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This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

This paper is concerned with the distributed ${H} _{\infty }$ -consensus filtering problem on attitude tracking over a radar filter network subject to switching topology and random packet dropouts occurring in the data transmission from both the Sun sensor and the filters. Since ground-based radars cannot directly measure the satellite attitude, a Sun sensor is deployed at the satellite side and its measurements are transmitted to radar filters through different network communication channels while suffering from random packet dropouts with different probabilities. In the radar filter network, each radar filter receives data not only from the Sun sensor but also from its local neighboring radar filters in accordance with a switching network topology. A delicate distributed ${H} _ {\infty}$ -consensus filtering algorithm, which incorporates the effects of switching network topology and random packet dropouts, is adopted to estimate attitude and attitude-rate. The algorithm guarantees ${H} _ {\infty }$ -consensus attenuation performance for the estimation deviations among radar filters, and the robustness against the switching network topology and packet dropouts for the radar filter network. The illustrative examples are given to verify the effectiveness of the proposed distributed ${H} _ {\infty}$ -consensus filtering algorithm.

The complexity of an optimization problem is determined by its decision and objective spaces. Over the past few decades, a large number of works have focused on the performance improvement of metaheuristic algorithms via the objective space, whereas studies related to the decision space have attracted little attentions. Moreover, metaheuristic algorithms may not obtain satisfactory results within an entire feasible region, even if sufficient computational resources are available. Therefore, reducing the search space (i.e., finding a trust region) may be an effective method to ensure that the convergence is sufficiently close to the global optimal region. However, inappropriate subspace size may also weaken the performance of algorithms except for ones with a sufficiently small search space. To alleviate aforementioned problems, a variable search space (VSS) strategy based on a sequential trust region determination approach is proposed in this paper. In the VSS, the entire optimization process is divided into two stages: the first stage is to use an optimization approach for sequentially finding the trust domain of each variable and then determine the best-matched subspace; the second stage is to employ the optimization method for searching an optimal/near-optimal solution within the found trust region. The effectiveness of the VSS is evaluated using two widely used test suites, that is, IEEE CEC2014 and BBOB2012. Experimental results indicate that improving the algorithm performance is an important method for tackling problems, but locating a trust region is also beneficial for metaheuristic algorithms to improve the solution precision, especially for complex optimization problems.

This paper is concerned with the security problem for a networked control system subject to deception attacks. An adversary is present to corrupt both the sensor-to-controller channel and the controller-to-actuator channel with deception signals when sensor and control data are transmitted via the network medium. We model the malicious deception signals as unknown-but-bounded signals in such a way that no priori knowledge of the attack signals is required. First, a delicate set-membership performance index is proposed for meeting the system security requirement. Second, a novel set-membership estimation method is developed to obtain the desired ellipsoidal estimation set which guarantees the enclosing of true state of the system at every instant of time. Such an ellipsoidal estimation set is then used for deriving the sufficient conditions such that the resulting closed-loop system is set-membership input-to-state-stable and achieves the prescribed security requirement. Finally, a target tracking system is given to demonstrate the effectiveness of the proposed method.

A novel set-membership control strategy is investigated for a class of discrete time-varying systems with unknown-but-bounded noises. Different from some existing set-membership estimation based control methods, the proposed control strategy is designed by using state estimation sets instead of some specific estimation points. The controlled performance requirement is modelled as a new set-membership performance constraint which ensures that the controlled output meets the requirement at any time instant. First, the set-membership filtering method is used for obtaining the ellipsoidal state estimation set. Second, a set-membership controller is constructed by utilising the state estimation which is arbitrarily chosen from the corresponding estimation set. Accordingly, sufficient conditions are derived such that the controlled system is asymptotically stable and satisfies the proposed performance constraint in the presence of unknown-but-bounded noises at each time instant. Then, a constructive computational algorithm is provided to describe the proposed control strategy. Finally, illustrative simulation examples are presented to demonstrate the effectiveness of the proposed method.

This paper is concerned with the strong γc-γcl H∞ stabilization problem for networked control systems (NCSs) subject to denial of service (DoS) attacks, which are common attack behaviors that affect the packet transmission of measurement or control signals. The purpose of the problem under consideration is to design a stable dynamic output feedback (DOF) controller (strong stabilizing controller) with the prescribed H∞ performance norm bound γc to tolerate multiple packet dropouts caused by DoS attacks, such that, the closed-loop system is mean-square stable and captures the H∞ disturbance attenuation norm bound γcl. Based on the Lyapunov functional and the stochastic control approach, some sufficient conditions with the form of matrix inequalities for the existence of the desired stable DOF controller are established. Then, by an orthogonal complement space technique, the controller gain is parameterized. Next, an iterative linear matrix inequality (LMI) algorithm is developed to obtain the controller gain. Finally, the usefulness of the proposed method is indicated by a numerical simulation example.

In these years, with the development of search engines, it is more convenient for people to get information via the Internet. However, despite the benefit, search engines bring new challenges to network security. More and more hackers search for vulnerable websites and computers with the help of search engines and make large-scale attacks. This is quite a challenge to traditional cybersecurity concepts. Honeypot is a technology that is widely used in capturing and analyzing hackers' behaviors. By building virtual objectives with vulnerabilities, honeypots attract hackers and analyze the interaction process with hackers. But traditional honeypot can't attract hackers well and just wait for attackers. We design and implement a new Web honeypot detection system based on search engines, which can not only attract hackers but also can renew the content automotive to synchronize with the new attack technologies. This new kind of honeypot makes up for the shortcomings of traditional honeypots and has great advantages in finding zero-day attacks.

Each type of problems, such as unimodal/multimodal, linear/non-linear, convex/non-convex, and symmetrical/asymmetrical, has its own characteristics. Although various differential evolution (DE) variants have been proposed, several studies indicate that a DE variant may only exhibit high solution efficiency in solving a specific type of problems, but may perform poorly in others. Therefore, an important decision is made to automatically select a suitable DE variant among several chosen algorithms for solving a particular type of problems during the evolutionary process. To achieve this objective, an auto-selection mechanism (ASM) is introduced in this study. In the ASM, rankings attained using Friedman's test are adopted to assess the performances of DE variants. A learning strategy is employed to update the choice probabilities of DE variants, and an additional selection probability is used to alleviate the greedy selection issue. Three sets of benchmark test functions proposed in BBOB2012, IEEE CEC2005, and IEEE CEC2014 are used to evaluate the effectiveness of the ASM. The performance of the proposed algorithm is also compared with that of nine state-of-the-art DE variants and four non-DE algorithms. Statistical analysis results demonstrate that the ASM is an efficient and effective method that can take full advantages of multiple algorithms. Furthermore, the ASM is utilized to estimate the parameters of a heavy oil thermal cracking model. Experimental results indicate that the proposed algorithm outperforms the other compared algorithms in this case.

This paper is concerned with the simultaneous stability of the multi-mode large-scale systems composed of the interaction subsystems. A novel distributed control network consisting of multiple network-based controllers with the partial information exchange is adopted to simultaneously stabilize the large-scale systems in multiple operation modes. In the distributed control network (DCN), a partial state information exchange approach is developed to save the real-time communication and computation resources. To compensate for the effects of dynamic couplings between interaction subsystems, the designed controllers use both the local states and the neighbors’ partial information with packet dropouts for local feedback design. Then, a series of Lyapunov functions are constructed to derive a matrix-inequality-based sufficient condition for the existence of the desired controllers. Based on an orthogonal complement technique, the gains of the controllers in DCN are parameterized. The iterative algorithm for the solution of simultaneous stabilization problem is also developed. Finally, a numerical example is performed to show the relevant feature of the proposed method.

The search efficiency of differential evolution (DE) algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on population information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE (i.e., JADE) using two-phase parameter control scheme (TPC-JADE) is proposed to enhance the performance of DE in the current study. In the TPC-JADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-the-art DE variants on two famous test suites (i.e., IEEE CEC2005 and IEEE CEC2015). Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.

Drilling and riveting are commonly used in aircraft panel assembly process. Due to the fixture positioning error and the deformation of workpiece, the real position and orientation of the workpiece as well as its 3D geometry at the drilling position varies from the nominal CAD model, which would cause an unfavorable impact on assembly quality. Therefore, surface normal measurement and adjustment at the drilling position is of great importance. In this paper, a fast and effective non-contact measurement method for normal vector and height of moderately curved surfaces is accomplished by four laser displacement sensors, and a dedicated NC machine tool is also developed for normal adjustment. Firstly, a novel sensor calibration method based on laser tracker is introduced, which can acquire the sensors’ position and orientation in Tool Coordinate System (TCS) at the same time. The normal vector at hole position is calculated by cross product of any two non-parallel vectors constructed by the four laser projection points on the panel surface. Secondly, the kinematic model of the machine tool is established to calculate the adjustment of each axis of the machine tool with the Homogeneous Transformation Matrix (HTM). Besides, an innovative method to identify the distance of two rotary centers based on two laser interferometers is proposed. Finally, a series of experiments are conducted to validate the feasibility of the proposed method. The results show that the angle deviation can be reduced to less than 0.5° after adjustment, while the accuracy of the surface height is ±0.04 mm.

Press working causes structural deformation of the machine tool, especially the machine tool with large-span gantry or double columns used for the large-sized workpiece. The structural deformation makes the press tool out of accurate positioning, which leads to dimension deviation of the pressed workpiece. Even more, for thin-walled panel riveting, the structural deformation of the riveting machine will cause local deformations of the panel at rivet joints; this accumulated deformation will make the panel out of tolerance. An online deformation measurement and compensation method is developed for adaptive riveting with high accuracy and efficiency in this paper. During the process of servo driving riveting combined with force measurement, the force–displacement data points can be online obtained, and the recognized feature data points by a robust algorithm can represent the critical points during rivet deformation. Combined with the analytical springback of the rivet joint, the deformation of the machine tool can be accurately solved and compensated to the displacement of press tool for high­precision riveting. Simultaneously, with this innovative measurement method, the online quantity inspection for rivet joints can be realized synchronously. Also, a contrast experiment is conducted to validate the effectiveness of the online adaptive deformation compensation based on force–displacement data analysis. The results demonstrate the feasibility of the proposed method in riveting error compensation and riveting quantity inspection.

Shape of flexible part is easy to be out of tolerances due to deformations in the clamping and machining process. These deformations are generated randomly and hard to be predicted. Surface normal drilling and countersinking of flexible part is commonly necessary in aircraft assembly process. An online high precision surface normal measurement and cutter orientation compensation method is developed for adaptively drilling of flexible part in this paper. During the process of normal measurement and adjustment, the distance from tool nose to drilling point is accurately measured and adjusted synchronously, which is critical for countersinking. To accurately measure the current normal of the deformed workpiece, two 2D laser displacement sensors are applied to measure the surface profiles of the workpiece and sample two sets of geometrical points each time. Two crossed spatial curves are respectively fitted by the two sets of geometrical points, and the surface normal at the intersection point of the crossed curves is computed. After the deviation between the measured normal and tool orientation is calculated, an online compensation method is used to eliminate the deviation and meet the perpendicularity requirement of the cutter axis to the workpiece surface, and the distance from tool nose to drilling point is measured and adjusted to the setting value synchronously. The compensation method is based on the kinematic transformation of a five-axis machine tool and is implemented by NC compensation. Simulations and experiments are conducted to validate the feasibility and effectiveness of the online adaptive measurement and adjustment method.

The performance of differential evolution (DE) is significantly influenced by the choice of crossover strategies; therefore, a self-adaptive differential evolution algorithm with crossover strategies adaptation (CSA-SADE) is proposed in this paper to enhance the performance of DE. In CSA-SADE, the suitable control parameters, mutation strategies, and crossover strategies can be achieved in different evolution stages. To demonstrate the effectiveness of CSA-SADE, the proposed algorithm is compared with eight state-of-the-art evolutionary algorithms. The simulation results indicate that CSA-SADE outperforms five improved DE algorithms and three non-DE approaches on a set of 25 CEC2005 benchmark functions. Additionally, the proposed algorithm is employed to estimate the kinetic parameters of mercury oxidation; the results show that CSA-SADE performs better than the compared algorithms in this simulation example.