There is no doubt that the members of a society can influence each other and a minority of them may guide, in some situations, the whole society toward a particular opinion. However, the promoted opinion is not always desirable, and in some cases, it is desired to prevent its propagation in the network. This mission can be done with the help of informed agents that are common agents that act as hidden advertisers. In this article, we will discuss how many and which agents should be selected as informed agents in a way that the final opinions of agents satisfy some given constraints. In the line of solving this problem, the notion of equilibratability is considered, and the problem is formulated as minimizing the zero-norm of available solutions. Knowing that the mentioned problem is NP-hard, some relaxation methods are considered to solve this problem. The efficiency of the proposed methods is investigated for selecting the sparsest solution in a well-known graph. Finally, the required informed agents to resist a group of spreaders in some random graphs [generated with different methods, such as Erdös and Rényi (ER), Watts and Strogatz (WS), and Barabási and Albert (BA)] are compared, and some reasonable results are observed.

In this paper, we answer two interleaved questions. The first one is, having a redundant serial manipulator with a given cyclic task, how can we benefit simultaneously from both natural dynamics modification (NDM) and kinematic redundancy resolution to reduce the actuators' torque? Here, the NDM is done by devising parallel nonlinear monoarticular compliances (MACs), which span one joint, and nonlinear biarticular compliances (BACs), which pass over two joints. We take advantage of kinematic redundancy to exploit the robot's natural dynamics. The second question is how do kinematic redundancy resolution and the NDM interact to minimize the cost? To answer these questions, we cast the problem of simultaneous modification and exploitation of natural dynamics into a constrained multiobjective optimization problem. We show that the set of optimal compliances has an analytical solution as a parametric function of joint trajectories. Accordingly, we study how the components of cost function affect the profile of optimal compliant elements. The proposed method is implemented on a simulated planar 3-DoF manipulator and a simulated nonplanar 4-DoF manipulator for three different tasks. The results shed light on how kinematic redundancy resolution influences efficiency of using MACs and BACs and, consequently, increases attainable gains from the NDM. Moreover, analysis of the results specifies the roles of mono- and BACs and especially explains the reason behind the particular importance of having BACs to reduce the actuation cost.

Bouncing, balancing and swinging the leg forward can be considered as three basic control tasks for bipedal locomotion. Defining the trunk by an unstable inverted pendulum, balancing as being translated to trunk stabilization is the main focus of this paper. The control strategy is to generate a hip torque to have upright trunk to achieve robust hopping and running. It relies on the Virtual Pendulum (VP) concept which is recently proposed for trunk stabilization, based on human/animal locomotion analysis. Based on this concept, a control approach, named Virtual Pendulum Posture control (VPPC) is presented, in which the trunk is stabilized by redirecting the ground reaction force to a virtual support point. The required torques patterns generated by the controller, could partially be exerted by elastic structures like hip springs. Hybrid Zero Dynamics (HZD) control approach is also applied as an exact method of keeping the trunk upright. Stability of the motion which is investigated by Poincaré map analysis could be achieved by hip springs, VPPC and HZD. The results show that hip springs, revealing muscle properties, could facilitate trunk stabilization. Compliance in hip produces acceptable performance and robustness compared with VPPC and HZD, while it is a passive structure.

This paper presents a new control approach to achieve robust hopping with upright trunk in the sagittal plane. It relies on an innovative concept for trunk stabilization, called Virtual Pendulum concept, recently proposed, based on experimental finding in animal locomotion. With this concept, the trunk is stabilized by redirecting the ground reaction force to a virtual support point, named Virtual Pivot Point (VPP). This concept is combined with a new leg adjustment scheme to induce stable hopping when an extended trunk is added to SLIP model. The stability is investigated by Poincaré map analysis. With fixed VPP position, stability, disturbance rejection and moderate robustness are achieved, but with low convergence speed. To improve the performances and attain higher robustness, event based control of VPP position is introduced, using feedback of the system state at apex. Dead beat control and Discrete LQR are alternatively considered to adjust the feedback gains. In both cases, considerable enhancements with respect to stability, convergence speed and robustness against perturbations are achieved.

Application of the compliance to increase the robustness of the bipedal walker locomotion is the main target of this paper. The control of bipedal robots with point feet is one of the most challenging problems in the domain of hybrid dynamical systems. In order to make an exponentially stable periodic walking for an Acrobot as a planar biped with only one degree of freedom, a controller is designed based on the hybrid zero dynamics analysis. Then, through appropriate alterations in robot structure, the controller complexities are reduced which result in fewer parameters for tuning. Improvement of the robot structure via compliance insertion not only compensates the lower degree of freedom of the control design process, but also expands the domain of stability of the closed-loop system. In this regard, a nonlinear damper is inserted between two legs of Acrobot to enable it to walk on a wider range of slopes. The main controller is designed for walking on a flat terrain and the compliance of the damper empowers it to make stable walking on slopes up to 17°. The simulation results confirmed the efficiency of the proposed approach.

This comment tries to describe a theoretical mistake made in the aforementioned paper [G. Feng,IEEE Trans. Fuzzy Syst., vol. 11, no. 5, pp. 605-612, Oct. 2003] to formulate the inverse of matrices used to construct the piecewise-quadratic Lyapunov functions. Derivation of these inverse matrices is the most critical step toward transforming the design constraints into linear matrix inequalities (LMIs). Therefore, the erroneous formulation essentially affects the validity of the approach and final results. Unfortunately, it seems that there is no simple correction for this problem. However, some close alternative approaches are suggested by the same authors in their other works.

This paper presents a constructive Takagi-Sugeno fuzzy modeling method for a general class of nonlinear systems. This method is particularly suitable for stability analysis based on piecewise quadratic Lyapunov functions. The modeling error is appropriately inserted into the model and an algorithm is proposed to automatically determine the model parameters to keep the modeling error smaller than a desired upper bound. Based on the constructed fuzzy model, exponential stability analysis is performed and the stability constraints are transformed into linear matrix inequalities. Modeling error is also included in the stability analysis to validate the results for the original nonlinear system. The way to utilize the modeling method and stability analysis to systematically find a Lyapunov function for a nonlinear system is demonstrated via an example and the potential capability of the method in estimating the domain of attraction is discussed.

This paper employs two types of neural networks to control a single-link flexible arm. To train each network, we utilize a gradient-based approach with adaptive learning rate. We first apply the diagonal recurrent neural network (DRNN) to a single-link flexible arm, which is a challenging control problem, in order to investigate the ability of this type of recurrent neural network. We then apply a feed-forward neural network (FFNN) to this problem and perform some case studies for the purpose of performance comparisons of the two structures. Several simulations presented in this paper verify that the DRNN-based controller significantly improves the precision of the tip motion tracking, suppresses the tip deflections of the manipulator more effectively and simultaneously produces more appropriate control voltages.

In this paper we present a linear time invariant controller for bilateral teleoperation of a pair of N-DOF linear robotic systems under constant time delay. Our framework uses position and velocity signals to achieve position tracking in free motion. It also uses force feedback for compensating the human/environment disturbances to achieve position coordination in contact tasks. In passifying position and velocity feedback terms, for simplicity reasons there is no need to consider the dynamics of the robotic systems as long as they satisfy Euler-Lagrangian dynamic system equation. However when the force feedback is applied, we have to consider the dynamics of the robot in controller design. We do passivity analysis in the frequency domain by using Parseval’s identity. Simulation has been performed to illustrate the proposed algorithm and to clarify some points in the design procedure.

This paper presents a systematic design procedure of fuzzy controllers for exponentially stabilizing affine nonlinear systems, which are subject to vanishing perturbations. The modeling error, as a key factor in the validity of the stability analysis is also taken into consideration. A unified framework is presented, dealing with the modeling error as the system perturbation. It is shown that the constraints imposed on the modeling error are always satisfied for affine nonlinear systems, when they are modeled by Takagi- Sugeno fuzzy model through currently available methods. The constraints required to guarantee the exponential stability of the original nonlinear system and design the controller are transformed into LMIs. Finally, the way to utilize the presented method for a stabilizing problem is demonstrated using two examples.

In this paper, a new approach for image compression is presented. The enhanced SOM algorithm applies a vigilance parameter in order to test if the maximum value of all activation functions in each training step exceeds the minimum threshold. If vigilance test is not passed, a new cluster will be added to the network; else the winner cluster will be updated. Therefore, the network could be extendable due to pattern distribution. The proposed approach is compared with ART1. The performance of enhanced SOM algorithm does not depend on the input presentation order. As observed through simulations, the new algorithm with vigilance parameter reduces the computational complexity, and presents better quality compared with Kohonen's SOM.

Channel assignment problem in cellular communication is a difficult combinatorial optimization problem. There is no exact polynomial-time solution for it and searching the whole solution space is infeasible for large problems. By defining the problem's cost function as the energy function of a chaotic Hopfield neural network, we devise a framework for finding competitive suboptimal or even optimal solutions for combinatorial optimization problem in general, and channel assignment problem in particular. In our architecture, we inject chaotic noise in order to help the network escape from local minima of the energy function while we enforce problem constraints by external inputs of neurons. Experimental results show the superiority of our method to other methods.

In this paper we propose a reinforcement learning (RL) algorithm for packet routing in computer networks with emphasis on different traffic conditions. It is shown that routing with an RL approach, considering the traffic, can result in shorter delivery time and less congestion. A simple, but rational simulation of a computer network has also been tested and the suggested algorithm has been compared with other conventional ones. At the end, it is concluded that the suggested algorithm can perform packet routing efficiently with advantage of considering the dynamics in a real network.