In this paper, we investigate the remote robust state estimation problems for nonlinear cyber-physical systems under denial-of-service attacks. The well-known extended Kalman filter is a commonly used method for state estimation of nonlinear systems. However, it does not take into account unmodeled dynamics or parametric uncertainties caused by first-order approximations, which makes its estimation performance unsatisfactory. In addition, denial-of-service attacks prevent the sensor from sending measurements to a remote state estimator by congesting the communication channel, which further deteriorates the estimation performance. To surmount these problems, a robust state estimation algorithm is developed based on sensitivity penalization, taking into account an explicit packet loss parameter. Under certain conditions, the boundedness of estimation error is proved. By selecting a negative resistance oscillation circuit for numerical simulations, it is demonstrated that the remote robust state estimator can markedly improve the estimation performance.

This paper proposes an adaptive funnel control (FC) scheme for servo mechanisms with an unknown dead-zone. To improve the transient and steady-state performance, a modified funnel variable, which relaxes the limitation of the original FC (e.g., systems with relative degree 1 or 2), is developed using the tracking error to replace the scaling factor. Then, by applying the error transformation method, the original error is transformed into a new error variable which is used in the controller design. By using an improved funnel function in a dynamic surface control procedure, an adaptive funnel controller is proposed to guarantee that the output error remains within a predefined funnel boundary. A novel command filter technique is introduced by using the Levant differentiator to eliminate the "explosion of complexity" problem in the conventional backstepping procedure. Neural networks are used to approximate the unknown dead-zone and unknown nonlinear functions. Comparative experiments on a turntable servo mechanism confirm the effectiveness of the devised control method.

In this paper, the tracking control problem is considered for a class of multiple-input multiple-output (MIMO) nonlinear systems with input saturation and unknown direction control gains. A command filtered adaptive neural networks (NNs) control method is presented with regard to the MIMO systems by designing the virtual controllers and error compensation signals. First, the command filtering is used to solve the "explosion of complexity" problem in the conventional backstepping design and the nonlinearities are approximated by NNs. Then, the error compensation signals are developed to conquer the shortcoming of the dynamic surface method. In addition, the Nussbaum-type functions are utilized to cope with the unknown direction control gains. The effectiveness of the proposed new design scheme is illustrated by simulation examples.

Finite-time stability (FTS) analysis and control problems are investigated in this paper for continuous-time networked dynamical systems constituted by many subsystems. Interactions among these subsystems are arbitrary and time-varying, and every subsystem has different and time-varying dynamics. Several differential linear matrix inequality (LMI) formed methods are presented for the FTS analysis and control, which avoid the inverse computation of matrices with large dimensions and efficiently utilize the block diagonal characteristic of system parameters and the sparseness of the subsystem connection matrix. Sufficient conditions only depending on the parameters of each individual subsystem are then derived. Furthermore, LMI-based conditions are also provided for the FTS analysis and control of linear time-invariant networked systems. Numerical simulations show that they are computationally effective to analyze and synthesize a large-scale networked dynamical systems.

This paper considers the fuzzy finite-time tracking control problem for a class of nonlinear systems with input saturation. A novel fuzzy finite-time command filtered backstepping approach is proposed by introducing the fuzzy finite-time command filter, designing the new virtual control signals and the modified error compensation signals. The proposed approach not only holds the advantages of the conventional command-filtered backstepping control, but also guarantees the finite-time convergence. A practical example is included to show the effectiveness of the proposed method.

Recently, graphical characterization of multiagent controllability has been studied extensively. A major effort in the study is to determine controllability directly from topology structures of communication graphs. In this paper, we proposed the concept of controllability destructive nodes, which indicates that the difficulty in graphical characterization turns out to be the identification of topology structures of controllability destructive nodes. It is shown that each kind of double and triple controllability destructive nodes happens to have a uniform topology structure which can be defined similarly. The definition, however, is verified not to be applicable to the topology structures of quadruple controllability destructive (QCD) nodes. Even so, a design method is proposed to uncover topology structures of QCD nodes for graphs with any size, and a complete graphical characterization is presented for the graphs consisting of five vertices. One advantage of the established complete graphical characterization is that the controllability of any graph with any selection of leaders can be determined directly from the identified/defined destructive topology structures. The results generate several necessary and sufficient graphical conditions for controllability. A key step of arriving at these results is the discovery of a relationship between the topology structure of the controllability destructive nodes and a corresponding eigenvector of the Laplacian matrix.

This paper considers the problem of discrete-time adaptive position tracking control for a interior permanent magnet synchronous motor (IPMSM) based on fuzzy-approximation. Fuzzy logic systems are used to approximate the nonlinearities of the discrete-time IPMSM drive system which is derived by direct discretization using Euler method, and a discrete-time fuzzy position tracking controller is designed via backstepping approach. In contrast to existing results, the advantage of the scheme is that the number of the adjustable parameters is reduced to two only and the problem of coupling nonlinearity can be overcome. It is shown that the proposed discrete-time fuzzy controller can guarantee the tracking error converges to a small neighborhood of the origin and all the signals are bounded. Simulation results illustrate the effectiveness and the potentials of the theoretic results obtained.