This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) system constraints designed. In design, adaptive NNs are adopted to handle uncertainties disturbances. Moore-Penrose inverse term employed in order prevent violation of A barrier Lyapunov function used guarantee uniform ultimate boundedness closed-loop performance...

In this paper, adaptive impedance control is developed for an n-link robotic manipulator with input saturation by employing neural networks. Both uncertainties and are considered in the tracking design. order to approximate system uncertainties, we introduce a radial basis function network controller, handled designing auxiliary system. By using Lyapunov's method, design controllers. state output feedbacks constructed. To verify proposed control, extensive simulations conducted.

This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and uncertain compliant environment with which robot comes into contact. A NN algorithm is developed identify plant model. The prominent feature that there no need get prior knowledge about uncertainty sufficient amount observed data. Also, introduced tackle interaction between its environment, so follows...

In this paper, a flexible cable with payload attached at the bottom is considered to be model of crane system used for positioning payload. The dynamics coupled tip contribute hybrid represented by partial-ordinary differential equations. An integral-barrier Lyapunov function (IBLF)-based control proposed suppress undesirable vibrations boundary output constraint. Adaption laws are developed handling parametric uncertainties. A novel IBLF adopted guarantee uniform stability closed-loop...

In this paper, we consider the trajectory tracking of a marine surface vessel in presence output constraints and uncertainties. An asymmetric barrier Lyapunov function is employed to cope with constraints. To handle system uncertainties, apply adaptive neural networks approximate unknown model parameters vessel. Both full state feedback control are proposed paper. The law designed by using Moore-Penrose pseudoinverse case that all states known, high-gain observer. Under method controller...

For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few literature are concerned with convergence speed. In this paper, we developed a control/identification scheme to identify unknown kinematic and dynamic parameters enhanced rate. Superior traditional methods, information error was properly integrated into proposed identification algorithm, such that performance achieved. Besides, Newton-Euler (NE) method used build model, where...

Robots with coordinated dual arms are able to perform more complicated tasks that a single manipulator could hardly achieve. However, rigorous motion precision is required guarantee effective cooperation between the arms, especially when they grasp common object. In this case, internal forces applied on object must also be considered in addition external forces. Therefore, prescribed tracking performance at both transient and steady states first specified, then, controller synthesized...

In this paper, a neural network (NN) controller is designed to suppress the vibration of flexible robotic manipulator system with input deadzone. The NN aims approximate unknown dynamics and eliminate effects deadzone in actuators. order describe more accurately, model constructed based on lumping spring-mass method. Full state feedback control proposed first output high-gain observer then devised make scheme practical. effect approximated by radial basis function (RBFNN) another RBFNN. able...

In this paper, we present adaptive neural network tracking control of a robotic manipulator with input deadzone and output constraint. A barrier Lyapunov function is employed to deal the constraints. Adaptive networks are used approximate unknown model manipulator. Both full state feedback considered in paper. For control, high gain observer estimate unmeasurable states. With proposed constraints not violated, all signals closed loop system semi-globally uniformly bounded. The performance...

The research of this paper works out the attitude and position control flapping wing micro aerial vehicle (FWMAV). Neural network with full state output feedback are designed to deal uncertainties in complex nonlinear FWMAV dynamic system enhance robustness. Meanwhile, we design disturbance observers which exerted into via feedforward loops counteract bad influence disturbances. Then, a Lyapunov function is proposed prove closed-loop stability semi-global uniform ultimate boundedness all...

In this paper, we present the vibration control design for a Euler-Bernoulli beam with boundary output constraint. To prevent constraint violation, novel barrier Lyapunov function is employed and stability analysis. This paper represents an important step in extending theory to distributed parameter systems. Model-based proposed suppress of flexible under Then, adaptive designed handle system parametric uncertainties. The suppression well achieved without violation Numerical simulations are...

The control problem of an uncertain n -degrees freedom robotic manipulator subjected to time-varying output constraints is investigated in this paper. We describe the rigid system as a multi-input and multi-output nonlinear system. devise disturbance observer estimate unknown from humans environment. To solve problem, neural network which utilizes radial basis function used dynamics manipulator. An asymmetric barrier Lyapunov employed process design avert contravention constraints....

Adaptive neural networks (NNs) are employed for control design to suppress vibrations of a flexible robotic manipulator. To improve the accuracy in describing elastic deflection manipulator, system is modeled via lumped spring-mass approach. Full-state feedback as well output proposed separately. Aiming at achieving objective, uniform ultimate boundedness closed-loop ensured. Numerical simulations model carried out verify performance NN control. Finally, experiments given further validate...

This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of rigid body and two wings. Based on Hamilton's principle, distributed parameter system coupling in bending twisting, modeled. Two iterative learning control (ILC) schemes are designed to suppress the vibrations reject disturbances regulate displacement track prescribed constant trajectory. At basis composite energy function, boundedness convergence proved for...

This paper proposes an enhanced robot skill learning system considering both motion generation and trajectory tracking. During demonstrations, dynamic movement primitives (DMPs) are used to model robotic motion. Each DMP consists of a set systems that enhances the stability generated toward goal. A Gaussian mixture regression integrated improve performance DMP, such more features can be extracted from multiple demonstrations. The learned scaled in space time. Besides, neural-network-based...

A boundary control approach is used to a two-link rigid-flexible wing in this article. Its design based on the principle of bionics improve mobility and flexibility aircraft. First, series partial differential equations (PDEs) ordinary (ODEs) are derived through Hamilton's principle. These PDEs ODEs describe governing conditions system, respectively. Then, strategy developed achieve objectives including restraining vibrations bending twisting deflections flexible link achieving desired...

This paper studies the trajectory tracking problem of flapping-wing micro aerial vehicles (FWMAVs) in longitudinal plane. First all, kinematics and dynamics FWMAV are established, wherein aerodynamic force torque generated by flapping wings tail wing explicitly formulated with respect to frequency degree inclination. To achieve autonomous tracking, an adaptive control scheme is proposed under hierarchical framework. Specifically, a bounded position controller hyperbolic tangent functions...

In this brief, the control problem for flexible wings of a robotic aircraft is addressed by using boundary schemes. Inspired birds and bats, wing with flexibility articulation modeled as distributed parameter system described hybrid partial differential equations ordinary equations. Boundary both twist bending proposed on original coupled dynamics, bounded stability proved introducing proper Lyapunov function. The effectiveness verified simulations.

This paper presents adaptive impedance control of an upper limb robotic exoskeleton using biological signals. First, we develop a reference musculoskeletal model the human and experimentally calibrate to match operator's motion behavior. Then, proposed novel algorithm transfers stiffness from operator through surface electromyography (sEMG) signals, being utilized design optimal model. Considering unknown deadzone effects in robot joints absence precise knowledge robot's dynamics, neural...

In this brief, we investigate the control problem of tracking a desired trajectory for fully actuated marine surface vessel considering multiple outputs constraints. To prevent output constraints violation, symmetric barrier Lyapunov function (SBLF) is employed. Backstepping, in combination with adaptive feedback approximation techniques, introduced to design an neural network control. Experimental simulations are provided evaluate feasibility and effectiveness proposed controller. Compared...