摘要：We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties.It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center in a finite time.Moreover,we extend our results to more general attraction/repulsion functions.Numerical simulations demonstrate that all agents will eventually enter into and remain in a bounded region around the swarm center which may exhibit complex spiral motion due to asymmetry of the coupling structure.The model in this paper is more general than isotropic swarms and our results provide further insight into the effect of the interaction pattern on individual motion in a swarm system.

摘要：A delay-dependent H-infinity control for descriptor systems with a state-delay is investigated.The purpose of the problem is to design a linear memoryless state-feedback controller such that the resulting closed-loop system is regular,impulse free and stable with an H-infinity norm bound.Firstly,a delay-dependent bounded real lemma(BRL) of the time-delay descriptor systems is presented in terms of linear matrix inequalities(LMIs) by using a descriptor model transformation of the system and by taking a new Lyapunov-Krasovsii functional.The introduced functional does not require bounding for cross terms,so it has less conservation.Secondly,with the help of the obtained bounded real lemma,a sufficient condition for the existence of a new delay-dependent H-infinity state-feedback controller is shown in terms of nonlinear matrix inequalities and the solvability of the problem can be obtained by using an iterative algorithm involving convex optimization.Finally,numerical examples are given to demonstrate the effectiveness of the new method presented.

摘要：This paper considers an anisotropic swarm model with a class of attraction and repulsion functions. It is shown that the members of the swarm will aggregate and eventually form a cohesive cluster of finite size around the swarm center. Moreover,It is also proved that under certain conditions, the swarm system can be completely stable, i. e., every solution converges to the equilibrium points of the system. The model and results of this paper extend a recent work on isotropic swarms to more general cases and provide further insight into the effect of the interaction pattern on self-organized motion in a swarm system.

摘要：A new dynamic terminal sliding mode control (DTSMC) technique is proposed for a class of single-input and single-output (SISO) uncertain nonlinear systems. The dynamic terminal sliding mode controller is formulated based on Lyapunov theory such that the existence of the sliding phase of the closed-loop control system can be guaranteed, chattering phenomenon caused by the switching control action can be eliminated, and high precision performance is realized.Moreover, by designing terminal equation, the output tracking error converges to zero in finite time, the reaching phase of DSMC is eliminated and global robustness is obtained. The simulation results for an inverted pendulum are given to demonstrate the properties of the proposed method.

摘要：In this paper a comprehensive introduction for modeling and control of networked evolutionary games (NEGs) via semi-tensor product (STP) approach is presented. First, we review the mathematical model of an NEG, which consists of three ingredients:network graph, fundamental network game, and strategy updating rule. Three kinds of network graphs are considered, which are i) undirected graph for symmetric games;ii) directed graph for asymmetric games, and iii) d-directed graph for symmetric games with partial neighborhood information. Three kinds of fundamental evolutionary games (FEGs) are discussed, which are i) two strategies and symmetric (S-2); ii) two strategies and asymmetric (A-2); and iii) three strategies and symmetric (S-3). Three strategy updating rules (SUR) are introduced, which are i) Unconditional Imitation (UI);ii) Fermi Rule(FR);iii) Myopic Best Response Adjustment Rule (MBRA). First, we review the fundamental evolutionary equation (FEE) and use it to construct network profile dynamics (NPD)of NEGs. To show how the dynamics of an NEG can be modeled as a discrete time dynamics within an algebraic state space, the fundamental evolutionary equation (FEE) of each player is discussed. Using FEEs, the network strategy profile dynamics (NSPD) is built by providing efficient algorithms. Finally, we consider three more complicated NEGs:i) NEG with different length historical information, ii) NEG with multi-species, and iii) NEG with time-varying payoffs. In all the cases, formulas are provided to construct the corresponding NSPDs. Using these NSPDs, certain properties are explored. Examples are presented to demonstrate the model constructing method, analysis and control design technique, and to reveal certain dynamic behaviors of NEGs.

摘要：A multi-local-world model is introduced to describe the evolving networks that have a localization property such as the Intemet. Based on this model, we show that the traffic load defined by "betweenness centrality" on the multi-local-world scale-free networks' model also follows a power law form. In this kind of network, a few vertices have heavier loads and so play more important roles than the others in the network.

摘要：For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential (PID) controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.

摘要：A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm (ASA).For a given control system with this kind of PID controller,a group of optimal PID controller parameters K*p,T*i, and T*d can be obtained by taking the overshoot,settling time,and steady-state error of the system's unit step response as the performance indexes and by use of our improved ant system algorithm.K*p,T*i, and T*d can be used in real-time control.This kind of controller is called the ASA-PID controller with incomplete derivation.To verify the performance of the ASA-PID controller,three different typical transfer functions were tested,and three existing typical tuning methods of PID controller parameters,including the Ziegler-Nichols method (ZN),the genetic algorithm (GA),and the simulated annealing (SA),were adopted for comparison.The simulation results showed that the ASA-PID controller can be used to control different objects and has better performance compared with the ZN-PID and GA-PID controllers,and comparable performance compared with the SA-PID controller.

摘要：Large-scale wind turbine generator systems have strong nonlinear multivariable characteristics with many uncertain factors and disturbances.Automatic control is crucial for the efficiency and reliability of wind turbines.On the basis of simplified and proper model of variable speed variable pitch wind turbines,the effective wind speed is estimated using extended Kalman filter.Intelligent control schemes proposed in the paper include two loops which operate in synchronism with each other.At below-rated wind speed,the inner loop adopts adaptive fuzzy control based on variable universe for generator torque regulation to realize maximum wind energy capture.At above-rated wind speed, a controller based on least square support vector machine is proposed to adjust pitch angle and keep rated output power.The simulation shows the effectiveness of the intelligent control.

摘要：Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x=sin(2/x)with infinite collapses within the finite region[-1,1] to some representative iterative chaotic maps with finite collapses(e.g.,Logistic map,Tent map,and Chebyshev map),a new adaptive mutative scale chaos optimization algorithm (AMSCOA)is proposed by using the chaos model x=sin(2/x).In the optimization algorithm,in order to ensure its advantage of speed convergence and high precision in the seeking optimization process,some measures are taken:1)the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly;2)the most circle time is regarded as its control guideline.The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.

摘要：In this work,a fast and accurate stationary alignment method for strapdown inertial navigation system (SINS) is proposed.It has been demonstrated that the stationary alignment of SINS can be improved by employing the multiposition technique,but the alignment time of the azimuth error is relatively longer.Over here,the two-position alignment principle is presented.On the basis of this SINS error model,a fast estimation algorithm of the azimuth error for the initial alignment of SINS on stationary base is derived fully from the horizontal velocity outputs and the output rates,and the novel azimuth error estimation algorithm is used for the two-position alignment.Consequently,the speed and accuracy of the SINS's initial alignment is enhanced greatly.The computer simulation results illustrate the efficiency of this alignment method.

摘要：The guaranteed cost control problem for networked control systems (NCSs) is addressed under communication constraints and varying sampling rate. First of all, a simple information-scheduling scheme is presented to describe the scheduling approach of system signals in NCSs. Then, based on such a scheme and given sampling method, the design procedure in dynamic output feedback manner is also derived which renders the closed loop system to be asymptotically stable and guarantees an upper bound of the LQ performance cost function.

摘要：This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from multi-agent dynamical systems, where pinning control is used to make all the agents synchronize to the state of a command generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents’ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.