In this work a control scheme is proposed to enforce dynamic obstacle avoidance constraints to the full body of actively compliant robots. We argue that both compliance and accuracy are necessary to build safe collaborative robotic systems; obstacle avoidance is usually not enough, due to the reliance on perception systems which exhibit delays and errors. Our scheme is able to successfully avoid obstacles, while remaining compliant in the entirety of the executed task. Therefore, in case of unexpected collisions due to perception system errors, the robot remains safe for humans and its environment. Our approach is validated through experiments with simulated and real obstacles utilizing a 7-dof KUKA LBR iiwa robotic manipulator.

The problem of motion planning in obstacle cluttered environments is an important task in robotics. In the literature several methodologies exist to address the problem. In this work we consider using the feedback-based approach, where the solution comes from designing a controller capable of guaranteeing trajectory tracking with obstacle avoidance. Commonly, all respective studies consider simplified robot dynamics, which is usually insufficient in practical applications. In this work we focus on the collision avoidance problem with respect to a moving spherical object. We assume knowledge of a nominal controller that achieves tracking of a desired trajectory in the absence of obstacles, and we design an auxiliary control scheme to guarantee that the robot’s end-effector will always operate in a safe distance from the moving obstacle’s surface. The controller we develop does not take into account the actual robot dynamics, thus constituting a truly model-free approach. Experimental studies conducted on a KUKA LWR4+ robotic manipulator clarify and verify the proposed control scheme.

In this article, a passive physical human-robot interaction (pHRI) controller is proposed to enhance pHRI performance in terms of precision, cognitive load, and user effort, in cases partial knowledge of the task is available. Partial knowledge refers to a subspace of SE(3) determined by the desired task, generally mixing both position and orientation variables, and is mathematically approximated by parametric expressions. The proposed scheme, which utilizes the notion of virtual constraints and the prescribed performance control methodology, is proved to be passive with respect to the interaction force, while guaranteeing constraint satisfaction in all cases. The control scheme is experimentally validated and compared with a dissipative control scheme utilizing a KUKA LWR4+ robot in a master-slave task; experiments also include an application to a robotic assembly case.

In this work, a passive physical human-robot interaction (pHRI) controller is proposed to intraoperatively ensure that sensitive tissues will not be damaged by the robot's tool. The proposed scheme uses the point cloud of the restricted region's surface as constraint definition and Artificial Potential fields for constraint enforcement. The controller is proven to be passive with respect to the interaction force and to guarantee constraint satisfaction in all cases. The proposed methodology is experimentally validated by the kinesthetic guidance of a KUKA LWR4+ robot's end-effector driving a virtual slave KUKA in the vicinity of a 3D point-cloud of a kidney and its adjacent vessels.

In this work a modification of a Cartesian impedance controller is presented. The proposed controller guarantees compliance of the operated manipulator strictly within preselected spatial limits. Specifically, by introducing a nonlinear stiffness term into the control design, the manipulator is prevented from colliding with an explicitly defined hard boundary, like a human being or a fragile object. When operating in spatial regions sufficiently away from the boundary the manipulator behaves as if it were operated via a linear stiffness impedance controller, thus preserving in these regions its compliance and convergence characteristics. A comparative simulation with a linear stiffness impedance controller is performed to verify and clarify the proposed approach.

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. Furthermore, the loss of model controllability problem, typically introduced owing to approximation model singularities, is avoided without attaching additional complexity to the control or adaptive law. Simulations are performed to verify and clarify the theoretical findings.

In this paper a novel type of tracking controller for flexible joint robots is proposed. Joint elasticity is considered unknown and may be time varying. Robot and motor dynamics are also considered unknown. The controller guarantees link position performance specifications that have been a-priori set utilizing full state feedback. Simulation on a two link flexible joint robot validate the efficiency of the proposed control approach.

In this paper a simple tracking controller for a variable stiffness joint is proposed. System dynamics is considered unknown. The controller guarantees link and stiffness motor position performance specifications that have been apriori set, utilizing full state feedback. Simulation results on the previously published CompAct-VSA joint validate the efficiency of the proposed control approach.

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. A comparative simulation study clarifies and verifies the approach.

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. However, its continuity is guaranteed, thus alleviating any issues related to the existence and uniqueness of solutions. Simulations on a planar two-link articulated manipulator illustrate the approach.

A neural network output feedback/observer-free continuous controller for multiple-input-multiple-output uncertain nonlinear systems is designed, which is capable of guaranteeing prescribed performance bounds on the system output, as well as the boundedness of all other closed-loop signals, despite the presence of additive external disturbances and unmodeled dynamics. The assumptions that were made concern the satisfaction of an unboundedness observability property and an output Lagrange stability condition of the unmodeled dynamics subsystem and that the nominal system is output feedback equivalent to a strictly passive one. Simulations on an induction motor system illustrate the approach.

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. Thus, the controller gains are easily selected, and potentially large neural network approximation errors as well as disturbances can be tolerated. Simulation results on a 6-DOF robot confirm the theoretical findings.

A control law is proposed that achieves predefined performance indices regarding the speed of response, the steady state and the allowed overshoot of the robot force/position tracking errors, ensuring no loss of contact of the robot end effector. The controller incorporates a transformed error, which includes the performance indices. The control objective is satisfied under parametric uncertainties in the robot dynamics and the elasticity model constant. Simulation results confirm the theoretical findings and compare the proposed controller with a conventional one.

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.

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. In this way, problems connected to existence of solutions and chattering phenomena are alleviated. Simulations illustrate the approach.

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. Without requiring a zero-state detectability assumption, uniform ultimate boundedness, with respect to an arbitrarily small set, of both the system's state and the output is guaranteed, along with boundedness of all other signals in the closed loop. To effectively avoid possible division by zero, the proposed adaptive controller is of switching type. However, its continuity is guaranteed, thus alleviating drawbacks connected to existence of solutions and chattering phenomena. Simulations illustrate the 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. The friction internal state is not assumed to be available for measurement. The neural fault detection methodology is analyzed with respect to its robustness and sensitivity properties. Rigorous fault detectability conditions and upper bounds for the detection time are also derived. Extensive simulation results showing the effectiveness of the proposed methodology are provided, including a real case study on an industrial actuator.

Present multimedia services are provided through a heterogeneous set of networks. Because of the heterogeneity of the networks, long-term resource availability guarantees are difficult to obtain. Consequently, even if the resource requirements throughout the service could be accurately mapped, it would not be feasible to provide overall performance guarantees. Application adaptation arises as an appropriate solution for quality-of-service assurance in such dynamic service infrastructures. Application adaptation basically involves adapting application characteristics according to the network resource availability. We formulate the problem of adaptation of multimedia applications to network infrastructure as well as to user- and application-imposed constraints and preferences. We benefit from the good approximation, identification, and control capabilities of recurrent high-order neural networks and we introduce an algorithm that guarantees the resource constraints of the network infrastructure will not be violated while maintaining the user and application requirements.

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. The uniform ultimate boundedness of the system output to an arbitrarily small set, plus the boundedness of all other signals in the closed-loop is guaranteed.

Neural network literature for function approximation is by now sufficiently rich. In its complete form, the problem entails both parametric (i.e., weights determination) and structural learning (i.e., structure selection). The majority of works deal with parametric uncertainty assuming knowledge of the appropriate neural structure. In this paper we present an algorithmic approach to determine the structure of High Order Neural Networks (HONNs), to solve function approximation problems. The method is based on a Genetic Algorithm (GA) and is equipped with a stable update law to guarantee parametric learning. Simulation results on an illustrative example highlight the performance and give some insight of the proposed approach.

In this paper, a hybrid neural network/genetic algorithm technique is presented, aiming at designing a feature extractor that leads to highly separable classes in the feature space. The application upon which the system is built, is the identification of the state of human peripheral vascular tissue (i.e., normal, fibrous and calcified). The system is further tested on the classification of spectra measured from the cell nucleii in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. As advantages of the proposed technique we may encounter the algorithmic nature of the design procedure, the optimized classification results and the fact that the system performance is less dependent on the classifier type to be used.

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. To increase the accuracy in the computation of the lead displacement, we introduce a combined classification/estimation process, in which the individual lead displacement classifications are viewed as measurements (or observations) of the same physical quantity i.e., the displacement of the entire component as a rigid body. Certain geometric relations connecting lead shifts to component displacement are also derived. Employing these relations we can infer a new refined measurement of the shift of each individual lead, a quantity crucial to the calculation of the quality measures. Experimental results highlight the potential of the developed algorithm.

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 considered approach comprises two stages, i.e., observation and Bayesian estimation. The first stage 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. To increase the accuracy in the computation of the displacement, the second stage develops a combined classification/estimation process, in which the individual lead displacement classifications are viewed as measurements (or observations) of the same physical quantity i.e., the displacement of the entire component as a rigid body. Experimental results highlight the potential of the developed algorithm.

The use of neural network structures for feature extraction and classification is addressed here. More precisely, a nonlinear filter based on higher order neural networks (HONN) whose weights are updated by stable learning laws is used to extract the characteristic features of fluorescence spectra corresponding to human tissue samples of different states. The features are then classified with a multi-layer perceptron (MLP). The high rates of success together with the small time needed to analyze the signals, proves our method very attractive for real time applications.

We discuss the tracking problem in the presence of additive and multiplicative external disturbances, for affine in the control nonlinear dynamical systems, whose nonlinearities are assumed unknown. Based on a recurrent high order neural network (RHONN) model of the unknown plant, a smooth control law is designed to guarantee the uniform ultimate boundedness of all signals in the closed loop. Certain measures are utilized to test its performance. The controller, which can be viewed as a nonlinear combination of three high order neural networks, does not require knowledge regarding upper bounds on the optimal weights, modeling error and external disturbances. Simulations performed on a simple example illustrate the approach.

The purpose of this paper is to design and rigorously analyze a tracking controller, based on a dynamic neural network model for unknown but affine in the control, multi input nonlinear dynamical systems, Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. The controller derived is smooth. No a priori knowledge of an upper bound on the "optimal" weights and modeling errors is required. Simulation studies are used, to illustrate and clarify the theoretical results.

In this paper, we consider the problem of adaptive stabilizing unknown nonlinear systems whose state is contaminated with external disturbances that act additively. A uniform ultimate boundedness property for the actual system state is guaranteed, as well as boundedness of all other signals in the closed loop. It is worth mentioning that the above properties are satisfied without the need of knowing a bound on the "optimal" weights, providing in this way higher degrees of autonomy to the control system. Thus, the present work can be seen as a first approach toward the development of practically autonomous systems.

With this paper we extend our previous work on the subject, by including the case where the number of control inputs is different from the number of states which is frequently faced in control engineering problems. Uniform ultimate boundedness of the state and uniform boundedness of all other signals in the closed loop is guaranteed. Robustness of our algorithm due to the presence of a modeling error term which has linear growth with unknown growth coefficient is also established. Finally, the applicability of our control scheme is highlighted via simulation results.

A direct nonlinear adaptive state regulator, for unknown dynamical systems that are modeled by dynamic neural networks is discussed. In the ideal case of complete model matching, convergence of the state to zero plus boundedness of all signals in the closed loop is ensured. Moreover, the behavior of the closed loop system is analyzed for cases in which the true plant differs from the dynamic neural network model in the sence that it is of higher order, or due to the presence of a modeling error term. In both cases, modifications of the original control and update laws are provided, so that at least uniform ultimate boundedness is guaranteed, even though in some cases the stability results obtained for the ideal case are retained.

In this paper, we are dealing with the problem of controlling an unknown nonlinear dynamical system. The algorithm is divided into two phases. First a dynamical neural network identifier is employed to perform "black box" identification and then a dynamic state feedback is developed to appropriately control the unknown system. We apply the algorithm to control the speed of a nonlinearized DC motor, giving in this way an application insight. In the algorithm, not all the plant states are assumed to be available for measurement.>