We review the research on complex dynamical networks (CDNs) with impulsive effects. We provide a comprehensive and intuitive overview of the fundamental results and recent progress of CDNs with impulsive effects, where impulsive effects are considered from two aspects, i.e., impulsive control and impulsive perturbation. Five aspects of CDNs with impulsive effects are surveyed, including synchronizing impulses, desynchronizing impulses, adaptive-impulsive synchronization, pinning impulsive synchronization, and CDNs with stochastic and impulsive effects. Finally, conclusions and some future research directions are briefly addressed.
The control design problem plays a fundamental role in the study of logical control networks (LCNs). This paper presents a detailed survey on new developments in control design techniques of LCNs. First, some preliminary results on the semi-tensor product method and LCNs are reviewed. Then, we move on to some new developments for control design techniques of LCNs, including the reachable set approach, the pinning control technique, the control Lyapunov function approach, the event-triggered control technique, and the sampled-data control technique. Finally, an illustrative example is given to demonstrate the effectiveness of these techniques.
In this study, we investigate the problem of multiple Mittag-Leffler stability analysis for fractional-order quaternion-valued neural networks (QVNNs) with impulses. Using the geometrical properties of activation functions and the Lipschitz condition, the existence of the equilibrium points is analyzed. In addition, the global Mittag-Leffler stability of multiple equilibrium points for the impulsive fractional-order QVNNs is investigated by employing the Lyapunov direct method. Finally, simulation is performed to illustrate the effectiveness and validity of the main results obtained.
In genetic regulatory networks, a stable configuration can represent the evolutionary behavior of cell death or unregulated growth in genes. We present analytical investigations on output feedback stabilizer design of Boolean networks (BNs) to achieve global stabilization via the semi-tensor product method. Based on network structure information describing coupling connections among nodes, an output feedback stabilizer is designed to achieve global stabilization. Compared with the traditional pinning control design, the output feedback stabilizer design is not based on the state transition matrix of BNs, which can efficiently determine pinning control nodes and reduce computational complexity. Our proposed method is efficient in that the calculation of the state transition matrix with dimension 2n × 2n is avoided; here n is the number of nodes in a BN. Finally, a signal transduction network and a D. melanogaster segmentation polarity gene network are presented to show the efficiency of the proposed method. Results are shown to be simple and concise, compared with traditional pinning control for BNs.
We aim to further study the global stability of Boolean control networks (BCNs) under aperiodic sampleddata control (ASDC). According to our previous work, it is known that a BCN under ASDC can be transformed into a switched Boolean network (SBN), and further global stability of the BCN under ASDC can be obtained by studying the global stability of the transformed SBN. Unfortunately, since the major idea of our previous work is to use stable subsystems to offset the state divergence caused by unstable subsystems, the SBN considered has at least one stable subsystem. The central thought in this paper is that switching behavior also has good stabilization; i.e., the SBN can also be stable with appropriate switching laws designed, even if all subsystems are unstable. This is completely different from that in our previous work. Specifically, for this case, the dwell time (DT) should be limited within a pair of upper and lower bounds. By means of the discretized Lyapunov function and DT, a sufficient condition for global stability is obtained. Finally, the above results are demonstrated by a biological example.
This study is concerned with probabilistic Boolean control networks (PBCNs) with state feedback control. A novel definition of bisimilar PBCNs is proposed to lower computational complexity. To understand more on bisimulation relations between PBCNs, we resort to a powerful matrix manipulation called semi-tensor product (STP). Because stabilization of networks is of critical importance, the propagation of stabilization with probability one between bisimilar PBCNs is then considered and proved to be attainable. Additionally, the transient periods (the maximum number of steps to implement stabilization) of two PBCNs are certified to be identical if these two networks are paired with a bisimulation relation. The results are then extended to the probabilistic Boolean networks.
In this study, the complete synchronization problem of coupled delayed Boolean networks (CDBNs) is investigated. The state delays and output delays may not be equal, and the state delay in each Boolean network may be different in the proposed CDBN model. Based on the semi-tensor product of matrices, a necessary and sufficient condition for the complete synchronization of CDBNs is obtained. Then, an efficient algorithm for solving the synchronization of CDBNs is provided. Finally, numerical examples are presented to demonstrate the effectiveness of our algorithm.
We investigate the problem of finding optimal one-bit perturbation that maximizes the size of the basin of attractions (BOAs) of desired attractors and minimizes the size of the BOAs of undesired attractors for large-scale Boolean networks by cascading aggregation. First, via the aggregation, a necessary and sufficient condition is given to ensure the invariance of desired attractors after one-bit perturbation. Second, an algorithm is proposed to identify whether the one-bit perturbation will cause the emergence of new attractors or not. Next, the change of the size of BOAs after one-bit perturbation is provided in an algorithm. Finally, the efficiency of the proposed method is verified by a T-cell receptor network.
Two types of cascading decomposition problems of Boolean control networks are investigated using a raph-theoretical method. A new graphic concept called nested perfect equal vertex partition (NPEVP) is proposed. Based on NPEVP, the necessary and sufficient graphic conditions for solvability of the cascading decomposition problems are obtained. Given the proposed graphic conditions, the logical coordinate transformations are constructively obtained to realize the corresponding cascading decomposition forms. Finally, two illustrative examples are provided to validate the results.
In this study, the output tracking of delayed logical control networks (DLCNs) with state and control constraints is further investigated. Compared with other delays, state-dependent delay updates its value depending on the current state values and a pseudo-logical function. Multiple constraints mean that state values are constrained in a nonempty set and the design of the controller is conditioned. Using the semi-tensor product of matrices, dynamical equations of DLCNs are converted into an algebraic description, and an equivalent augmented system is constructed. Based on the augmented system, the output tracking problem is transformed into a set stabilization problem. A deformation of the state transition matrix is computed, and a necessary and sufficient condition is derived for the output tracking of a DLCN with multi-constraint. This condition is easily verified by mathematical software. In addition, the admissible state-feedback controller is designed to enable the outputs of the DLCN to track the reference signal. Finally, theoretical results are illustrated by an example.
This paper is concerned with inertial bidirectional associative memory neural networks with mixed delays and impulsive effects. New and practical conditions are given to study the existence, uniqueness, and global exponential stability of anti-periodic solutions for the suggested system. We use differential inequality techniques to prove our main results. Finally, we give an illustrative example to demonstrate the effectiveness of our new results.
Good access to traffic information provides enormous potential for automotive powertrain control. We propose a logical control approach for the gearshift strategy, aimed at improving the fuel efficiency of vehicles. The driver power demand in a specific position usually exhibits stochastic features and can be statistically analyzed in accordance with historical driving data and instant traffic conditions; therefore, it offers opportunities for the design of a gearshift control scheme. Due to the discrete characteristics of a gearshift, the control design of the gearshift strategy can be formulated under a logic system framework. To this end, vehicle dynamics are discretized with several logic states, and then modeled as a logic system with the Markov process model. The fuel optimization problem is constructed as a receding-horizon optimal control problem under the logic system framework, and a dynamic programming algorithm with algebraic operations is applied to determine the optimal strategy online. Simulation results demonstrate that the proposed control design has better potential for fuel efficiency improvement than the conventional method.