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In this paper, a novel method for solving consensus problem in a multi agent system consisting of single link manipulators with flexible joint is presented. This method is based on linear matrix inequalities and the objective is to design a dynamic fixed order controller that can fulfill consensus by using output feedback information and Laplacian Matrix of the network of manipulators. The exact model of a single Link manipulator is assumed thus a nonlinear Lipchitz term emerges. Each manipulator as an agent in the corresponding network obtains only its neighbors output information therefore the controller is decentralized. To guarantee consensus in this method, first the multi agent system should become one augmented system. Then, based on considered conditions on nonlinear terms, using appropriate structure conversion is necessary. The unknown controller state space matrices of the closed loop system can be achieved by using Lyapunov stability theorem. Applying special conditions on symmetric positive definite matrix in Lyapunov quadratic function, results in an LMI form, thus iterative methods of solving nonlinear matrix inequalities with less accuracy is prevented. Finally, to demonstrate the effectiveness of this algorithm and compare with similar earlier researches, a numerical example on a multi agent system consisting of three single link flexible manipulators is investigated.

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This paper studies the consensus problem of nonlinear leader-following multi-agent systems (MAS). To do this, the error dynamics between the leader agent and follower ones are described via a Takagi-Sugeno (TS) fuzzy model. If the obtained TS fuzzy model is stable, then all of the nonlinear agents reach consensus. The consensus problem is investigated based on the parameterized or fuzzy Lyapunov function combined with a technique of introducing slack matrices. The slack matrices cause to decouple the Lyapunov matrices from systems ones and therefore, sufficient consensus conditions are obtained in terms of linear matrix inequalities (LMIs). The proposed slack matrices add an extra degree-of-freedom to the LMI conditions and also decrease the conservativeness of the LMI-based conditions. Finally, in order to illustrate the effectiveness and merits of the proposed method, a numerical example for the consensus problem of nonlinear leader-follower MAS with thirteen followers is solved.

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Vastness of operation airspace and uncertain environment in aerial search missions, makes utilizing multiple intelligent agents more preferable to integrated centralized systems due to robustness, parallel computing structure, scalability, and cost optimality of distributed systems. Cooperative search missions require the search space to be divided properly between agents. In order to minimize the uncertainty, the agents will calculate the best path in the assigned space partition. According to the communication topology, environmental information and the near-future decisions are shared between agents. In this paper, cooperative search using multiple UAVs has been considered. First, mathematical representation of the search space, kinematic and sensor model of UAVs, and communication topology have been presented. Then, an approach has been proposed to update and share information using the Bayes’ rule. Afterwards, path planning problem has been solved using different optimization algorithms namely First-order Gradient, Conjugate Gradient, Sequential Quadratic Programming, and Interior Point Algorithm. Finally, the performance of these algorithms have been compared according to mean uncertainty reduction and target detection time.