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A human-like multi-fingered prosthetic hand, HIT hand, has been developed in Harbin Institute of Technology. This paper presents a new pattern discrimination method for HIT hand control. The method uses a bagged-BP neural network based on combing the BP neural networks using bagging algorithm. Bagging has been used to overcome the problem of limited number of training data in uni-model systems, by combining neural networks as weak learners. We compared the results of the bagging based BP network, using four features, with the results obtained separately from these uni-feature systems. The results show that the bagged-BP network improves both the accuracy and stability of the BP classifier.

A method investigating a Gaussian radial-basis-function neural network (GRBFNN) with sliding mode control (SMC) for missile electro-hydraulic servo mechanism is presented. Since the dynamics of the system are highly nonlinear and have large extent of model uncertainties, such as big changes in parameters and external disturbance, firstly, SMC is introduced. Since the accurate equivalent control is difficult to reach, a Gaussian radial basis function neural network is utilized. By adjusting the weight on-line, a neural-network-based SMC is developed to estimate the equivalent control of SMC control system. Then the switching control is appended to guarantee the stability of the proposed controller, and a set of fuzzy control rules are used to attenuate chattering phenomenon of the switching control. We apply the control method to the missile electro-hydraulic servo mechanism. Simulation results verify the validity of the proposed approach.

In this paper, an AND-OR fuzzy neural network (AND-OR FNN) and a piecewise optimization approach are proposed. The in-degree of neuron and the connectivity of layer are firstly defined and Zadeh’s operators are employed in order to infer the symbolic expression of every layer, the equivalent is proved between the architecture of AND-OR FNN and fuzzy weighted Mamdani inference. The main superiority is shown not only in reducing the input space, but also auto-extracting the rule base. The optimization procedure consists of GA (Genetic Algorithm) and PA (Pruning Algorithm);the AND-OR FNN ship controller system is designed based on input-output data to validate this method. Simulating results demonstrate that the number of rule base is decreased remarkably and the performance is good, illustrate the approach is practicable, simple and effective.

The inherent nonlinear of switched reluctance motor (SRM) makes it hard to get a good performance by using the conventional PID controller to the speed control of SRM. This paper develops a radial basis function (RBF) artificial neural network (ANN) nonlinear prediction model based adaptive PID controller for SRM. ANN, under certain condition, can approximate any nonlinear function with arbitrary precision. It also has a strong ability of adaptive, self-learning and self-organization. So, combining it with the conventional PID controller, a neural network based adaptive PID controller can be developed. Appling it to the speed control of SRM, a good control performance can be gotten. At the same time, the nonlinear mapping property and high parallel operation ability of ANN make it suitable to be applied to establish nonlinear prediction model performing parameter prediction. In this paper, two ANN – NNC and NNI are employed. The former is a back propagation (BP) ANN with sigmoid activation function. The later is an ANN using RBF as activation function. The former is used to adaptively adjust the parameters of the PID controller on line. The later is used to establish nonlinear prediction model performing parameter prediction. Compared with BP ANN with sigmoid activation function, the RBF ANN has a more fast convergence speed and can avoid getting stuck in a local optimum. Through parameter prediction, response speed of the system can be improved. To increase the convergence speed of ANN, an adaptive learning algorithm is adopted in this paper that is to adjust the learning rate according to the error. This can increase the convergence speed of ANN and make the system response quick. The experimental results demonstrate that a high control performance is achieved. The system responds quickly with little overshoot. The steady state error is zero. The system shows robust performance to the load torque disturbance.

For a nonlinear discrete-time Multi-Input Multi-Output (MIMO) system, a Hierarchical Multiple Models Neural Network Decoupling Controller (HMMNNDC) is designed in this paper. Firstly, the nonlinear system’s working area is partitioned into several sub-regions by use of a Self-Organizing Map (SOM) Neural Network (NN). In each sub-region, around every equilibrium point, the nonlinear system can be expanded into a linear term and a nonlinear term. Therefore the linear term is identified by a BP NN trained offline while the nonlinear term by a BP NN trained online. So these two BP NNs compose one system model. At each instant, the best sub-region is selected out by the use of the SOM NN and the corresponding multiple models set is derived. According to the switching index, the best model in the above model set is chosen as the system model. To realize decoupling control, the nonlinear term and the interaction of the system are viewed as measurable disturbance and eliminated using feedforward strategy. The simulation example shows that the better system response can be got comparing with the conventional NN decoupling control method.

This paper presents a new approach using switching grey prediction PID controller to an experimental propeller setup which is called the twin rotor multi-input multi-output system (TRMS). The goal of this study is to stabilize the TRMS in significant cross coupling condition and to experiment with set-point control and trajectory tracking. The proposed scheme enhances the grey prediction method of difference equation, which is a single variable second order grey model (DGM(2,1) model). It is performed by real-value genetic algorithm (RGA) with system performance index as fitness function. We apply the integral of time multiplied by the square error criterion (ITSE) to form a suitable fitness function in RGA. Simulation results show that the proposed design can successfully adapt system nonlinearity and complex coupling condition.

Based on RBF (radial basis function) neural network, an adaptive neural network feedback control scheme and an impulsive controller for output tracking error disturbance attenuation of nonlinear switched impulsive systems are given under all admissible switched strategy in this paper. Impulsive controller is designed to attenuate effect of switching impulse. The RBF neural net-work is used to compensate adaptively for the unknown nonlinear part of switched impulsive systems, and the approximation error of RBF neural net-work is introduced to the adaptive law in order to improve the tracking attenuation quality of the switched impulsive systems. Under all admissible switching law, impulsive controller and adaptive neural network feedback controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall switched impulsive system.

An approach is investigated for the adaptive guaranteed cost control design for a class of nonlinear state-delayed systems. The nonlinear term is approximated by a linearly parameterized neural networks(LPNN). A linear state feedback control law is presented. An adaptive weight adjustment mechanism for the neural networks is developed to ensure regulation performance. It is shown that the control gain matrices and be transformed into a standard linear matrix inequality problem and solved via a developed recurrent neural network.

Modern exploration missions require modern control systems that can handle catastrophic changes in behavior, compensate for slow deterioration in sustained operations, and support fast system identification. The dynamics and control of new vehicles remains a significant technical challenge. Neural network based adaptive controllers have these capabilities, but they can only be used safely if proper Verification and Validation can be done. Due to the nonlinear and dynamic nature of an adaptive control system, traditional Verification and Validation (V&V) and certification techniques are not sufficient for adaptive controllers, which is a big barrier in their deployment in the safety-critical applications. Moreover, traditional methods of V&V involve testing under various conditions which is costly to run and requires scheduling a long time in advance. We have developed specific techniques, tools, and processes to perform design time analysis, verification and validation, and dynamic monitoring of such controllers. Combined with advanced modelling tools, an integrated development or deployment methodology for addressing complex control needs in a safety- and reliability-critical mission environment can be provided.

Exoskeleton system which is to assist the motion of physically weak persons such as disabled, injured and elderly persons is discussed in this paper. The proposed exoskeletons are controlled basically based on the electromoyogram (EMG) signals. And a mind model is constructed to identify person’s mind for predicting or estimating person’s behavior. The proposed mind model is installed in an exoskeleton power assistive system named IAE for walking aid. The neural-network is also be used in this system to help learning. The on-line learning adjustment algorithm based on multi-sensor that are fixed on the robot is designed which makes the locomotion stable and adaptable.