Modeling and Control of Robotic Manipulators Based on Artificial Neural Networks: A Review

Liu, Zhaobing; Peng, Kerui; Han, Lvpeng; Guan, Shengchuang

doi:10.1007/s40997-023-00596-3

Cited by 23 publications

(5 citation statements)

References 274 publications

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“…It means we want the moving platform to reach the desired reference pose. Plenty of control techniques have been developed for robotic manipulators, such as a nonlinear model and a multi-input/multi-output for multiple degrees of freedom robots [42]- [45]. However, in industry, controllers usually control individual joints through drivers linearly [46].…”

Section: Kinematics and Control Strategy Of Stewartmentioning

confidence: 99%

Addressing Challenges in Dynamic Modeling of Stewart Platform using Reinforcement Learning-Based Control Approach

Yadavari,

Aghaei,

İkizoğlu

2024

Journal of Robotics and Control

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In this paper, we focus on enhancing the performance of the controller utilized in the Stewart platform by investigating the dynamics of the platform. Dynamic modeling is crucial for control and simulation, yet challenging for parallel robots like the Stewart platform due to closed-loop kinematics. We explore classical methods to solve its inverse dynamical model, but conventional approaches face difficulties, often resulting in simplified and inaccurate models. To overcome this limitation, we propose a novel approach by replacing the classical feedforward inverse dynamic block with a reinforcement learning (RL) agent, which, to our knowledge, has not been tried yet in the context of the Stewart platform control. Our proposed methodology utilizes a hybrid control topology that combines RL with existing classical control topologies and inverse kinematic modeling. We leverage three deep reinforcement learning (DRL) algorithms and two model-based RL algorithms to achieve improved control performance, highlighting the versatility of the proposed approach. By incorporating the learned feedforward control topology into the existing PID controller, we demonstrate enhancements in the overall control performance of the Stewart platform. Notably, our approach eliminates the need for explicit derivation and solving of the inverse dynamic model, overcoming the drawbacks associated with inaccurate and simplified models. Through several simulations and experiments, we validate the effectiveness of our reinforcement learning-based control approach for the dynamic modeling of the Stewart platform. The results highlight the potential of RL techniques in overcoming the challenges associated with dynamic modeling in parallel robot systems, promising improved control performance. This enhances accuracy and reduces the development time of control algorithms in real-world applications. Nonetheless, it requires a simulation step before practical implementations.

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Section: Kinematics and Control Strategy Of Stewartmentioning

confidence: 99%

Addressing Challenges in Dynamic Modeling of Stewart Platform using Reinforcement Learning-Based Control Approach

Yadavari,

Aghaei,

İkizoğlu

2024

Journal of Robotics and Control

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“…However, in recent research, the integration of NNs with conventional controllers has garnered significant attention. This hybrid approach seeks to capitalize on the respective strengths of both methodologies: combining the interpretability and stability of conventional controllers, such as PID and sliding mode approaches, with the adaptability and learning capabilities of NNs [24]. This integration holds promise for enhancing control system performance, particularly in scenarios characterized by uncertainties and nonlinearities.…”

Section: State Of the Artmentioning

confidence: 99%

Indirect Adaptive Control Using Neural Network and Discrete Extended Kalman Filter for Wheeled Mobile Robot

Silaa,

Bencherif,

Barambones

2024

Actuators

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This paper presents a novel approach to address the challenges associated with the trajectory tracking control of wheeled mobile robots (WMRs). The proposed control approach is based on an indirect adaptive control PID using a neural network and discrete extended Kalman filter (IAPIDNN-DEKF). The proposed IAPIDNN-DEKF scheme uses the NN to identify the system Jacobian, which is used for tuning the PID gains using the stochastic gradient descent algorithm (SGD). The DEKF is proposed for state estimation (localization), and the NN adaptation improves the tracking error performance. By augmenting the state vector, the NN captures higher-order dynamics, enabling more accurate estimations, which improves trajectory tracking. Simulation studies in which a WMR is used in different scenarios are conducted to evaluate the effectiveness of the IAPIDNN-DEKF control. In order to demonstrate the effectiveness of the IAPIDNN-DEKF control, its performance is compared with direct adaptive NN (DA-NN) control, backstepping control (BSC) and an adaptive PID. On lemniscate, IAPIDNN-DEKF achieves RMSE values of 0.078769, 0.12086 and 0.1672. On sinusoidal trajectories, the method yields RMSE values of 0.01233, 0.015138 and 0.088707, and on sinusoidal with perturbation, RMSE values are 0.021495, 0.016504 and 0.090142 in x, y and θ, respectively. These results demonstrate the superior performance of IAPIDNN-DEKF for achieving accurate control and state estimation. The proposed IAPIDNN-DEKF offers advantages in terms of accurate estimation, adaptability to dynamic environments and computational efficiency. This research contributes to the advancement of robust control techniques for WMRs and showcases the potential of IAPIDNN-DEKF to enhance trajectory tracking and state estimation capabilities in real-world applications.

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“…Adaptive control is a valuable algorithm for handling structured uncertainties [30][31][32][33]; however, it is not suitable for handling unstructured uncertainties. Neural network (NN) control with learning capability and good quality in an approximation of nonlinear function is a useful selection for modeling complicated processes and compensating for unstructured uncertainties [34][35][36]. Wu et al [37] presented a tracking control method leveraging RBFNN with the aim of minimizing the tracking error in nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

Tran,

Nguyen,

et al. 2024

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In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method.

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Modeling and Control of Robotic Manipulators Based on Artificial Neural Networks: A Review

Cited by 23 publications

References 274 publications

Addressing Challenges in Dynamic Modeling of Stewart Platform using Reinforcement Learning-Based Control Approach

Addressing Challenges in Dynamic Modeling of Stewart Platform using Reinforcement Learning-Based Control Approach

Indirect Adaptive Control Using Neural Network and Discrete Extended Kalman Filter for Wheeled Mobile Robot

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

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