We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis. The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of n linear matrix inequality (LMI) equations combining system parameters. Finally, we perform several numerical simulations to verify the effectiveness of the given algorithms.
We proposed a lower extremity exoskeleton for power amplification that perceives intended human motion via humanexoskeleton interaction signals measured by biomedical or mechanical sensors, and estimates human gait trajectories to implement corresponding actions quickly and accurately. In this study, torque sensors mounted on the exoskeleton links are proposed for obtaining physical human-robot interaction (pHRI) torque information directly. A Kalman smoother is adopted for eliminating noise and smoothing the signal data. Simultaneously, the mapping from the pHRI torque to the human gait trajectory is defined. The mapping is derived from the real-time state of the robotic exoskeleton during movement. The walking phase is identified by the threshold approach using ground reaction force. Based on phase identification, the human gait can be estimated by applying the proposed algorithm, and then the gait is regarded as the reference input for the controller. A proportional-integral-derivative control strategy is constructed to drive the robotic exoskeleton to follow the human gait trajectory. Experiments were performed on a human subject who walked on the floor at a natural speed wearing the robotic exoskeleton. Experimental results show the effectiveness of the proposed strategy.
A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.
Security issues in networked control systems (NCSs) have received increasing attention in recent years. However, security protection often requires extra energy consumption, computational overhead, and time delays, which could adversely affect the real-time and energy-limited system. In this paper, random cryptographic protection is implemented. It is less expensive with respect to computational overhead, time, and energy consumption, compared with persistent cryptographic protection. Under the consideration of weak attackers who have little system knowledge, ungenerous attacking capability and the desire for stealthiness and random zero-measurement attacks are introduced as the malicious modification of measurements into zero signals. NCS is modeled as a stochastic system with two correlated Bernoulli distributed stochastic variables for implementation of random cryptographic protection and occurrence of random zero-measurement attacks; the stochastic stability can be analyzed using a linear matrix inequality (LMI) approach. The proposed stochastic stability analysis can help determine the proper probability of running random cryptographic protection against random zero-measurement attacks with a certain probability. Finally, a simulation example is presented based on a vertical take-off and landing (VTOL) system. The results show the effectiveness, robustness, and application of the proposed method, and are helpful in choosing the proper protection mechanism taking into account the time delay and in determining the system sampling period to increase the resistance against such attacks.
With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features, but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code (DCBMCC) as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code (MCC), as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed. Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC. Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process. Eventually, a multi-index hashing (MIH) based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.
Reconstructing the shape and position of plasma is an important issue in Tokamaks. Equilibrium and fitting (EFIT) code is generally used for plasma boundary reconstruction in some Tokamaks. However, this magnetic method still has some inevitable disadvantages. In this paper, we present an optical plasma boundary reconstruction algorithm. This method uses EFIT reconstruction results as the standard to create the optimally optical reconstruction. Traditional edge detection methods cannot extract a clear plasma boundary for reconstruction. Based on global contrast, we propose an edge detection algorithm to extract the plasma boundary in the image plane. Illumination in this method is robust. The extracted boundary and the boundary reconstructed by EFIT are fitted by same-order polynomials and the transformation matrix exists. To acquire this matrix without camera calibration, the extracted plasma boundary is transformed from the image plane to the Tokamak poloidal plane by a mathematical model, which is optimally resolved by using least squares to minimize the error between the optically reconstructed result and the EFIT result. Once the transform matrix is acquired, we can optically reconstruct the plasma boundary with only an arbitrary image captured. The error between the method and EFIT is presented and the experimental results of different polynomial orders are discussed.
Combining named data networking (NDN) and software-defined networking (SDN) has been considered as an important trend and attracted a lot of attention in recent years. Although much work has been carried out on the integration of NDN and SDN, the forwarding mechanism to solve the inherent problems caused by the flooding scheme and discard of interest packets in traditional NDN is not well considered. To fill this gap, by taking advantage of SDN, we design a novel forwarding mechanism in NDN architecture with distributed controllers, where routing decisions are made globally. Then we show how the forwarding mechanism is operated for interest and data packets. In addition, we propose a novel routing algorithm considering quality of service (QoS) applied in the proposed forwarding mechanism and carried out in controllers. We take both resource consumption and network load balancing into consideration and introduce a genetic algorithm (GA) to solve the QoS constrained routing problem using global network information. Simulation results are presented to demonstrate the performance of the proposed routing scheme.
In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization (EM) algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly, we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements. These results are confirmed by our simulations.
We first present a new multi-modular shunt active power filter system suitable for large-capacity compensation. Each module in the system has the same circuit topology, system functionality, and controller design, to achieve coordination control among the modules. The module’s reference signals are obtained by multiplying the total reference signal by the respective distribution coefficient. Next, a novel fault-tolerant approach is proposed based on split-phase control in the a-b-c frame and real-time bus communication. When a phase fault occurs, instead of halting the whole module, the proposed strategy isolates only the faulted bridge arm, and then recalculates the distribution coefficients and transfers the compensation capacity to the same phases of the other normal modules, resulting in a continuous operation of the faulted module and optimization of the remaining usable power devices. Through steady-state analysis of the post-fault circuit, the system stability and control reliability are proven to be high enough to guarantee its engineering application value. Finally, a prototype is established and experimental results show the validity and feasibility of the proposed multi-modular system and its fault-tolerant control strategy.
In this study, a new controller for chaos synchronization is proposed. It consists of a state feedback controller and a robust control term using Legendre polynomials to compensate for uncertainties. The truncation error is also considered. Due to the orthogonal functions theorem, Legendre polynomials can approximate nonlinear functions with arbitrarily small approximation errors. As a result, they can replace fuzzy systems and neural networks to estimate and compensate for uncertainties in control systems. Legendre polynomials have fewer tuning parameters than fuzzy systems and neural networks. Thus, their tuning process is simpler. Similar to the parameters of fuzzy systems, Legendre coefficients are estimated online using the adaptation rule obtained from the stability analysis. It is assumed that the master and slave systems are the Lorenz and Chen chaotic systems, respectively. In secure communication systems, observer-based synchronization is required since only one state variable of the master system is sent through the channel. The use of observer-based synchronization to obtain other state variables is discussed. Simulation results reveal the effectiveness of the proposed approach. A comparison with a fuzzy sliding mode controller shows that the proposed controller provides a superior transient response. The problem of secure communications is explained and the controller performance in secure communications is examined.
We design a grey wolf optimizer hybridized with an interior point algorithm to correct a faulty antenna array. If a single sensor fails, the radiation power pattern of the entire array is disturbed in terms of sidelobe level (SLL) and null depth level (NDL), and nulls are damaged and shifted from their original locations. All these issues can be solved by designing a new fitness function to reduce the error between the preferred and expected radiation power patterns and the null limitations. The hybrid algorithm has been designed to control the array’s faulty radiation power pattern. Antenna arrays composed of 21 sensors are used in an example simulation scenario. The MATLAB simulation results confirm the good performance of the proposed method, compared with the existing methods in terms of SLL and NDL.