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We present the control system synthesis for the multilink redundant manipulator. Our control system is based on the unique algorithm that includes the novel hybrid method for solving the inverse kinematics problem. This method combines ANFIS-network and iterative refinement. As a result, the control system has high integrative capabilities and is easy to modify for another construction. The manipulator...
This manuscript describes a methodology to design a controller for an artificial muscles type four fingers dexterous robotics hand. Electro-Active Polymers (EAP)materials have been used to construct the muscles type four fingers hand. The main dilemma in the control of artificial muscles type fingers, is to provide an accurate fingertips tracking within the operational space. Yet, if bending angles...
In this paper we describe the application of genetic algorithms for optimal type-2 fuzzy system design. We illustrate the approach with two cases, one of designing optimal neural networks and the other of fuzzy control. Simulation results show the feasibility of the proposed approach of using hierarchical genetic algorithms for designing type-2 fuzzy systems.
This paper implement an online training of dynamic neural networks (NNs) for identification and control of permanent magnet synchronous motor (PMSM) servo system. Utilizing two multilayer feed-forward NNs, it makes no such assumptions. The two networks work in tandem to simultaneously achieve system identification and adaptive control. The proposed control system is designed and its effectiveness...
In this work is presented a design and implementation of an intelligent controller applied to dynamic systems. The main objective is to design a management intelligent system that acts in the controllers of Direct current machine (DC) and thermo-eletric control systems drives. The purpose is to evaluate the performance of algorithms for intelligent control that integrate DC and thermal actuators to...
This paper describes an Adaptive Backstepping Neural Network (ABNN) method used for a ship course tracking control. The ship model is described by a third order nonlinear model whose parameters are unknown. The control design uses estimate values of unknown parameters of the system. Then, adaptive laws of the estimation of these values have been proposed. The stability of the controlled system has...
In this paper, we apply an adaptive control algorithm to a nonlinear multivariable process. Such controller is based on the multiple models approach. As the design of the control law requires the knowledge of the dynamical model of the system, we deal firstly with the identification of the system parameters using the recursive least squares and the retro propagation of the gradient algorithms. Then,...
A novel approach is promoted for fuzzy neural ship controllers. A RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network...
This research work presents supervised Artificial Intelligence based control technique for an inverted pendulum. The inverted pendulum system is a classic control problem that is used in research. It is a suitable process to test prototype controllers due to its high non-linearities and lack of stability. Most traditional controllers (feedback linearisation, rule based control) are based around an...
The motorcycle model which has two degrees of freedom (2-DOF for short) is adopted to study the characteristics of four-wheel steering (4WS for short) vehicle, and the linear 2-DOF dynamic equation of 4WS vehicle is constructed in this paper. A neural network direct inverse controller is designed to control the steering system of 4WS through the offline identification and online learning processes...
In the view of the worsening congestion situation of freeway and related urban expressway. Based on fuzzy metering and neural network theories. A fuzzy control method which chooses d-value of mainline traffic state and expected state and ramp traffic state as input variables, ramp metering rate as output variable was raised. accordingly a ramp metering algorithm was put forward to establish a five-layer...
For a class of nonlinear networked control system (NCS) with network-induced delay and packed dropout, which is influenced by external disturbance with limited energy in transmission, a T-S fuzzy model is employed to represent the nonlinear controlled plant. By using appreciate Lyapunov-Krasovskii function, a H∞ integrality sufficient condition against actuator failures is derived based on delay-dependent...
Aimed at the controlled object's character of long delay-time, this paper presents a strategy that the long delay-time object is remodeled into short delay-time object through decreasing time-delay control, after the adoption of BP neural network PID controller to control the transformed system. In this paper, the three-layer BP neural network is designed and BP neural network PID controller algorithm...
A novel compound controller is developed for the vehicle active suspension with an electro-hydraulic actuator. The models of the actuator and the quarter vehicle are both first established. A compound controller is designed to utilize RBF neural network approximate the output of PID, and to combine PID algorithm with a RBF neural network output as its control laws. This controller synthesizes both...
Reformulated principle of fault estimation design for one class of first order continuous-time nonlinear system is treated in this paper, where a neural network is regarded as model-free fault approximator. The problem addressed is presented as approach based on sliding mode methodology with combination of radial basis function neural network to design robust nonlinear fault estimation. The method...
This paper presents a neural network controller for multi-level inverter based Static Synchronous Series Compensator (SSSC) for a multi-machine power system. The neural network controller is designed using back propagation network algorithm. Back Propagation algorithm is employed to update weights for the design of the proposed controller. The proposed neural network controller is capable of providing...
Tracking control of nonlinear uncertain Chua's chaotic systems is studied. Based on coordinate transform, the paper deduced the principle with which Chua's chaotic system can be translated into the so-called general strict-feedback form. Combining the back stepping method with robust control technology, an adaptive parameter control law is developed and thus the output tracking is successfully accomplished...
This review gives an overall introduction of the artificial evolution mechanism. It presents the main strategies for robotic controller design. Various applications of artificial evolution in robotics are surveyed and classified. Open issues and future prospects are given.
The performance comparison between two controllers, namely Adaptive Neuro-Controller (ANC), based on Multi Layered Perceptron (MLP) network and Adaptive Parametric Black Box Controller (APBBC) are presented in this paper. The comparison is based on the capability of the controlled output tracking the model reference output and the percentage of overshoot. Both controllers are based on a black box...
Given a task of designing controller for mobile robots in swarms, one might wonder which distributed control paradigms should be selected. Until now, paradigms of robot controllers have been within either behaviour based control or neural network based control, which have been recognized as two mainstreams of controller design for mobile robots. However, in swarm robotics, it is not clear how to determine...
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