Control
Farideh Azadmanesh; Reza Ghasemi
Abstract
Different types of optimal leader-follower consensus of high-order multi-agent systems (MAS) under fixed, connected, and directed communication topology are presented in this paper. The dynamics of each agent including the followers and their corresponding leader is a linear high order system. First, ...
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Different types of optimal leader-follower consensus of high-order multi-agent systems (MAS) under fixed, connected, and directed communication topology are presented in this paper. The dynamics of each agent including the followers and their corresponding leader is a linear high order system. First, the Linear Quadratic Regulator (LQR) problem is discussed to achieve the optimal consensus for high-order linear MAS with a guaranteed predefined phase and gain margin. Then stochastic leader-follower consensus problem of MAS in the presence of the Gaussian noise is designed. To tackle these problems, a set of fixed distributed control laws for each follower agent is designed, based on algebraic graph theory. Simulation results indicate the effectiveness of the proposed method and display the consensus in both cases via distributed control laws.
Control
Mohammad Mehdi Zohrei; Hamid Reza Javanmardi
Abstract
For decades in the aerospace and control sciences, the Inertial Stabilized Platform (ISP) system has been studied to improve the accuracy of recipient photos or target tracking. This paper presents a nonlinear observer-based control method for three Degrees Of Freedom (3-DOF) ISP systems. First, a new ...
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For decades in the aerospace and control sciences, the Inertial Stabilized Platform (ISP) system has been studied to improve the accuracy of recipient photos or target tracking. This paper presents a nonlinear observer-based control method for three Degrees Of Freedom (3-DOF) ISP systems. First, a new formula of the state space equation for the 3-DOF ISP system is proposed to make this model suitable for designing an observer-based control. Then, by measuring the angular positions as output feedback, the angular velocities are estimated by the nonlinear observer, and Lyapunov-based nonlinear control techniques are used to design the observer. Furthermore, the exponential stability and convergence of the observer system are proved. Finally, the auxiliary control signal is considered so that the dynamics of the designed observer become a simple linear form and are easily controlled by the state feedback controller. Simulation results illustrate the effectiveness and feasibility of the proposed control strategy.
Control
Mats Leon Richter; Leila Malihi; Anne-Kathrin Patricia Windler; Ulf Krumnack
Abstract
The predictive performance of a neural network depends on the one hand on the difficulty of a problem, defined by the number of classes and complexity of the visual domain, and on the other hand on the capacity of the model, determined by the number of parameters and its structure. By applying layer ...
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The predictive performance of a neural network depends on the one hand on the difficulty of a problem, defined by the number of classes and complexity of the visual domain, and on the other hand on the capacity of the model, determined by the number of parameters and its structure. By applying layer saturation and logistic regression probes, we confirm that these factors influence the inference process in an antagonistic manner. This analysis allows the detection of over- and under-parameterization of convolutional neural networks. We show that the observed effects are independent of previously reported pathological patterns, like the “tail pattern”. In addition, we study the emergence of saturation patterns during training, showing that saturation patterns emerge early in the optimization process. This allows for quick detection of problems and potentially decreased cycle time during experiments. We also demonstrate that the emergence of tail patterns is independent of the capacity of the networks. Finally, we show that information processing within a tail of unproductive layers is different, depending on the topology of the neural network architecture.
Control
Fatemeh Tavakkoli; Alireza Khosravi; Pouria Sarhadi
Abstract
This work represents a new method for robustness analysis of the model reference adaptive controller (MRAC) in the presence of input saturation. Saturation is one of the nonlinear factors affecting the stability of control systems which must be considered in controller design and stability analysis experiments. ...
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This work represents a new method for robustness analysis of the model reference adaptive controller (MRAC) in the presence of input saturation. Saturation is one of the nonlinear factors affecting the stability of control systems which must be considered in controller design and stability analysis experiments. Various methods are presented for the stability and robustness analysis of adaptive control systems, and employment of describing function (DF) can be attractive and practical, due to the appropriate effectiveness of DF in estimating limit cycles and also the application of quasi-linearization theory. In this work, the stability analysis and a limit cycle estimation of a saturated system in the frequency domain are performed. The controller parameters are adjusted in a way that the system achieves its stable limit cycle in the presence of the initial conditions for the states. Moreover, the efficiency of the proposed method for second-order systems is reported in the presence of symmetric saturation and uncertainty model in Rohrs’s counterexample as the unmodeled dynamics. The results demonstrate the proposed method provides a proper analysis of system stability during the changes in the control parameters and the saturation amplitude.