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2020
DOI: 10.1109/jas.2017.7510871
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Position control of a flexible manipulator using a new nonlinear self-tuning PID controller

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Cited by 47 publications
(35 citation statements)
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“…Moreover, model parameters may need tuning, even if model identification has been made. Different methods for tuning FLM controller gains include the Ziegler-Nichols method (Mohamed et al, 2016;Agrawal et al, 2020), LMI approach (Mohamed et al, 2016), dynamic particle swarm optimization method (Agrawal et al, 2020), self-tuning method using the artificial neural network (Njeri et al, 2019), self-tuning method based on nonlinear autoregressive moving average with exogenous-input (NARMAX) model of the FLM (Pradhan and Subudhi, 2020), soft computing based tuning method (Singh and Ohri, 2018), and selftuning method based on generalized minimum variance . Compared to the standard Ziegler-Nichols tuning method, the recent self-tuning methods have shown superior performance in the control of FLMs (Agrawal et al, 2020).…”
Section: Control Of Flmsmentioning
confidence: 99%
See 1 more Smart Citation
“…Moreover, model parameters may need tuning, even if model identification has been made. Different methods for tuning FLM controller gains include the Ziegler-Nichols method (Mohamed et al, 2016;Agrawal et al, 2020), LMI approach (Mohamed et al, 2016), dynamic particle swarm optimization method (Agrawal et al, 2020), self-tuning method using the artificial neural network (Njeri et al, 2019), self-tuning method based on nonlinear autoregressive moving average with exogenous-input (NARMAX) model of the FLM (Pradhan and Subudhi, 2020), soft computing based tuning method (Singh and Ohri, 2018), and selftuning method based on generalized minimum variance . Compared to the standard Ziegler-Nichols tuning method, the recent self-tuning methods have shown superior performance in the control of FLMs (Agrawal et al, 2020).…”
Section: Control Of Flmsmentioning
confidence: 99%
“…Singh and Ohri (2018) presented a comparative study of different nature-inspired soft computing based PID control tuning strategies, including genetic algorithm, ant colony optimization, and particle swarm op-timization for the position and vibration control of a single-link flexible manipulator. Pradhan and Subudhi (2020) proposed a nonlinear self-tuning PID controller to control the joint position and link deflection of the FLM subjected to varying payloads. Fareh et al (2020) presented robust active disturbance rejection control for FLM to solve joint trajectories tracking control problem and minimize the link's vibrations.…”
Section: Model-free Control Techniquesmentioning
confidence: 99%
“…However, it also ignores the problem that it will fall into the local optimal accuracy problem in later stage. Pradhan [17] put forward the nonlinear Autoregressive Moving Average (ARMA) algorithm to PID control to realize its parameter self-tuning, but the convergence speed of the ARMA itself needs to be improved. Morawski [18] proposed a data transfer method based on evolutionary game among network nodes.…”
Section: Related Workmentioning
confidence: 99%
“…Hence, it is necessary to truncate the higher-order flexible modes. Therefore, the dynamics of TLFM is derived by using the Euler-Lagrangian formulation technique along with the assumed mode method (AMM) [21]. In this work, it is assumed that motion of the TLFM is in the horizontal plane; the links have uniform material properties and have a constant cross-sectional area [22].…”
Section: Tlfm Dynamicsmentioning
confidence: 99%