2,312 publications from this institution
This paper studies neural learning control. Based on an earlier result for deterministic learning of unknown system dynamics from a stable control process, this paper provides detailed analysis on how the learned knowledge can be effectively exploited to achieve stability and improved control performance. Comparisons on neural learning control with adaptive neural control and linear control are also included. The effectiveness of the neural learning control approach is demonstrated using simulations.
We study the interplay between evolutionary game and network structure and show how the dynamics of the game affect the growth pattern of the network and how the evolution of the network influence the cooperative behavior in the game. Simulation results show that the payoff-based preferential attachment mechanism leads to the emergence of a scale-free structural property, $P(k)\sim k^{-γ}$. Moreover, we investigate the average path length and the assortative mixing features. The obtained results indicate that the network has small-world and positive assortative behaviors, which are consistent with the observations of some real social networks. In parallel, we found that the evolution of the underlying network structure effectively promotes the cooperation level of the game. We also investigate the wealth distribution obtained by our model, which is consistent with the Pareto law in the real observation. In addition, the analysis of the generated scale-free network structure is provided for better understanding the evolutionary dynamics of our model.
This paper provides a unified method for analyzing chaos synchronization of the generalized Lorenz systems. The considered synchronization scheme consists of identical master and slave generalized Lorenz systems coupled by linear state error variables. A sufficient synchronization criterion for a general linear state error feedback controller is rigorously proven by means of linearization and Lyapunov’s direct methods. When a simple linear controller is used in the scheme, some easily implemented algebraic synchronization conditions are derived based on the upper and lower bounds of the master chaotic system. These criteria are further optimized to improve their sharpness. The optimized criteria are then applied to four typical generalized Lorenz systems, i.e. the classical Lorenz system, the Chen system, the Lü system and a unified chaotic system, obtaining precise corresponding synchronization conditions. The advantages of the new criteria are revealed by analytically and numerically comparing their sharpness with that of the known criteria existing in the literature.
The aim of this note is two-fold. First, it discusses synchronization of chaotic systems from a control theoretic point of view and introduces the concept of secure synchronization, i.e., a communication scheme that resists possible intrusion based on either adaptive or robust control techniques. Second, for a large class of chaotic systems, i.e., the generalized Lorenz system, global exponential synchronization via a scalar communication signal is suggested and its security is analyzed from a control theoretic viewpoint. Both theoretical analysis and numerical simulations are provided, verifying the proposed chaos-synchronization-based secure communication design principle and methodology.
This paper is to reduce the contact impact, control the leg stiffness and bouncing height. Firstly, the combining position/force active compliance control was involved in the deceleration phase to decrease the impact force and improve the leg compliance capacity. Then a reasonable velocity control of cylinder was addressed to control the bouncing height to the given value in the acceleration phase. Due to the model uncertainties and disturbances in the deceleration and acceleration phase, a near inverse like controller with a proportional and differential control (PD) was added into the velocity control of acceleration phase to compensate the bouncing height control error. Finally, the effectiveness of proposed controller was validated by experiments. Experimental results showed the impact force could be reduced effectively and a significant bouncing height control performance could be achieved. The influences of initial energy, preload of spring and velocity of cylinder on the bouncing height were addressed as well.
Based on the closed operational laws in picture fuzzy numbers and strict triangular norms, we extend the Bonferroni mean (BM) operator under the picture fuzzy environment to propose the picture fuzzy interactional Bonferroni mean (PFIBM), picture fuzzy interactional weighted Bonferroni mean (PFIWBM), and picture fuzzy interactional normalized weighted Bonferroni mean (PFINWBM) operators. We prove the monotonicity, idempotency, boundedness, and commutativity for the PFIBM and PFINWBM operators. We also establish a novel multi-criteria decision making (MCDM) method under the picture fuzzy environment by applying the PFINWBM operator. Furthermore, we apply our MCDM method to the enterprise resource planning (ERP) systems selection. The comparative results for our MCDM method induced by six classes of well-known triangular norms ensure that the best selection is always the same ERP system. Therefore, our MCDM method is effective for dealing with the picture fuzzy MCDM problems.
In this paper, a time-delayed chaos control method based on repetitive learning is proposed. A general repetitive learning control structure based on the invariant manifold of chaotic system is given. The integration of the repetitive learning control principle and the time-delayed chaos control technique enables adaptive learning of appropriate control actions from learning cycles. In contrast to the conventional repetitive learning control, no exact knowledge (analytic representation) of the target periodic orbits is needed, except for the time delay constant, which can be identified via either experiments or adaptive learning. The controller effectively stabilizes the states of the continuous-time chaos on desired unstable periodic orbits. Simulations on Duffing and Lorenz chaos are provided to verify the design and analysis.
A new model called Naming Game with Multiple Hearers (NGMH) is proposed in this paper. A naming game over a population of individuals aims to reach consensus on the name of an object through pair-wise local interactions among all the individuals. The proposed NGMH model describes the learning process of a new word, in a population with one speaker and multiple hearers, at each interaction towards convergence. The characteristics of NGMH are examined on three types of network topologies, namely ER random-graph network, WS small-world network, and BA scale-free network. Comparative analysis on the convergence time is performed, revealing that the topology with a larger average (node) degree can reach consensus faster than the others over the same population. It is found that, for a homogeneous network, the average degree is the limiting value of the number of hearers, which reduces the individual ability of learning new words, consequently decreasing the convergence time; for a scale-free network, this limiting value is the deviation of the average degree. It is also found that a network with a larger clustering coefficient takes longer time to converge; especially a small-word network with smallest rewiring possibility takes longest time to reach convergence. As more new nodes are being added to scale-free networks with different degree distributions, their convergence time appears to be robust against the network-size variation. Most new findings reported in this paper are different from that of the single-speaker/single-hearer naming games documented in the literature.
For the modified Camassa–Holm equation, by using the methodologies of dynamical systems and singular traveling wave theory developed by Li and Chen [2007] to its corresponding traveling wave system, under different parameter conditions, all possible exact explicit bounded solutions (solitary wave solutions, periodic wave solutions, peakon, periodic peakons, as well as compactons) are obtained. More than 23 explicit exact parametric representations of the above-mentioned traveling wave system are presented.
Complex dynamic evolution may originate from parameter-controlled bifurcation or initial-condition-oriented multistability. Offset boosting of any attractor in a system can be realized by directly introducing a constant or by nudging an initial condition, which sets up a bridge between bifurcation and multistability. As an effective tool, designing a piecewise-linear function is fundamentally important for transporting any desired attractor to an initial-condition-controlled phase space. In this letter, the principle and methodology of dynamic transport is studied in detail.
In this paper, a general two-neuron model with distributed delays and a strong kernel is investigated. By applying the frequency domain approach and analyzing the associated characteristic equation, the existence of bifurcation parameter for the model is determined. Furthermore, if the mean delay used as a bifurcation parameter, it is found that Hopf bifurcation occurs for the strong kernel. This means that a family of periodic solutions bifurcates from the equilibrium when the bifurcation parameter exceeds a critical value. The direction and stability of the bifurcating periodic solutions are determined by the Nyquist criterion and the graphical Hopf bifurcation theorem. Some numerical simulations are given to justify the theoretical analysis results.
