This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unkn...
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This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations *** uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the *** show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
Steering-by-wire(SBW) is a key technology to realize angle tracking for autonomous vehicles,and it directly affects the performance of active *** reliability performance of SBW system can be greatly improved by adopti...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
Steering-by-wire(SBW) is a key technology to realize angle tracking for autonomous vehicles,and it directly affects the performance of active *** reliability performance of SBW system can be greatly improved by adopting dual motor steering ***,the dual-motor SBW vehicle often loses its stability due to the external interference,and the asynchronous response between the dual steering actuator motors will further deteriorate the stability of the *** purpose of this paper is to establish a method of angle tracking for the dual-motor steering by wire ***,the model of SB W was established and its validity was proofed by comparing the steering performance between the SBW and the traditional mechanical steering ***,by considering parameters uncertainties and difference of wheel load torque of the dual steering actuator motors,the steering angle tracking control strategy of dual motor SBW was *** paper established methods for the synchronization control of dual motor and closed-loop control of angle *** the angle synchronization error was decreased by current compensation *** validation results show that the proposed control strategy can decrease the synchronization error of dual motor and ensure the reliability of angle tracking.
This paper addresses the design of sparse, H2-optimized dynamic output feedback controllers for distributed systems. These systems consist of several physically remote subsystems, which are assumed to be controlled vi...
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The atomic number densities of ^87 Rb vapor and surrounding gas 3He in a sealed cubic cell were measured by sweeping the absorption line and fitting the experimental data with a Lorentzian profile. The absorption was ...
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The atomic number densities of ^87 Rb vapor and surrounding gas 3He in a sealed cubic cell were measured by sweeping the absorption line and fitting the experimental data with a Lorentzian profile. The absorption was carried out around the D1 transition at different temperatures. We compare our results for the number density for^87 Rb with the previous methods and calculate the fractional error to be less than 5% as well as the error for ^3He between this work and the density obtained from the filling procedure to be no more than 4.2% at 370K. In addition, we discuss the factors that contribute to the error, among which the cell temperature plays the most important role.
This paper proposes an approach for the design of distributed state-feedback controllers for interconnected discrete-time LTI systems with uncertain communication links. These links may be subject to intermittent or p...
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Dynamic programming provides a method to solve hybrid optimal control problems. This contribution extends existing numerical methods originally developed for purely continuous systems, to a class of hybrid systems wit...
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ISBN:
(纸本)9783902661593
Dynamic programming provides a method to solve hybrid optimal control problems. This contribution extends existing numerical methods originally developed for purely continuous systems, to a class of hybrid systems with autonomous as well as controlled switching behavior. The hybrid dynamics is approximated by a locally consistent discrete Markov decision process. The original optimal control problem is then reformulated for the Markov decision process and solved by standard dynamic programming methods. The convergence of the discrete approximation to the original problem is ensured. The viability of the numerical scheme is illustrated by a two gear transmission system used previously in literature.
A communication-based distributed model predictive control scheme for a set of dynamically decoupled autonomous systems (agents) is proposed. The individual dynamics are described by discrete-time linear systems. Loca...
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Human Robot Interaction (HRI) is an active field of integrating and embedding different techniques in artificial intelligence. This paper describes my research topic on: control of Robotic systems for Safe Interaction...
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ISBN:
(纸本)9781577355120
Human Robot Interaction (HRI) is an active field of integrating and embedding different techniques in artificial intelligence. This paper describes my research topic on: control of Robotic systems for Safe Interaction with Human Operators. It consists of online motion generation for robotic manipulators interactingwith dynamic obstacles and humans using a moving horizon scheme, modeling and long term prediction of human motion using probabilistic models and reachability analysis, and development of an HRI demonstration platform.
In current manufacturing systems, short times for product development and ramp-up as well as high production rates and customized products are of increasing importance. To push the efficiency (and thus the economic ga...
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This paper investigates an interval analysis method for neural networks and applies it to fault detection for systems with unknown but bounded measurement noise. First, a novel interval analysis method is presented, w...
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This paper investigates an interval analysis method for neural networks and applies it to fault detection for systems with unknown but bounded measurement noise. First, a novel interval analysis method is presented, which can compute the bounds of the output of a feedforward neural network subject to a bounded input. By applying the proposed interval analysis method to a network trained with fault-free system data, adaptive thresholds for fault detection are computed. Finally, one can acquire fault detection results via a fault detection strategy. The proposed method can achieve tight bounds of the network output and employ simple operations, which leads to accurate fault detection results and a low computational burden.A numerical simulation and an experiment on an AC servo motor are given to illustrate the effectiveness and superiority of the proposed method.
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