Resilient control of multi-microgrids against false data injection attack.

A multi-microgrid system, including several microgrids and distributed energy resources, is always threatened by numbers of faults and attacks as a consequence of which malfunctioning can occur on a large scale. Thus, minimizing the effects of such disruptions is of paramount importance. This paper addresses the problem of mitigating a multi-microgrid system that faces false data injection and replay attacks by considering the multi-microgrid as a multi-agent system in which each microgrid as an agent represents a node in a weighted directed graph. The problem of consensus among normal agents is studied when microgrids and their communications are attacked. The malicious agents become isolated with the help of Weighted Mean Subsequence Reduced (W-MSR) algorithms in which all normal agents neglect the extreme values received from their neighbors. The proposed controller is able to maintain the system's desired performance when false data is injected into the system, or valid data is received with time-delays. Finally, numerical examples and simulation results are provided.

A GPU-implemented physics-based haptic simulator of tooth drilling.

BACKGROUND: Restorative dentistry simulation is one of the most challenging applications involving virtual reality and haptics. This paper presents a haptics-based tooth drilling simulator for dental education. METHODS: Unlike the existing methods, the force model is based on physical properties which consider the geometrical model of the tool. In order to provide uniform force feedback from tooth layers, a new approach is suggested in which the physical properties of each tooth voxel are subsequently used in calculating the feedback force. We implement a hashing algorithm for collision detection due to its reduced time complexity. The haptics algorithm has been implemented on a graphics processing unit using the CUDA toolbox. RESULTS: In parallel processing, the speed of haptic loop execution is increased almost 8 times. CONCLUSION: The proposed idea for force calculation leads to a uniform sensation of force. An important feature of the designed system is the capability to run in a real-time fashion.

Estimation of intra-operative brain shift based on constrained Kalman filter.

In this study, the problem of estimation of brain shift is addressed by which the accuracy of neuronavigation systems can be improved. To this end, the actual brain shift is considered as a Gaussian random vector with a known mean and an unknown covariance. Then, brain surface imaging is employed together with solutions of linear elastic model and the best estimation is found using constrained Kalman filter (CKF). Moreover, a recursive method (RCKF) is presented, the computational cost of which in the operating room is significantly lower than CKF, because it is not required to compute inverse of any large matrix. Finally, the theory is verified by the simulation results, which show the superiority of the proposed method as compared to one existing method.

Robust force control in a novel electro-hydraulic structure using polytopic uncertainty representation.

Electro-hydraulic servo systems (EHSS) are used in many industrial applications for position and force control. Force control with a hydraulic actuator is challenging and requires complicated control algorithms used along with high crossover frequency electro-hydraulic valves, even for simple force control tasks. In this paper, a different hydraulic structure is proposed to improve the force tracking quality and increase efficiency in the EHSS. This comes at the cost of a new model with linearization and uncertainty challenges. To address these challenges, a robust H∞ control design approach is followed to control the proposed EHSS. Model linearization uncertainties are approximated by a polytope and a robust controller is designed to keep the system stable and satisfy the H∞ performance conditions within this polytope. Experimental results verify that the objectives of the paper are satisfied after using the proposed system.

Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements.

Whole arm manipulation planning based on feedback velocity fields and sampling-based techniques.

Changing the configuration of a cooperative whole arm manipulator is not easy while enclosing an object. This difficulty is mainly because of risk of jamming caused by kinematic constraints. To reduce this risk, this paper proposes a feedback manipulation planning algorithm that takes grasp kinematics into account. The idea is based on a vector field that imposes perturbation in object motion inducing directions when the movement is considerably along manipulator redundant directions. Obstacle avoidance problem is then considered by combining the algorithm with sampling-based techniques. As experimental results confirm, the proposed algorithm is effective in avoiding jamming as well as obstacles for a 6-DOF dual arm whole arm manipulator.

Bilateral control of master-slave manipulators with constant time delay.

This paper presents a novel teleoperation controller for a nonlinear master-slave robotic system with constant time delay in communication channel. The proposed controller enables the teleoperation system to compensate human and environmental disturbances, while achieving master and slave position coordination in both free motion and contact situation. The current work basically extends the passivity based architecture upon the earlier work of Lee and Spong (2006) [14] to improve position tracking and consequently transparency in the face of disturbances and environmental contacts. The proposed controller employs a PID controller in each side to overcome some limitations of a PD controller and guarantee an improved performance. Moreover, by using Fourier transform and Parseval's identity in the frequency domain, we demonstrate that this new PID controller preserves the passivity of the system. Simulation and semi-experimental results show that the PID controller tracking performance is superior to that of the PD controller tracking performance in slave/environmental contacts.

A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem.

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.

A stable neural network-based observer with application to flexible-joint manipulators.

A stable neural network (NN)-based observer for general multivariable nonlinear systems is presented in this paper. Unlike most previous neural network observers, the proposed observer uses a nonlinear-in-parameters neural network (NLPNN). Therefore, it can be applied to systems with higher degrees of nonlinearity without any a priori knowledge about system dynamics. The learning rule for the neural network is a novel approach based on the modified backpropagation (BP) algorithm. An e-modification term is added to guarantee robustness of the observer. No strictly positive real (SPR) or any other strong assumption is imposed on the proposed approach. The stability of the recurrent neural network observer is shown by Lyapunov's direct method. Simulation results for a flexible-joint manipulator are presented to demonstrate the enhanced performance achieved by utilizing the proposed neural network observer.

Neural network based control schemes for flexible-link manipulators: simulations and experiments.

This paper presents simulation and experimental results on the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. The controllers are designed by utilizing the modified output re-definition approach. The modified output re-definition approach requires only a priori knowledge about the linear model of the system and no a priori knowledge about the payload mass. Four different neural network schemes are proposed. The first two schemes are developed by using a modified version of the 'feedback-error-learning' approach to learn the inverse dynamics of the flexible manipulator. Both schemes require only a linear model of the system for defining the new outputs and for designing conventional PD-type controllers. This assumption is relaxed in the third and fourth schemes. In the third scheme, the controller is designed based on tracking the hub position while controlling the elastic deflection at the tip. In the fourth scheme which employs two neural networks, the first network (referred to as the 'output neural network') is responsible for specifying an appropriate output for ensuring minimum phase behavior of the system. The second neural network is responsible for implementing an inverse dynamics controller. The performance of the four proposed neural network controllers is illustrated by simulation results for a two-link planar flexible manipulator and by experimental results for a single flexible-link test-bed. The networks are all trained and employed as online controllers and no off-line training is required.