Non‐linear optimal control for multi‐DOF electro‐hydraulic robotic manipulators

Rigatos, Gerasimos; Zervos, Nikolaos; Abbaszadeh, Masoud; Pomares, Jorge; Wira, Patrice

doi:10.1049/iet-csr.2020.0003

Cited by 9 publications

(9 citation statements)

References 34 publications

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“…In the early days, some scholars used the linear control method of state feedback linearization and decomposition of control rate. However, the linear control methods need to establish a linearized mathematical model, and the better choice is to use some nonlinear schemes of control, such as sliding mode variable structure control(SMVC), adaptive method, backstepping technique, Fuzzy logic control(FLC), neural network control(NNC), etc [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive RBF Neural Network Backstepping Control for Two-Link Robot Manipulators

Wang

2022

J. Phys.: Conf. Ser.

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In this paper, an adaptive radial basis function neural network(RBFNN) backstepping controller is presented for a two-link robot manipulator in the presence of uncertainty and external interferences. RBFNNs are applied to approximate uncertain nonlinear functions, and considering the backstepping technique, an adaptive RBFNN backstepping control strategy is proposed. It is proved by the Lyapunov function that tracking errors converge to a small neighborhood of the equilibrium point and the closed-loop system variables are bounded. The simulation results demonstrate the effectiveness of the presented design method.

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Section: Introductionmentioning

confidence: 99%

Adaptive RBF Neural Network Backstepping Control for Two-Link Robot Manipulators

Wang

2022

J. Phys.: Conf. Ser.

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“…Typical applications of EHSs include active suspensions, robotics, load simulators, and machine tolls. [1][2][3][4][5] However, the high-performance position tracking control of EHS is a challenging task due to the presence of unknown external disturbance and high nonlinear dynamics that caused by the servo valve flow-pressure characteristic, friction, and oil temperature variation. Moreover, the parameter uncertainties, (e.g., internal leakage and load mass variation) and unmolded dynamics will also degrade the control performance in practical applications.…”

Section: Introductionmentioning

confidence: 99%

“…12,32,33 However, the integral action may cause some side effects to the control systems, such as long settling time and large overshoot. 34 Another commonly used approach for unmatched disturbance attenuation is the disturbance observer (DOB)-based control; see previous studies 4,34,35 for details. Extended state observer (ESO) is a special kind of observer that can simultaneously estimate the unmeasured states and uncertainties (including external disturbance) using only the output signal of the controlled system.…”

Section: Introductionmentioning

confidence: 99%

“…Electro‐hydraulic systems (EHSs) are widely used in modern industries, especially in heavy industries where high actuating forces/torques and accurate positioning are needed, because of their small size‐to‐power ratio, fast response, high reliability, and superior load efficiency compared to motor drive systems. Typical applications of EHSs include active suspensions, robotics, load simulators, and machine tolls 1–5 . However, the high‐performance position tracking control of EHS is a challenging task due to the presence of unknown external disturbance and high nonlinear dynamics that caused by the servo valve flow‐pressure characteristic, friction, and oil temperature variation.…”

Section: Introductionmentioning

confidence: 99%

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Extended state observer‐based finite time control of electro‐hydraulic system via sliding mode technique

Zou

2021

Asian Journal of Control

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The position tracking control of electro‐hydraulic system (EHS) is disposed in this paper via sliding mode control (SMC) with nonlinear sliding surface. The main feature of the proposed controller is the combination of a totally singular‐free recursive terminal sliding mode controller (RTSMC) with an extended state observer (ESO). The uncertainties (including the unknown external disturbance) together with its derivatives and the unmeasured states of EHS are estimated simultaneously by the ESO while the controller is designed by employing the recursive nonsingular TSMC (RNTSMC), and a nonlinear sliding surface is employed to totally avoid the singularity in the traditional RNTSMC. Moreover, the observer dynamics are considered during the controller design. The position tracking error is proven to be bounded in finite time via Lyapunov theory, and the bound can be effectively reduced by tuning the parameters of the controller and observer. Simulation results show that the proposed controller is not sensitive to model uncertainties and external disturbances.

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“…In [10], to deal with the nonlinear control system with disturbances and input uncertainties, a novel disturbance observer has been designed and a stable control law has been proposed. In [11], the authors proposed an optimal H∞ tracking control method for nonlinear multivariable dynamic systems. In [12], a gap measurement method is introduced to design a multi-model stable controller for a class of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

SODO Based Reinforcement Learning Anti-Disturbance Fault Toler-Ant Control for a Class of Nonlinear Uncertain Systems With Matched and Mismatched Disturbances

et al. 2021

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This paper proposes a reinforcement learning anti-disturbance fault tolerant control structure for a class of nonlinear uncertain systems with time varying matched and mismatched disturbances. To deal with the time varying matched and mismatched disturbances, two second order disturbance observers (SODOs) are designed for the inner and outer loop dynamic equations. For the purpose of enhancing the robustness and adaptivity with respect to the system uncertainties, two long short-term memory (LSTM) networks those possesses perfect fitting ability, have been introduced as the critic and actor networks. Moreover, to overcome the difficulty caused by the unknown perturbations of the control effectiveness, several fault tolerant adaptive laws have been designed. Consequently, a novel reinforcement learning anti-disturbance fault tolerant control structure has been established for the concerned disturbed nonlinear uncertain system. Two numerical examples are provided finally, demonstrating the satisfactory performance of the proposed control structure.

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Non‐linear optimal control for multi‐DOF electro‐hydraulic robotic manipulators

Cited by 9 publications

References 34 publications

Adaptive RBF Neural Network Backstepping Control for Two-Link Robot Manipulators

Adaptive RBF Neural Network Backstepping Control for Two-Link Robot Manipulators

Extended state observer‐based finite time control of electro‐hydraulic system via sliding mode technique

SODO Based Reinforcement Learning Anti-Disturbance Fault Toler-Ant Control for a Class of Nonlinear Uncertain Systems With Matched and Mismatched Disturbances

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