The sensitivity of a class of N-th order time-multiplied performance costs for discrete-time optimal output feedback systems is investigated. Sensitivity matrices for optimal costs are evaluated with respect to variat...
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The sensitivity of a class of N-th order time-multiplied performance costs for discrete-time optimal output feedback systems is investigated. Sensitivity matrices for optimal costs are evaluated with respect to variations in system parameters and design variables. The derivation of sensitivity matrices can be readily obtained by considering the Lagrangian of the performance index. It is shown that while faster system response performance can be obtained by designing with higher order time-multiplying factor in the performance index, the design also increases the optimum cost of control and absolute cost sensitivity, as can be expected.
This paper extends the familiar 1-D concepts of observer designs to the design of observers for 2-D systems described by Roesser's model. Both full-order and reduced-order observers are considered. Extensions of 1...
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This paper extends the familiar 1-D concepts of observer designs to the design of observers for 2-D systems described by Roesser's model. Both full-order and reduced-order observers are considered. Extensions of 1-D concepts to 2-D is non-trivial in view of the requirement that state transformations for 2-D systems have to be block diagonal in order to preserve their input-output properties. Another issue addressed in this paper is the required asymptotic stability property of the 2-D observers. To maintain tractability in asymptotic stability analysis, we consider the class of 2-D observers with separable characteristic polynomials. Illustrative examples are provided.
A compact Lagrangian formulation has been developed and discussed to deal with the highly coupled non-linear dynamic equations of robotic manipulators. It bridges the dynamic and kinematic problems of robotics closely...
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A compact Lagrangian formulation has been developed and discussed to deal with the highly coupled non-linear dynamic equations of robotic manipulators. It bridges the dynamic and kinematic problems of robotics closely together by means of Jacobian and subjacobian matrices. Its numeric computational complexity has been reduced to O(n2) time. When n<6, the number of operations required for computing all joint torques is almost close to that of Newton-Euler approach. Due to its significant insight of the robot behavior, it is concluded that the compact Lagrangian formulation offers a convenient approach to building up a feasible real-time adaptive control strategy for computer-based manipulators. Finally, it has been found that all information required for solving the dynamic equation and the adaptive control problems is concentrated in Hessian matrix of the kinetic energy for a given robotic manipulator.
The design and implementation of a controller for a six-degree-off-freedom robot manipulator is presented. Two IBM Personal computers were used to implement the preliminary scheme reported here where execution of prog...
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The design and implementation of a controller for a six-degree-off-freedom robot manipulator is presented. Two IBM Personal computers were used to implement the preliminary scheme reported here where execution of programmed motion can be carried out in either point-to-point or straight line mode.
The problem of vibration isolation is investigated from the standpoint of modern control and optimization theory. The proposed suspension design was verified experimentally by means of a micromputerized suspension mod...
The problem of vibration isolation is investigated from the standpoint of modern control and optimization theory. The proposed suspension design was verified experimentally by means of a micromputerized suspension model. The experimental results are very encouraging and indicate promising potential application of the proposed scheme to real-world systems. This paper describes the modeling and formulation of an optimal suspension design and the implementation aspects of the proposed microcomputerized optimal suspension scheme.
The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount ...
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The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to new prospects for deep learning. However, this data is confined within data silos and contains sensitive information, making it difficult to be processed in a centralized manner, particularly under stringent data privacy regulations. Federated learning (FL) offers a solution by enabling collaborative learning while ensuring privacy. Nonetheless, data and device heterogeneity complicate FL implementation. This research presents a specialized FL algorithm for heterogeneous edge computing. It integrates a lightweight grouping strategy for homogeneous devices, a scheduling algorithm within groups, and a Split Learning (SL) approach. These contributions enhance model accuracy and training speed, alleviate the burden on resource-constrained devices, and strengthen privacy. Experimental results demonstrate that the GSFL outperforms FedAvg and SplitFed by 6.53× and 1.18×. Under experimental conditions with \(\alpha=0.05\), representing a highly heterogeneous data distribution typical of extreme Non-IID scenarios, GSFL showed better accuracy compared to FedAvg by 10.64%, HACCS by 4.53%, and Cluster-HSFL by 1.16%. GSFL effectively balances privacy protection and computational efficiency for real-world applications in mobile multimedia communications.
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