A simplified adaptive neural network prescribed performance controller for uncertain MIMO feedback linearizable systems.

In this paper, the problem of deriving a continuous, state-feedback controller for a class of multiinput multioutput feedback linearizable systems is considered with special emphasis on controller simplification and reduction of the overall design complexity with respect to the current state of the art. The proposed scheme achieves prescribed bounds on the transient and steady-state performance of the output tracking errors despite the uncertainty in system nonlinearities. Contrary to the current state of the art, however, only a single neural network is utilized to approximate a scalar function that partly incorporates the system nonlinearities.

A priori guaranteed evolution within the neural network approximation set and robustness expansion via prescribed performance control.

A neuroadaptive control scheme for strict feedback systems is designed, which is capable of achieving prescribed performance guarantees for the output error while keeping all closed-loop signals bounded, despite the presence of unknown system nonlinearities and external disturbances. The aforementioned properties are induced without resorting to a special initialization procedure or a tricky control gains selection, but addressing through a constructive methodology the longstanding problem in neural network control of a priori guaranteeing that the system states evolve strictly within the compact region in which the approximation capabilities of neural networks hold. Moreover, it is proven that robustness against external disturbances is significantly expanded, with the only practical constraint being the magnitude of the required control effort.

Adaptive dynamic output feedback neural network control of uncertain MIMO nonlinear systems with prescribed performance.

An adaptive dynamic output feedback neural network controller for a class of multi-input/multi-output affine in the control uncertain nonlinear systems is designed, capable of guaranteeing prescribed performance bounds on the system's output as well as boundedness of all other closed loop signals. It is proved that simply guaranteeing a boundedness property for the states of a specifically defined augmented closed loop system is necessary and sufficient to solve the problem under consideration. The proposed dynamic controller is of switching type.

Neuro-adaptive force/position control with prescribed performance and guaranteed contact maintenance.

In this paper, we address unresolved issues in robot force/position tracking including the concurrent satisfaction of contact maintenance, lack of overshoot, desired speed of response, as well as accuracy level. The control objective is satisfied under uncertainties in the force deformation model and disturbances acting at the joints. The unknown nonlinearities that arise owing to the uncertainties in the force deformation model are approximated by a neural network linear in the weights and it is proven that the neural network approximation holds for all time irrespective of the magnitude of the modeling error, the disturbances, and the controller gains.

Approximate adaptive output feedback stabilization via passivation of MIMO uncertain systems using neural networks.

An adaptive output feedback neural network controller is designed, which is capable of rendering affine-in-the-control uncertain multi-input-multi-output nonlinear systems strictly passive with respect to an appropriately defined set. Consequently, a simple output feedback is employed to stabilize the system. The controlled system need not be in normal form or have a well-defined relative degree.

Fairness guarantees in a neural network adaptive congestion control framework.

The recently proposed neural network rate control (NNRC) framework that achieves queueing delay and queue length regulation, is expanded to further guarantee fair allocation of network resources among competing sources. This is possible by introducing a novel algorithm that controls in a stable and adaptive manner the number of communication channels in each source. Simulation studies performed on a heterogeneous delay, long-distance high-speed network, illustrate all aspects of the developed methodology.

Adaptive neural network tracking control with disturbance attenuation for multiple-input nonlinear systems.

A switching adaptive neural network controller for multiple-input nonlinear, affine in the control dynamical systems with unknown nonlinearities is designed, capable of arbitrarily attenuating L(2) or L(infinity) external disturbances. In the absence of disturbances, a uniform ultimate boundedness property of the tracking error with respect to an arbitrarily small set around the origin is guaranteed, as well as the uniform boundedness of all the signals in the closed loop. The proposed switching adaptive controller effectively avoids possible division by zero, while guaranteeing the continuity of switching.

Fault detection in mechanical systems with friction phenomena: an online neural approximation approach.

In this paper, the problem of fault detection in mechanical systems performing linear motion, under the action of friction phenomena is addressed. The friction effects are modeled through the dynamic LuGre model. The proposed architecture is built upon an online neural network (NN) approximator, which requires only system's position and velocity.

Robust redesign of a neural network controller in the presence of unmodeled dynamics.

This paper presents a neural network control redesign, which achieves robust stabilization in the presence of unmodeled dynamics restricted to be input to output practically stable (IOpS), without requiring any prior knowledge on any bounding function. Moreover, the state of the unmodeled dynamics is permitted to go unbounded provided that the nominal system state and/or the control input also go unbounded. The neural network controller is equipped with a resetting strategy to deal with the problem of possible division by zero, which may appear since we consider unknown input vector fields with unknown signs.

A Bayesian framework for multilead SMD post-placement quality inspection.

In this paper, a novel framework is proposed to inspect the placement quality of surface mount technology devices (SMDs), immediately after they have been placed in wet solder paste on a printed circuit board (PCB). The developed approach involves the indirect measurement of each lead displacement with respect to its ideal position, centralized on its pad region. This displacement is inferred from area measurements on the raw image data of the lead region through a classification process.