arXiv subjects

Naeim Ebrahimi Toulkani

Publications and source records attributed to Naeim Ebrahimi Toulkani.

3 recordsLinked to original sources

arXiv2025

Minimally Conservative Controlled-Invariant Set Synthesis Using Control Barrier Certificates

Finding a controlled-invariant set for a system with state and control constraints is crucial for safety-critical applications. However, existing methods often produce overly conservative solutions. This paper presents a method for generating controlled-invariant (safe) sets for nonlinear polynomial control-affine systems using Control Barrier Certificates (CBCs). We formulate CBC conditions as Sum-of-Squares (SOS) constraints and solve them via an SOS Program (SOSP). First, we generalize existing SOSPs for CBC synthesis to handle environments with complex unsafe state representations. Then, we propose an iterative algorithm that progressively enlarges the safe set constructed by the synthesized CBCs by maximizing boundary expansion at each iteration. We theoretically prove that our method guarantees strict safe set expansion at every step. Finally, we validate our approach with numerical simulations in 2D and 3D for single-input and multi-input systems. Empirical results show that the safe set generated by our method covers in most part a larger portion of the state space compared to two state-of-the-art techniques.

eess.SY↗

arXiv2025

Safety Filter Design for Articulated Frame Steering Vehicles In the Presence of Actuator Dynamics Using High-Order Control Barrier Functions

Articulated Frame Steering (AFS) vehicles are widely used in heavy-duty industries, where they often operate near operators and laborers. Therefore, designing safe controllers for AFS vehicles is essential. In this paper, we develop a Quadratic Program (QP)-based safety filter that ensures feasibility for AFS vehicles with affine actuator dynamics. To achieve this, we first derive the general equations of motion for AFS vehicles, incorporating affine actuator dynamics. We then introduce a novel High-Order Control Barrier Function (HOCBF) candidate with equal relative degrees for both system controls. Finally, we design a Parametric Adaptive HOCBF (PACBF) and an always-feasible, QP-based safety filter. Numerical simulations of AFS vehicle kinematics demonstrate the effectiveness of our approach.

eess.SY↗

arXiv2025

Autonomous Wheel Loader Navigation Using Goal-Conditioned Actor-Critic MPC

This paper proposes a novel control method for an autonomous wheel loader, enabling time-efficient navigation to an arbitrary goal pose. Unlike prior works which combine high-level trajectory planners with Model Predictive Control (MPC), we directly enhance the planning capabilities of MPC by incorporating a cost function derived from Actor-Critic Reinforcement Learning (RL). Specifically, we first train an RL agent to solve the pose reaching task in simulation, then transfer the learned planning knowledge to an MPC by incorporating the trained neural network critic as both the stage and terminal cost. We show through comprehensive simulations that the resulting MPC inherits the time-efficient behavior of the RL agent, generating trajectories that compare favorably against those found using trajectory optimization. We also deploy our method on a real-world wheel loader, where we demonstrate successful navigation in various scenarios.

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