“…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.…”
This paper presents a review of dynamic modeling techniques and various control schemes to control flexible link manipulators (FLMs) that were studied in recent literature. The advantages and complexities associated with the FLMs are discussed briefly. A survey of the reported studies is carried out based on the method used for modeling link flexibility and obtaining equations of motion of the FLMs. The control techniques are reviewed by classifying them into two main categories: model-based and model-free control schemes. The merits and limitations of different modeling and control methods are highlighted.
“…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.…”
This paper presents a review of dynamic modeling techniques and various control schemes to control flexible link manipulators (FLMs) that were studied in recent literature. The advantages and complexities associated with the FLMs are discussed briefly. A survey of the reported studies is carried out based on the method used for modeling link flexibility and obtaining equations of motion of the FLMs. The control techniques are reviewed by classifying them into two main categories: model-based and model-free control schemes. The merits and limitations of different modeling and control methods are highlighted.
“…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.…”
Wireless Sensor Networks (WSNs) consist of multiple sensor nodes, each of which has the ability to collect, receive and send data. However, irregular data sources can lead to severe network congestion. To solve this problem, the Proportional Integral Derivative (PID) controller is introduced into the congestion control mechanism to control the queue length of messages in nodes. By running the PID algorithm on cluster head nodes, the effective collection of sensor data is realized. In addition, a fuzzy control algorithm is proposed to solve the problems of slow parameter optimization, limited adaptive ability and poor optimization precision of traditional PID controller. However, the parameter selection of the fuzzy control algorithm relies too much on expert experience and has certain limitations. Therefore, this manuscript proposes the Cuckoo Fuzzy-PID Controller (CFPID), whose core idea is to apply the cuckoo search algorithm to optimize the fuzzy PID controller’s quantization factor and PID parameter increment. Simulation results show that in comparison with the existing methods, the instantaneous queue length and real-time packet loss rate of CFPID are better.
“…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].…”
Owing to the non-collocation of actuators and sensors in a flexible-link manipulator (FLM) it becomes difficult to achieve accurate tip position tracking. To resolve this issue, a vision sensor is used for direct measurement of the tip position instead of employing the traditional mechanical sensors. Among the different visual servoing (VS) control schemes, imagebased VS (IBVS) is more effective. However, there are many challenges in the IBVS scheme such as singularities in the interaction matrix and local minima in trajectories that affect the system performance in real-time applications. In this study, the moment-based new visual feature is selected to address the aforesaid issues that arise in the IBVS scheme. Furthermore, a new two-time scale IBVS controller is developed for addressing the tip-tracking control problem of the two-link flexible manipulator (TLFM). In the proposed control scheme, the dynamics of the FLM is decomposed into two-time scale models, namely a slow subsystem and a fast subsystem. The performance and robustness of the proposed new two-time scale IBVS controller for TLFM are verified by pursuing simulation studies. It is observed from the obtained results that the proposed controller effectively stabilises the oscillatory dynamics and tracks the reference trajectory accurately.
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