicality.
Zhanshuai Song, Linhe Ge, Wei Li et al.· Nonlinear dynamics· 0 citations
This paper bridges the conceptual gap between trajectory-based feedforward control, common in academic motion control literature, and the dynamic inversion approach used in practical open-source flight control firmwares like Ardupilot. While classical methods aim for zero tracking error by anticipating reference trajectories, practical implementations prioritize actuator smoothness and effort prediction. A comparative analysis is conducted on the z-axis of a simulated quadcopter across 162 scenarios, varying velocity and acceleration feedforward gains. Performance is evaluated using Integral Absolute Error (IAE), Integral of Squared Input (ISU), and Total Variation (TV). Results indicate that while the classical approach reduces position tracking error by up to ten times, it induces severe velocity overshoots (up to 36.1%) and extreme actuator chattering at high acceleration gains. The study concludes that aggressive feedforward tuning degrades physical performance, validating the stability-focused architecture adopted by available flight controllers.
This paper proposes a nonlinear and robust State-Dependent Riccati Equation (SDRE) combined with H∞ control architecture for brake- by-wire systems, specifically designed to handle severe tire-road friction variations and μ-split scenarios. The primary objective is to maximize deceleration capabilities while rigorously maintaining yaw stability, trajectory tracking, and passenger comfort through jerk limitation. Situated within the domain of active safety, this research addresses robustness against real-world uncertainties by utilizing a high-fidelity 14-degree-of-freedom vehicle model that accounts for longitudinal, lateral, and yaw dynamics, suspension-induced pitch and roll effects, and nonlinear tire behavior with explicit load transfer. To ensure near-optimal slip tracking under variable surface conditions, the system employs online friction estimation via Extended and Unscented Kalman Filters (EKF/UKF) fusing wheel and IMU data to adaptively adjust slip targets. The control strategy is bifurcated: the SDRE component manages dominant nonlinearities through state-dependent gains to prevent wheel lock-up, while the H∞ component provides robust disturbance rejection against parametric uncertainties such as mass variations and sensor noise. Control efforts are distributed via a Quadratic Programming (QP) torque allocator featuring anti-windup mechanisms and explicit saturation handling to compensate for lateral drift during μ-split braking. Validation is conducted through a Model- in-the-Loop (MIL) to Software-in-the-Loop (SIL) pipeline using scenarios including wet surfaces and panic braking. Simulation results demonstrate enhanced yaw stability and controlled deceleration profiles compared to conventional baselines, ensuring computational feasibility for automotive Electronic Control Units (ECUs).
X. Cubillos· SAE technical paper series· 0 citations