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The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters,...
In this paper, the concept of coordination is introduced within the framework of two-level large-scale systems and a new fuzzy approach based on Interaction Balance Principle, in conjunction with Tamura's optimization method, is used for coordination of overall system. For this purpose, the overall optimization problem is first decomposed into m sub-problems, where each is optimized using Tamura's...
In this paper, decentralized control algorithms for cooperative multi-agent manipulation systems are developed. To control the positions of the agents and the exerted forces on the object in the presence of uncertainties in the dynamics of the agents, two different methods are considered. In the first approach, robust control of the system is proposed. Using the Lyapunov stability method, the convergence...
In this paper, an adaptive fuzzy sliding mode controller using multiple models approach is presented. By using the multiple models technique the nominal part of the control signal is constructed according to the most appropriate model at different environments. Adaptive single-input single-output (SISO) fuzzy system is used to approximate the discontinuous part of control signal; control gain, in...
In this paper, a new approach for online reactive path generation and control of multi-agent systems is proposed. This method is based on local optimization techniques used for solving the inverse kinematic problem of redundant manipulators. Convergence of the agents' velocities to the desired values in the null-space of the primary task is guaranteed by introducing a new control law. The efficacy...
Control of a class of uncertain nonlinear systems which estimates unavailable state variables is considered. A new approach for robust tracking control problem of satellite for large rotational maneuvers is presented in this paper. The features of this approach include - a strong algorithm to estimate attitude, based on discrete extended Kalman filter combined with a continuous extended Kalman filter...
The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. In this paper, an adaptive algorithm for tracking maneuvering targets based on neural networks is proposed. This algorithm is implemented with two filters based on the current statistical model and a multilayer feedforward neural network. The two filters track the same maneuvering...
In this paper, an adaptive multi-model CMAC-based controller (AMCBC) in conjunction with a supervisory controller is developed for uncertain nonlinear MIMO systems. AMCBC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple CMAC neural networks. With the help of a supervisory controller, the resulting close-loop system is globally stable. The...
In this paper, the concept of coordination is introduced within the framework of two-level large-scale systems and a new approach based on interaction prediction principle is presented. The proposed approach is formulated in an intelligent manner in such a way that it provides a new strategy that can be used to synthesize an on-line supervisory controller for the overall two-level large-scale systems,...
In this paper, an adaptive multi-model controller based on CMAC neural networks (AMNNC) is developed for uncertain nonlinear MIMO systems. AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks is used to approximate the system nonlinearity for a given task. The proposed control...
Coordination strategies in large-scale systems are mainly based on two principles; interaction prediction principle and interaction balance principle. In this paper, which consists of two parts, the concept of coordination is introduced within the framework of two-level large-scale systems, and two new approaches for coordination based on interaction prediction principle and interaction balance principle...
In this paper, a new approach based on generalized regression neural networks (GRNNs) has been proposed to predict the unsteady forces and moments on a 70deg swept wing undergoing sinusoidal pitching motion. Extensive wind tunnel testing results were being used for training the network and also for verification of the values predicted by this approach. The generalized regression neural network (GRNN)...
In this paper, an adaptive neural network multiple models sliding mode controller for robotic manipulators is presented. The proposed approach remedies the previous problems met in practical implementation of classical sliding mode controllers. Adaptive single-input single-output (SISO) RBF neural networks are used to calculate each element of the control gain vector; discontinuous part of control...
In this paper, a neural network decentralized control for trajectory tracking of robot manipulators is developed. The proposed decentralized control allows the overall closed-loop system to be stabilized while making the tracking error to be uniformly ultimately bounded (UUB), without having any prior knowledge of the robot manipulator dynamics. The interconnections in the dynamic equations of each...
In this paper, which consists of two parts, a new two-level computational algorithm is used for nonlinear optimal control of large-scale systems. The two-level optimizer uses a new coordination strategy which is based on the gradient of interaction errors instead of the gradient of overall performance function. The advantages of the new method can be categorized into two parts: first, the new formulation...
In this paper an adaptive fuzzy decentralized control algorithm for trajectory tracking of robot manipulators is developed. The proposed decentralized control algorithm allows the overall closed-loop system to be stabilized while making the tracking error to be uniformly ultimately bounded (UUB), without having any prior knowledge of the robot manipulator dynamics. The interconnections in the dynamic...
We consider the problem of minimizing rank of a matrix under linear and nonlinear matrix inequality constraints. This problem arises in diverse applications such as estimation, control and signal processing and it is known to be computationally NP-hard even when constraints are linear. In this paper, we first formulize the RMP as an optimization problem with linear objective and simple nonlinear semialgebraic...
In this paper a decentralized control scheme for multiple cooperative manipulators is developed to achieve the desired performance in motion and force tracking, in the presence of uncertainties in dynamic equations of the robots. To eliminate the effects of uncertainties in the closed-loop performance, a new adaptive control algorithm is proposed. Based on the Lyapunov stability method, it is proved...
In this part, similar to Part I of this paper, a new two-level method for nonlinear optimal control of large scale systems is introduced. This approach is based on interaction balance principle for coordination of large-scale systems. In the first level, the optimization problems are solved for nonlinear dynamics using a gradient method, and in the second level, the coordination is done using the...
In this paper, an adaptive neural network sliding mode controller (ANNSMC) for robotic manipulators is proposed to alleviate the problems met in practical implementation using classical sliding mode controllers. The chattering phenomenon is eliminated by substituting single-input single-output radial-basis-function neural networks (RBFNN's), which are nonlinear and continuous, in lieu of the discontinuous...
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