Input-Output Feedback Linearization for the Control of a 4 Cable-Driven Parallel Robot

Kumar, Atal Anil; Antoine, Jean-François; Abba, Gabriel

doi:10.1016/j.ifacol.2019.11.154

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Determining the inverse dynamics is a tough task. In addition, following various simplifications, it becomes necessary to once again linearize this model around a designated equilibrium point for practical applications [51], [52]. Yet, this process is task-specific, and we need to do all steps in case new changes are made to the design and structure of the platform.…”

Section: Feed Forward Control Via Reinforcement Learningmentioning

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: Feed Forward Control Via Reinforcement Learningmentioning

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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“…Input−output feedback linearization technology provides convenience for further design of linearization control algorithm by transforming the nonlinear system model (in whole or part) into a linear system. 26,27 The standard input and output feedback linearization technology is described as follows.…”

Section: Control-oriented Nonlinear Temperature Gradient Modelmentioning

confidence: 99%

“…Feedback linearization is conveniently classified into categories, that is, input–output feedback linearization and state-space linearization. Input–output feedback linearization technology provides convenience for further design of linearization control algorithm by transforming the nonlinear system model (in whole or part) into a linear system. , The standard input and output feedback linearization technology is described as follows.…”

Section: Temperature Gradient Control Of the Sofcmentioning

confidence: 99%

Temperature Gradient Control of the Solid Oxide Fuel Cell under Variable Load

Huo

Cui

et al. 2021

ACS Omega

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Nowadays, the temperature gradient is considered as one of the most important parameters which impact the performance of the solid oxide fuel cell (SOFC). In this paper, a control strategy based on an input−output feedback linearization technology is derived for controlling the maximum temperature gradient within the anode fuel flow channel at the desired value. For the controller design, the temperature dynamic model is proposed and simplified to a controloriented multi-input and multioutput nonlinear dynamic model. Then, this paper presents an input−output feedback linearization controller to realize the control objective by adjusting the cathode input air flow. Finally, the simulation results are given to demonstrate the accuracy of the proposed model in reflecting the temperature dynamic characteristics. Moreover, the compound controller is added for simulation as a comparison, which shows that the proposed controller is equipped with better effectiveness and efficiency in the presence of external disturbances.

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“…Xie [27] proposed a robust synchronous control strategy, which can achieve high-precision trajectory tracking, but currently only focuses on the control of CDPRs with three degrees of freedom (3-DOF). Kumar [28] used an input-output feedback linearization method to control the CDPRs with good control effects, but currently concentrated on under-actuation robots. KINO H [29] proposed a robust PD control algorithm based on an uncertain Jacobi matrix and proved the stability of the system using Lyapunov theory, but this method is only applicable in zero gravity.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Motion Control Strategy of Cable-Driven Cleaning Robot for Ship Cargo Hold

Han

Chen

et al. 2023

JMSE

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Ship cargo-hold cleaning is a low-efficiency and high-risk operation in marine industry, which is generally carried out manually, putting the workers in danger. To improve the efficiency and safety of ship cargo-hold cleaning, a C-DCR is proposed in this article. Most research on the dynamics and control of CDPRs has focused on the scenarios with fixed bases; however, the effect of moving-base excitation on the end-effector is largely ignored. In this article, the dynamic model is established based on Lagrange method considering the ship motion and external disturbance, in which the motor model is considered. On this basis, for the high-speed maneuverability of the C-DCR, a modified PD feedforward tracking controller was proposed. Furthermore, the stability of the controller was proved with the Lyapunov Stability Theory. To keep the cable in tension at all times, the tensions are optimized based on the minimum 2-norm method. The simulation results show that the error mean of position is 0.22 m and the angular error mean is 2.8° under ship motion and external disturbance, indicating that the C-DCR has stable, smooth and bounded tracking performance, which will ensure the accuracy of the cleaning operation.

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Input-Output Feedback Linearization for the Control of a 4 Cable-Driven Parallel Robot

Cited by 9 publications

References 7 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

Temperature Gradient Control of the Solid Oxide Fuel Cell under Variable Load

Dynamic Modeling and Motion Control Strategy of Cable-Driven Cleaning Robot for Ship Cargo Hold

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