Results show that the PINN and closed-loop experimental validations are consistent with the proposed method, and the PINN model is investigated experimentally to assess the closed-loop strategy and evaluate its efficiency and reproducibility in recognizing the mechanical system behavior.
Abstract
The demand for robotic manipulators has increased because of their precision, speed, and cost-efficiency in complex or hazardous tasks. Flexible robotic manipulators, unlike rigid ones, offer lower mass and energy consumption, enabling advanced applications across various fields. Despite these advantages, the mass reduction of these manipulators can lead to undesired effects, including decreased precision, increased sensitivity to parametric uncertainties, coupled dynamics, and increased oscillations caused by their inherent flexibility. Moreover, the modeling complexity of such mechanical systems represents a significant challenge, since multiple degrees of freedom must be considered. In this study, a physics-informed neural network (PINN) is designed to estimate the dynamic behavior of a flexible-link manipulator. First, a dataset is created by executing different trajectories (i.e., different rotation angles) of the flexible manipulator. Based on the dataset, the PINN is then trained using time and strain signals as inputs to estimate the angular displacement, combining a data-driven loss with a physics-based loss derived from the system’s dynamic model. Finally, the PINN model is investigated experimentally to assess the closed-loop strategy and evaluate its efficiency and reproducibility in recognizing the mechanical system behavior. Therefore, the results show that the PINN and closed-loop experimental validations are consistent with the proposed method.
This thesis investigates advanced modeling and control strategies for robotic manipulators, focusing on the DLR-HIT II robotic hand and the KUKA LBR iiwa. It presents three core contributions that integrate simulation, model-based control, and data-driven methods to improve torque and position control under uncertainties and disturbances. First, a dual-platform simulation framework is developed using MATLAB Simscape Multibody and CoppeliaSim. The system accurately models the DLR-HIT II hand’s kinematics and dynamics, enabling both control validation and realistic interaction with virtual environments. The use of Unified Robot Description Format (URDF)-based modeling sup-ports reusability and modular analysis. Second, a physics-informed neural network (PINN) is proposed for direct torque and position control. This method uses only time and joint position inputs, internally computes derivatives, and generalizes well across various trajectory types. It eliminates the need for separate feedback controllers and shows strong robustness under disturbances, while maintaining low computational cost. Third, a Spike-Aware Hybrid Torque Control (SA-HTC) architecture is enhanced with a feedforward neural network (NN) trained offline. The network learns to improve Computed Torque Control (CTC) for unmodeled effects, friction, and external forces by refining the CTC torque output in real time. Simulation results across diverse trajectories and noise levels demonstrate that the SA-HTC method significantly improves tracking accuracy and robustness compared to classical CTC. To support deployment on position-controlled hardware, a physics-informed torque-to-position interface is introduced and compared with virtual stiffness and admittance wrappers, yielding lower steady-state bias, reduced phase lag, lower tracking error, and robust cross-trajectory generalization. Thus, these contributions advance the integration of learning-based and model-based control strategies in robotics. The results highlight scalable and efficient methods for accurate trajectory tracking and torque control, offering practical potential for robotic manipulation and automation applications.
Robotic analysis uses mathematical modeling and digital techniques to represent and study mechanical, electrical, and computational systems. Combining these models with physical robots enhances their functions, and artificial intelligence adds intelligence, enabling robots to perform more complex tasks. Inverse dynamics is a key component, mapping joint positions, velocities, and accelerations to the torques needed for accurate motion. This study presents a unified framework that integrates inverse dynamics learning, adaptive control, multi objective optimization, stability verification, and sensorless torque estimation for a 6 DOF UR5 manipulator. A structured dataset representing robot motion with controlled noise and uncertainty conditions is used to train three learning models: Deep Neural Networks, Type 2 Fuzzy Systems, and Gaussian Process Regression. The results show that Gaussian Process Regression achieves the highest torque prediction accuracy, with RMSE of 7.87 Nm and R2 of 0.892, closely followed by the Deep Neural Network (RMSE 11.90 Nm, R2 0.753). Integration with an adaptive controller reduces the trajectory tracking error to 0.0473 rad, which is further reduced to 0.0238 rad using Particle Swarm Optimization, alongside a 5.41% reduction in control energy. Stability analysis using Lyapunov and frequency domain methods confirms bounded, stable closed-loop tracking behaviour, while sensorless torque estimation improves prediction accuracy by 70%. The proposed framework provides a clear methodological foundation for learning-based inverse dynamics modelling and its integration with robotic control systems, and future work will extend the framework to real-time implementation on physical robotic systems.
The autonomous robotic manipulators have become inevitable in the contemporary industrial automation, medical robotics, space exploration, and service robots. But it is an inherent challenge to have accurate, active and strong control of robotic arms under dynamic and uncertain conditions. Conventional control techniques utilizing only forward or inverse kinematics have drawbacks of singularities, local minima, sluggish convergence and lack of computational efficiency. In order to solve these problems, the current paper will cover a new autonomous robotic arm control system (ARCs) by relying on a Hybrid Kinematic Optimization(HKO) approach that combines analytical inverse kinematics, numerical optimization, intelligent constraint management. The suggested framework will integrate classical DenavitHartenberg (D-H) kinematic modeling with the use of the gradient-based and evolutionary optimization to produce the optimal joint trajectories in real-time. There is the introduction of a hybrid cost function that involves position accuracy, orientation error, joint smoothness, and energy efficiency. Collision avoidance and workspace constraints are also factored in the control architecture to be used to ensure safe and reliable operation. The hybrid optimizer is a dynamical algorithm that changes between fast analytical solvers and the global numerical optimizers based on the complexity of the task and the environmental conditions. Experiments involving simulation experiments on a 6-DOF model of industrial robotic arm on different task settings, such as pick-and-place settings, obstacle avoidance, and tracking in a trajectory are carried out. Convergence rate, tracking accuracy, joint torque efficiency and computational load are among the performance metrics considered and compared to the more traditional inverse kinematics and pure optimization-based methods. Findings indicate that the suggested hybrid structure attains a maximum speed of convergence is 35 percent, trajectory error is 28 percent and energy use is 22 percent. The Hybrid Kinematic Optimization framework presented provides a robust, scalable and intelligent framework applicable to solve the needs of next generation autonomous robotic manipulators to work within dynamic environments.
Hyeon Woo-Lee, Ken Seok-Park· International Journal of Int...· 0 citations
This work begins with dynamic modeling using the Euler-Lagrange formulation and demonstrates that the proposed hybrid controller outperforms traditional methods in terms of tracking accuracy, settling time, and disturbance rejection.
Anita Verma· International Journal of Int...· 0 citations
Addressing the inherent low stiffness of flexible manipulators, existing control schemes often face an intrinsic contradiction where rapid convergence leads to increased vibration amplitudes, making it challenging to achieve high-precision trajectory tracking while effectively suppressing elastic vibrations. To tackle this issue, this paper proposes a neural network-based Fixed-Time learning control strategy. This strategy is capable of simultaneously handling output constraints, model uncertainties, and input dead-zone nonlinearities of the system. The designed controller effectively compensates for the adverse effects of the input deadzone, ensuring that all system states converge to a small neighborhood around the origin within a fixed time, thereby significantly improving the system’s convergence speed and transient performance. By introducing a logarithmic Barrier Lyapunov Function (BLF), the prescribed tracking error constraints are strictly guaranteed. Furthermore, high-frequency chattering is mitigated through a smooth approximation of the sign function. Experimental results demonstrate that, compared with the PSF controller, the proposed Fixed-Time control scheme reduces the steady-state tracking errors by 61.9% and 69.2%, respectively. In terms of vibration suppression, the steady-state values of elastic vibrations are reduced by 49.2% and 32.6%, respectively. These results fully validate the superiority and robustness of the proposed control strategy in balancing rapid convergence with vibration suppression.
He-Jia Gao, Tan-Yu Chen, Jiang-Xu Liu et al.· CAAI Artificial Intelligence...· 0 citations
Reconfigurable Robot Manipulators (RRMs) have extensive applications in power grids, industrial 4.0 and flexible manufacturing, disaster rescue and exploration, as well as complex environments such as earthquakes and fires. This study examines the performance of reconfigurable robot manipulators operating in uncertain environments, addressing the challenge of mitigating interference noise in Harmonic Drive (HD) signal transmission. Furthermore, it presents a simplified robust Adaptive Dynamic Programming (ADP) framework to implement H∞ control for RRMs subject to unknown external disturbances. By representing the RRM dynamics as an integrated set of joint subsystem models, the corresponding control is formulated as zero-sum game, allowing a closed-form solution in robotic systems. The research results show that the ADP algorithm proposed in this paper is effective. Its contributions are primarily reflected in problem modeling, theoretical framework, and algorithmic implementation. The developed algorithm solves the HJI equation via a critic neural network, which facilitates direct adaptation of the H∞ control pair with assured convergence.
Qiao-Fan Shi, Xiao-Hong Zhu, Yu Zheng et al.· European Conference on Elect...· 0 citations
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