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Based on the rigid dynamics as well as applied nonlinear theory, an adaptive nonlinear controller of a single hydraulic cylinder's test stand is developed and realized, which compensate in real time for nonlinear force produced by joints when mechanism rapidly moving in high speed. First, model of kinematics and dynamics of the test stand are built, meanwhile nonlinear Controller is designed by using...
In this study, four adaptive neural network based fuzzy logic controllers (ANNFL) are designed and used as two controllers in terms of interval type-2 fuzzy logic control. The new controllers are called as adaptive neural network based interval type-2 fuzzy logic controller (ANNIT2FL) and applied to a rigid-flexible robot manipulator. Initially dynamic model of the manipulator is obtained by using...
In this paper, a decentralized adaptive fuzzy nonlinear observer is proposed for fault detection of reconfigurable manipulators. Estimation error is obtained from measured and observed velocity is used as the residual vector, the system is fault-free operation when residual vector is smaller than the selected threshold value. A fault is declared if the residual vector is greater than or equal to threshold,...
This paper describes a hybrid approach to the problem of controlling flexible link manipulators in the face of both structured and unstructured uncertainties. First, a nonlinear controller based on the equations of motion of the robot is elaborated. Its aim is to produce a stable control. Then, an adaptive RBF neural controller is implemented to compensate structured and unstructured uncertainties...
Robust and adaptive controller of the uncertain mobile manipulator with holonomic and non-holonomic constrains is designed. Considering some problems in model reduction by implicit function theorem when the mobile manipulator subjects to holonomic and non-holonomic constrains, an uniform expression of holonomic and non-holonomic constrains is introduced, and then based on the reduced model, a steady...
Ignoring actuator dynamics in control of rigid manipulators can in practice result in performance degradation or loss of system stability. However, consideration of actuator dynamics usually requires measurement of robot joint torques. This paper addresses motion tracking control of an n-DOF rigid robot by taking into account its actuator dynamics. Joint torque measurement is avoided by using an adaptive...
This paper describes a hybrid approach to the problem of controlling flexible link manipulators in the face of both structured and unstructured uncertainties. First, a nonlinear controller based on the equations of motion of the robot is elaborated. Its aim is to produce a stable control. Then, an adaptive CMAC neural controller is implemented to compensate structured and unstructured uncertainties...
This paper proposes a novel adaptive robust PID controller to deal with the trajectory tracking of robot manipulators with nonlinear uncertainties and disturbances. Based on the classical PID controller, an adaptive PID controller is designed to deal with the nonlinear uncertainties of the system. The adaptation mechanism which is motivated from the sliding mode control derived for tuning three PID...
Adaptive control problem of nonlinear systems having dynamics in parametric strict feedback form is addressed here. Effort is made to derive adaptive methodology for controller design in contraction framework. General results and conditions for stabilization are derived using backstepping. At each step of recursive design, system is made contracting by suitable selection of control inputs. As contraction...
In this paper, an adaptive control of a parallel robot is proposed for trajectory tracking problems. This approach is based on adaptive multi-layer perceptron (MLP) neural network and sliding mode technique. The aim of this study is to design a robust controller with respect to external disturbances in order to improve the trajectory tracking. In fact, an adaptive MLP neural network is developed to...
In this paper, a new iterative learning algorithm is proposed for repetitive nonlinear systems. The control system employs a combination of state feedback and iterative learning control (ILC) in which the coefficients of states are learned similar to ILC methods. The control system is in a closed loop format both in iteration domain (because of ILC) and in time domain (because of feedback control)...
In this paper, to cope with parametric uncertainties in the nonlinear dynamical model of a flexible robot arm a Lyapunov based adaptive nonlinear output feedback control strategy directly based on tip position and strain gauge measurements is investigated. Effectiveness and performance of the controller in vibration suppressing and fast motion duration tracking capability is shown through simulation.
The dynamic behavior of electro-hydraulic driven parallel manipulators is highly nonlinear system, the nonlinear behavior arising from load friction as well as the valve flow-pressure drop relationship. This paper is concerned with the robust tracking control of electro-hydraulic driven parallel manipulators with the model uncertainties. An adaptive fuzzy controller is used to estimate the uncertainties...
This paper presents a neural-adaptive sliding mode control for the tracking control of 4-SPS(PS) type parallel manipulator. The neural-adaptive controller is introduced to modify the coefficients of sliding manifold in sliding control strategy, which solve the problem that the equivalent control can not be obtained accurately because of the uncertain and fixed coefficients of sliding manifold and...
A novel second-order update law proposed in this paper, whose coefficients are decided by a linear matrix differential equation with the regressors as input, achieves uniform asymptotic stability parameters convergence as well as motion tracking error of robot manipulators, if the persistency of excitation condition is satisfied. This algorithm is simpler for the execution. Based on this adaptive...
In this paper, two kinds of controllers are proposed, which are adaptive fuzzy sliding mode control and advanced adaptive fuzzy sliding mode control. These approaches use an adaptive fuzzy system to approximate the system whose model has uncertainty. The sliding mode control part is used to guarantee the stability of the system and resistant the disturbance in the control channel. Further more, advanced...
This paper proposes an adaptive control method for robotic manipulators with input toque uncertainties. For each link of manipulators, it is assumed that the input torque uncertainties can be divided into unknown parameters term and bounded disturbance term. In addition, all the parameters for input torque uncertainties and robot are unknown. The proposed method ensures that the unknown parameters...
A new remote time-delay feedback controller is presented for a class of robot manipulator systems with unknown nonlinear dynamics and communication time-delay. The proposed control scheme consists of a local neural network (NN) compensation and a time-delay feedback controller. A NN-based identification is first employed to identify the robot manipulator system. Local linearization compensation is...
This study focuses on the development of an adaptive fuzzy-neural-network control (AFNNC) scheme for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order...
A novel robust Hinfin decentralized intelligent control (RHDIC) strategy is proposed for the trajectory following problem of robot manipulators. The proposed system is comprised of a computed torque controller and neural robust controller with new learning algorithm. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee stability of closed-loop systems and satisfactory...
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