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...

This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed guarantee that are not violated, in which Moore-Penrose pseudo-inverse term used design. To handle unmodeled dynamics, network (NN) adopted approximate dynamics. NN based full-state feedback robots proposed when all states of closed loop known. Subsequently, only robot measurable practice; output...

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,...

This paper addresses the control design for an upper limb exoskeleton in presence of input saturation. An adaptive controller employing neural network technology is proposed to approximate uncertain robotic dynamics. Also, auxiliary system designed deal with effect Furthermore, we develop both state feedback and output strategies, which effectively estimates uncertainties online from measured errors, instead model-based control. In addition control, a disturbance observer reject unknown...

This paper addresses the problem of robotic manipulators with unknown deadzone. In order to tackle uncertainty and deadzone effect, we introduce adaptive neural network (NN) control for manipulators. State-feedback is introduced first a high-gain observer then designed make proposed scheme more practical. One radial basis function NN (RBFNN) used other RBFNN also estimate dynamics robot. The verified on two-joint rigid manipulator via numerical simulations experiments.

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...

A fundamental requirement in robot control is the capability to improve robot-environment interaction performance. Motivated by fact that humans are able adapt limb impedance stably interact with various environments skillful dexterity, this paper investigates variable for robots, subject uncertainties from plant model or environment. The proposed assists perform given tasks its unknown environment and improves overall robot- system stiffness, damping, inertia can be changed during tasks,...

In this study, the authors aim to solve tracking control problem of coordinated robotic manipulators. order handle with uncertainties and instability manipulators improve performance system output constraint, they design a controller by using radial basis function neural network which has ability approximate any bounded continuous functions effectively. A barrier Lyapunov is also introduced prevent violation constraint. The stability analysis closed‐loop provided verified through simulation.

The biological neural network is a vast and diverse structure with high heterogeneity. Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weights of connections through training while modeling neurons as highly homogenized entities lacking exploration Only few studies have addressed heterogeneity by optimizing neuronal properties connection to ensure performance. However, this strategy impact specific contribution In paper, we first demonstrate challenges faced...

The impedance control for coordinated robots interacting with the unknown environment is investigated in this article, subject to system dynamics and which come into contact. For whole system, developed robots. notable feature that robot-environment interaction performance improved without any information about environment, so robotic follows commanded position trajectory noncontact phase, while desired destination obtained according force exerted on during contact phase. Moreover, based...

The universal droop control (UDC) can be applied to power inverters having an impedance angle between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-\pi /2$</tex-math></inline-formula> and notation="LaTeX">$\pi rad achieve voltage frequency regulation accurate proportional sharing without the need of knowing type or value impedance. However, there is increasing for within predetermined ranges even under...

Simultaneous Localization and Mapping (SLAM) system equipped quadrotors are preferable candidates for autonomous building inspections surveillance tasks, because of their mobility capability working in both indoor outdoor environments. However, the lack robustness still remains as one challenging problems SLAM implementations. Sudden camera moving, motion blur, occlusion all might cause a pose lost map corruption. This problem becomes significant when is mounted on quadrotor, since high...

The only constant is change-power systems worldwide are going through a paradigm change from centralized generation to distributed generation; transportation being electrified; and billions of lives in third-world countries awaiting low-cost sustainable electricity. Control power electronic technologies two common enablers address these grand challenges. Empowering next-generation engineers with hands-on skills control electronics has become priority for global higher education. However,...

During emergencies or natural disasters, at remote areas, the main electrical grid might be damaged not accessible, therefore, a sustainable electricity generation is critical for human survivals other needs. This paper presents sustainable, portable, and efficient delivery (SPEED) system community emergency, disaster relief, power supply areas. A demonstrated with utility-level AC output built up 180 W photovoltaics (PV) array which consists of six serial-connected solar panels, 1.8 kWh...

In this paper, a sustainable, portable, and efficient electricity delivery (SPEED) system is designed built for community emergency/disaster relief with sustainable power solutions. The consists of 180W PV array six serial-connected solar panels, 1.8kWh battery system, SYNDEM converter, human-machine interface. Advanced electronics control technologies are implemented to guarantee the autonomous operation SPEED at either islanded mode or grid-tied cope scenarios. Field testing conducted...

In this paper, a fuzzy neural network controller is proposed for class of robotic manipulators with unknown dynamics and input constraints. First, to cope the dynamics, logic systems are designed approximate it. Second, considering case where an aggressive control may lead poor performance, even resulting in instability practice, function tanh(·), has ability make keep predefined range. At last, evidence based on Lyapunov theory proves system errors converge small range near zero....

Control and power electronics are two major enablers for the paradigm shift of systems from centralized generation to distributed generation, electrification transportation, transformation billions lives in third-world countries. Experimental validations control algorithms these play a vital role. However, setting up suitable experimental system requires time, effort, broad range expertise. This demonstration session aims help researchers, graduate students, engineers remove barriers go real...

In this paper, an adaptive fuzzy neural network control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems with uncertainties state constraints. Fuzzy networks are used to approximate in MIMO systems. Integral Lyapunov functions introduced address By choosing appropriate integral function, the performances closed-loop system completely guaranteed. Simulation results constraints carried out prove effectiveness designed control.

In this paper, adaptive output feedback control via neural networks is designed for a robotic exoskeleton with unknown dynamics. Neural are used to compensate the deadzone effect induced by actuators and dynamics of robot. High-gain observer employed estimate velocity information then integrated in design controller. The approximated Radial Basis Function Network (RBFNN) tracking error bounded converging. estimated another RBFNN. proposed able track desired trajectory. Finally, numerical...

In this paper, an uncertainty and disturbance estimator (UDE)-based robust control strategy is developed for AC/DC converters to achieve accurate DC-link voltage regulation. The models of both dynamics power delivering are derived firstly. UDE introduced into voltage-loop design power-loop handle the uncertainties (e.g., effects model/parametric uncertainties), external disturbances variations amplitude frequency in grid, or load change). proposed simplifies parameter tuning algorithm...

This student project has demonstrated the operation of an autonomous microgrid that is built up with eight grid-forming solar inverters. Experimental results from under different operational scenarios are presented.

Abstract The uncertainty and disturbance estimator (UDE)-based robust control has a two-degree-of-freedom nature through the design of error dynamics UDE filters. In conventional to handle periodic disturbances or mixed sinusoidal disturbances, high-order filters incorporated with internal model principle (IMP) time-delay (TDF) are adopted achieve asymptotic reference tracking rejection. this paper, new combined repetitive loop is proposed for UDE-based rejection both step disturbances....