arXiv subjects

Amir Hossein Barjini

Publications and source records attributed to Amir Hossein Barjini.

5 recordsLinked to original sources

arXiv2026

Adaptive Modular Geometric Control of Robotic Manipulators

This paper develops an adaptive modular geometric control framework for robotic manipulators with uncertain inertial parameters. The manipulator is decomposed into rigid-body and joint modules, where each rigid-body module is represented by an Euler-Poincaré-type spatial dynamics on the Lie algebra se(3), and configuration errors are defined intrinsically through logarithmic maps on SE(3). The joint modules impose local screw constraints that relate adjacent body twists, accelerations, and transmitted wrenches, yielding a recursive propagation structure for the interconnected multibody system. Within this formulation, local geometric control laws are constructed at the module level, while the interconnection among modules is characterized by power-conjugate twist--wrench pairs induced by the natural duality pairing between the Lie algebra se(3) and its dual space se(3)^*. For the nominal case, exponential tracking stability of the interconnected system is established using local configuration energy functions on SE(3) and the power-preserving structure of the modular interconnection. To address inertial parametric uncertainty, a geometric adaptation law is introduced on the manifold of symmetric positive-definite matrices, ensuring physically consistent parameter estimates while retaining compatibility with the Lie-algebraic control formulation. Under the adaptive controller, semi-global uniform ultimate boundedness of the closed-loop tracking and parameter estimation errors is proven. Numerical simulations on a redundant high-inertia robotic manipulator demonstrate accurate pose tracking, smooth transient behavior, orientation regulation, and robustness under inertial perturbations. Comparative studies with state-of-the-art methods further illustrate the effectiveness of the proposed framework for complex robotic manipulation tasks.

eess.SY↗

arXiv2025

AI-Enhanced Kinematic Modeling of Flexible Manipulators Using Multi-IMU Sensor Fusion

This paper presents a novel framework for estimating the position and orientation of flexible manipulators undergoing vertical motion using multiple inertial measurement units (IMUs), optimized and calibrated with ground truth data. The flexible links are modeled as a series of rigid segments, with joint angles estimated from accelerometer and gyroscope measurements acquired by cost-effective IMUs. A complementary filter is employed to fuse the measurements, with its parameters optimized through particle swarm optimization (PSO) to mitigate noise and delay. To further improve estimation accuracy, residual errors in position and orientation are compensated using radial basis function neural networks (RBFNN). Experimental results validate the effectiveness of the proposed intelligent multi-IMU kinematic estimation method, achieving root mean square errors (RMSE) of 0.00021~m, 0.00041~m, and 0.00024~rad for $y$, $z$, and $θ$, respectively.

cs.RO↗

arXiv2025

All-Electric Heavy-Duty Robotic Manipulator: Actuator Configuration Optimization and Sensorless Control

This paper presents a unified framework that integrates modeling, optimization, and sensorless control of an all-electric heavy-duty robotic manipulator (HDRM) driven by electromechanical linear actuators (EMLAs). An EMLA model is formulated to capture motor electromechanics and direction-dependent transmission efficiencies, while a mathematical model of the HDRM, incorporating both kinematics and dynamics, is established to generate joint-space motion profiles for prescribed TCP trajectories. A safety-ensured trajectory generator, tailored to this model, maps Cartesian goals to joint space while enforcing joint-limit and velocity margins. Based on the resulting force and velocity demands, a multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to select the optimal EMLA configuration. To accelerate this optimization, a deep neural network, trained with EMLA parameters, is embedded in the optimization process to predict steady-state actuator efficiency from trajectory profiles. For the chosen EMLA design, a physics-informed Kriging surrogate, anchored to the analytic model and refined with experimental data, learns residuals of EMLA outputs to support force and velocity sensorless control. The actuator model is further embedded in a hierarchical virtual decomposition control (VDC) framework that outputs voltage commands. Experimental validation on a one-degree-of-freedom EMLA testbed confirms accurate trajectory tracking and effective sensorless control under varying loads.

eess.SY↗

arXiv2025

Surrogate-Enhanced Modeling and Adaptive Modular Control of All-Electric Heavy-Duty Robotic Manipulators

This paper presents a unified system-level modeling and control framework for an all-electric heavy-duty robotic manipulator (HDRM) driven by electromechanical linear actuators (EMLAs). A surrogate-enhanced actuator model, combining integrated electromechanical dynamics with a neural network trained on a dedicated testbed, is integrated into an extended virtual decomposition control (VDC) architecture augmented by a natural adaptation law. The derived analytical HDRM model supports a hierarchical control structure that seamlessly maps high-level force and velocity objectives to real-time actuator commands, accompanied by a Lyapunov-based stability proof. In multi-domain simulations of both cubic and a custom planar triangular trajectory, the proposed adaptive modular controller achieves sub-centimeter Cartesian tracking accuracy. Experimental validation of the same 1-DoF platform under realistic load emulation confirms the efficacy of the proposed control strategy. These findings demonstrate that a surrogate-enhanced EMLA model embedded in the VDC approach can enable modular, real-time control of an all-electric HDRM, supporting its deployment in next-generation mobile working machines.

cs.RO↗

arXiv2025

Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators

This article presents a motion planning and control framework for flexible robotic manipulators, integrating deep reinforcement learning (DRL) with a nonlinear partial differential equation (PDE) controller. Unlike conventional approaches that focus solely on control, we demonstrate that the desired trajectory significantly influences endpoint vibrations. To address this, a DRL motion planner, trained using the soft actor-critic (SAC) algorithm, generates optimized trajectories that inherently minimize vibrations. The PDE nonlinear controller then computes the required torques to track the planned trajectory while ensuring closed-loop stability using Lyapunov analysis. The proposed methodology is validated through both simulations and real-world experiments, demonstrating superior vibration suppression and tracking accuracy compared to traditional methods. The results underscore the potential of combining learning-based motion planning with model-based control for enhancing the precision and stability of flexible robotic manipulators.

cs.RO↗