In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of unknown robotic dynamics and environment with which robot comes into contact. First, an FNN algorithm is developed to identify plant model. Second, introduced regulate input order improve environment-robot interaction, can track desired trajectory generated by learning. Third, light condition requiring move finite...

In this paper, an adaptive neural bounded control scheme is proposed for ${n}$ -link rigid robotic manipulator with unknown dynamics. With the combination of approximation and backstepping technique, network policy developed to guarantee tracking performance robot. Different from existing results, bounds designed controller are known a priori, they determined by gains, making them applicable within actuator limitations. Furthermore, also able compensate effect Via Lyapunov stability theory,...

In this article, a novel adaptive tracking control technique is developed for multiple-input-multiple-output nonlinear systems with model uncertainty and under output constraints occurring in limited time interval (OCOLT). The OCOLT, which type of sometime after (rather than the beginning of) system operation duration, can be found many practical has not been effectively addressed literature until now. By designing new shift function aid barrier functions, constrained transformed into an...

Abstract In this article, a robot skills learning framework is developed, which considers both motion modeling and execution. order to enable the learn from demonstrations, method called dynamic movement primitives (DMPs) introduced model motion. A staged teaching strategy integrated into DMPs frameworks enhance generality such that complicated tasks can be also performed for multi-joint manipulators. The DMP connection used make an accurate smooth transition in position velocity space...

This article develops a robust fault tolerant (FT) control scheme for an n-link uncertain robotic system with actuator failures. In order to eliminate the influence of both uncertainties and failures on performance, Gaussian radial basis function neural networks are used compensate dynamics. An adaptive observer is designed external disturbance. addition, in accelerate recovery stability after failure, nonsingular fast terminal sliding mode given. Finally, simulation results two-link...

A finite-time control method is presented for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -link robots with actuator saturation under time-varying constraints of work space. Barrier Lyapunov functions (BLFs) are designed ensuring that the robot remains In order to deal asymmetric nonlinearity, we transform into a symmetric one by using hyperbolic tangent function, which...

In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multipleinput and multiple-output (MIMO) nonlinear systems subject to unknown dynamics state constraints. FNNs are used approximate the that comprises effects uncertain parameters functions. Also, integral Lyapunov functions introduced address A neuralnetwork-based observer designed estimate unmeasurable states. With state-feedback output feedback tracking control, stability closed-loop system...

We aim at the optimization of tracking control a robot to improve robustness, under effect unknown nonlinear perturbations. First, an auxiliary system is introduced, and optimal can be seen as approximate robot. Then, neural networks (NNs) are employed solution Hamilton-Jacobi-Isaacs equation frame adaptive dynamic programming. Next, based on standard gradient attenuation algorithm critic design, NNs trained depending designed updating law with relaxing requirement initial stabilizing...

With the more extensive application of flexible robots, expectation for manipulators is also increasing rapidly. However, fast convergence will cause increase vibration amplitude to some extent, and it difficult obtain suppression satisfactory transient performance at same time. In order deal with problem, a fixed-time learning control method proposed realize convergence. The constraint on system outputs, uncertainty, input saturation addressed under framework. A novel adaptive law neural...

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for class of nonlinear bilateral teleoperation systems. First, an scheme is applied estimate the upper bound delay, which can resolve predicament that has significant impacts on stability Then, radial basis function neural networks (RBFNNs) are utilized estimating systems, including operator, environmental models. Novel adaptation laws introduced address...

We develop and experimentally test a prescribed performance control methodology for trajectory tracking of underactuated autonomous aerial vehicles under unknown time-varying disturbances. An extended state observer is first proposed to filter position velocity measurements, compensate disturbances, including model parametric uncertainty and/or exogenous perturbations, e.g., wind. Then, by resorting barrier function constraints on the transient steady-state response error, we derive thrust...

In this article, neural network (NN)-based sliding mode control schemes are proposed for an n-link robotic manipulator with system uncertainties, input deadzone, and external perturbations. A novel error-shifting function is to release initial conditions. NNs employed approximate the unknown parameters of both uncertainties deadzone. To update scheme, two advanced surfaces barrier reduce dependency prior information realize a finite time convergence result, collectively. It should be pointed...

In this article, we develop a methodology for trajectory tracking control of vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) in the presence unknown mass time-varying external disturbances. A time-dependent shift function is introduced, which, alongside set barrier vector functions, enables finite period constrained outputs, namely, position velocity errors. The proposed controller also features two stable adaption laws to estimate following: 1) UAV 2) parameter relating...

In this article, we propose a novel dual-filter architecture utilizing RGB-D camera data and dynamic control barrier functions (D-CBFs) for real-time obstacle avoidance in unstructured environments. The method efficiently handles static, sudden, obstacles, maintaining consistent performance across diverse scenarios. To address the challenges of processing substantial pixel depth managing increased optimization solver time multiobstacle scenarios, first introduce an enhanced fast-saliency...

An adaptive fuzzy neural network (FNN) control scheme is proposed for a marine vessel with time‐varying constraints, guaranteed transient response and unknown dynamics. A series of continuous constraint functions are introduced to shape the motion vessel. To deal problems problems, an asymmetric barrier Lyapunov function designed ensure that system states upper bounded by considered functions. FNNs constructed identify Considering existing approximation errors when approximating dynamics,...

This article discusses a building-like structure composed of flexible beam, rigid support plate, and motor fixture. The beam causes significant vibration, which leads to user discomfort. proposes solution suppress the vibration by designing performance constraint boundary limit amplitude in terms transient steady-state performance. system model with uncertainties is approximated using radial basis function (RBF) neural networks. Fixed-time methods are employed further reduce enhance...

A neural learning-based finite-time control policy is presented for a robotic manipulator with unknown backlash-like hysteresis and system uncertainties. Adaptive networks are adopted to deal dynamics. In order eliminate the effect of hysteresis, robust adaptive term designed in backstepping design process. network-based controller by introducing fractional term, which guarantees convergence both terms, this type improves accuracy certain extent. With Lyapunov stability theory, proposed...

In this article, cooperative circumnavigation (CCN) is studied for groups of networked unmanned aerial vehicles (UAVs) under a directed interaction topology. The CCN drives UAVs to given planar ellipses with desired spatial formation. A first contribution that based on affine transformations, control design structured, which can deploy orbits different radii concerning moving target in 3-D space. Second, no global information, such as the formation center, radius, angular velocity...