Search

This paper proposes a hybrid neural network model based on the Generalized Learning Vector Quantization(GLVQ) learning algorithm and applies this proposed method to the BIT system of More-Electric Aircraft Electrical Power System (MEAEPS). This paper first discusses the feasibility of application unsupervised neural networks to the BIT system and the representative Generalized LVQ (GLVQ) neural network is selected due to its good performance in clustering analysis. Next, we adopt a new form of loss factor to modify the original GLVQ algorithm in order to make it more suitable for our application. Since unsupervised networks cannot distinguish the similar classes, we add a LVQ layer to the GLVQ network to construct a hybrid neural network model. Finally, the proposed method has been applied to the intelligent BIT system of the MEAEPS, and the results show that the proposed method is promising to improve the performance of the BIT system. [ABSTRACT FROM AUTHOR]

This paper presents a power system stabilizer design that uses a generalized minimum variance-inverse dynamic neuro controller, which is the combination of the inverse dynamic neural model, the generalized minimum variance, and the neuro compensator. An inverse dynamic neural model represents the inverse dynamics of the system. The inverse dynamic neural model is trained to provide control input into the system, which makes the plant output reach the target value at the next sampling time. Once the inverse dynamic neural model is trained, it does not require retuning for cases with other types of disturbances. In this paper, a generalized minimum variance control scheme is adapted to prevent unstable system performance caused by non-minimum phase characteristics. In addition, a neural compensator is designed to compensate for modeling errors. The proposed control scheme is tested in a multimachine power system. [ABSTRACT FROM AUTHOR]

The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, in general, a combinatorial optimization problem. This paper proposes optimal reconfiguration of the phase balancing using the neural network, to switch on and off the different switches, allowing the three phases supply by the transformer to the end-users to be balanced. This paper presents the application examples of the proposed method using the real and simulated test data. [ABSTRACT FROM AUTHOR]

Electric Power Load Forecasting is important for the economic and secure operation of power system, and highly accurate forecasting result leads to substantial savings in operating cost and increased reliability of power supply. Conventional load forecasting techniques, including time series methods and stochastic methods, are widely used by electric power companies for forecasting load profiles. However, their accuracy is limited under some conditions. In this paper, neural networks have been successfully applied to load forecasting. Forecasting model with Neural Networks is set up based on the analysis of the characteristics of electric power load, and it works well even with rapidly changing weather conditions. This paper also proposes a novel method to improve the generalization ability of the Neural Networks, and this leads to further increasing accuracy of load forecasting. [ABSTRACT FROM AUTHOR]

In this paper, we propose a grasping control method for robot hand using fuzzy theory and partially- linearized neural network. The robot hand has Double-Octagon Tactile Sensor (D.O.T.S), which has been proposed in our previous papers, to detect grasping force between the grasped object and the robot fingers. Because the measured forces are fluctuant due to the measuring error and vibration of the hand, the tactile information is ambiguous. In order to quickly control the grasping force to prevent the grasped object sliding out off the robot fingers, we apply the possibility theory to deal with the ambiguous problem of the tactile information, and use the partially- linearized neural network (P.L.N.N) to construct a fuzzy neural network. The method proposed in this paper is verified by applying it to practical grasping control of breakable objects, such as eggs, fruits, etc. [ABSTRACT FROM AUTHOR]

This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steady-state error of normal PD control. 2) guarantee stability via neural compensation. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper. [ABSTRACT FROM AUTHOR]

A novel least squares support vector machines based on Mexican hat wavelet kernel is presented in the paper. The wavelet kernel which is admissible support vector kernel is characterized by its local analysis and approximate orthogonality, and we can well obtain estimates for regression by applying a least squares wavelet support vector machines (LS-WSVM). To test the validity of the proposed method, this paper demonstrates that LS-WSVM can be used effectively for the identification and adaptive control of nonlinear dynamical systems. Simulation results reveal that the identification and adaptive control schemes suggested based on LS-WSVM gives considerably better performance and show faster and stable learning in comparison to neural networks or fuzzy logic systems. LS-WSVM provides an attractive approach to study the properties of complex nonlinear system modeling and adaptive control. [ABSTRACT FROM AUTHOR]

Presented in this paper is neural network based adaptive control for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. A popular recursive design methodology - backstepping is employed to systematically construct feedback control laws and associated Lyapunov functions. The significance of this paper is to make best use of available signals, avoid unnecessary parameterization, and minimize the node number of neural networks as on-line approximators. The design assures that all the signals in the closed loop are semi-globally uniformly, ultimately bounded and the outputs of the system converges to a tunable small neighborhood of the desired trajectory. Novel parameter tuning algorithms are obtained on a more practical basis. [ABSTRACT FROM AUTHOR]

In this paper, the topic of fractional order digital differentiators (FODD) is designed using neural networks approximation method. First, FODD amplitude response is given in the form of sum of exponential basis functions. Then, the exponential basis function neural network is used to approximate FODD amplitude response. Finally, some examples compared with others' method are given to illustrate the advantages of this paper approach. [ABSTRACT FROM AUTHOR]

A new approach to object detection using image icons based on Unit-linking PCNN (Pulse Coupled Neural Network) is introduced in this paper. Unit-linking PCNN, which has been developed from PCNN exhibiting synchronous pulse bursts in cat and monkey visual cortexes, is a kind of time-space-coding SNN (Spiking Neural Network). We have used Unit-linking PCNN to produce the global image icons with translation and rotation invariance. Unit-linking PCNN image icon (namely global image icons) is the 1-dimentional time series, and is a kind of image feature extracted from the time information that Unit-linking PCNN code the 2-dimentional image into. Its translation and rotation invariance is a good property in object detection. In addition to translation, rotation invariance, the object detection approach in this paper is also independent of scale variation. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary cellular automata based neural systems (Evolutionary CANS) for visual servoing of RV-M2 robot manipulator. The architecture of CANS consist of a two-dimensional (2-D) array of basic neurons. Each neuron of CANS has local connections only with contiguous neuron and acts as a form of pulse according to the dynamics of the chaotic neuron model. CANS are generated from initial cells according to the cellular automata (CA) rule. Therefore neural architecture is determined by both initial pattern of cells and production rule of CA. Production rules of CA are evolved based on a DNA coding. DNA coding has the redundancy and overlapping of gene and is apt for representation of the rule. In this paper we show the general expression of CA rule and propose translating method from DNA code to CA rule. In addition, we present visual servoing application using evolutionary CANS. [ABSTRACT FROM AUTHOR]

This paper first introduces the system requirement and the system flow of the auto-plotting system. As the data points needed by the auto-plotting system coming from the remote speech signals, to reach high recognition accuracy, the Hidden Markov Model (HMM) approach was chosen as the speech recognition approach. Then the paper is detailed on the speaker dependent (SD), speaker independent (SI) and speaker adaptive (SA) speech recognition methods. We proposed the n-speech models SD system as the recognition system to gain the highest recognition performance in varying speech environments. However the system required that searching for the optimal model from the database should finish in 5 minutes, so the paper finally describes how the Self-Organizing Map (SOM) was used to pre clustering to the n-speech models, to decrease the time for speech recognition and results evaluation and decrease matching time, Experiments show the n-speech models SD system can select the best-matching model in the limited time and improve the average speech recognition accuracy to 97.2. It ideally suits the system requirements. [ABSTRACT FROM AUTHOR]

Constructive fuzzy neural networks (i.e., CFNN) proposed in [1] cannot be used for non-numerical data. In order to use CFNN to deal with non-numerical complicated data, rough set theory is adopted to improve the CFNN in this paper. First of all, we use rough set theory to extract core set of non-numerical attributes and decrease number of dimension of samples by reducing redundancy. Secondly, we can pre-classify the samples according to non-numerical attributes. Thirdly, we use CFNN to classify the samples according to numerical attributes. The proposed method not only increases classification accuracy but also speeds up classification process. Finally, the classification of wireless communication signals is given as an example to illustrate the validation of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) are increasingly important for wheeled mobile robots (WMRs), especially those in unknown environments such as planetary exploration. There are many kinds of fault diagnosis methods available for mobile robots, including multiple model-based approaches, particle filter based approaches, sensor fusion based approaches. Currently, all of these methods are designed for complete models. However, completely modeling a system is difficult, even impossible. In this paper, particle filter and neural network are integrated to diagnose complex systems with imperfect models. Two features are extracted from particles: the sum of sample weights, and the maximal a posteriori probability. These features are further feed to a neural network to decide whether the estimation given by the particle filter is credible or not. An incredible estimation indicates that the true state isn't included in the state space, i.e. it is a novel state (or an unknown fault). This method preserves the merits of particle filter and can diagnose known faults as well as detect unknown faults. It is testified on a real mobile robot. [ABSTRACT FROM AUTHOR]

Nowadays, mobile robots have attracted more and more attention from researchers due to their extensive applications. Mobile robots need to have the capabilities of autonomy and intelligence, and they pose a challenge to researchers, which is to design algorithms that allow the robots to function autonomously in unstructured, dynamic, partially observable, and uncertain environments [1]. Navigation is the key to the relative technologies of mobile robots and neural networks are widely used in the field of mobile robot navigation due to their properties such as nonlinear mapping, ability to learn from examples, good generalization performance, massively parallel processing, and capability to approximate an arbitrary function given sufficient number of neurons. This paper surveys the developments in the last few years of the neural networks with applications to mobile robot navigation. [ABSTRACT FROM AUTHOR]

This paper presents a novel self-adaptive wavelet neural network method for automatic recognition and classification of power quality disturbances. The types of disturbances include harmonic distortions, flickers, voltage sags, voltage swells, voltage interruptions, voltage notches, voltage impulses and voltage transients. The self-adaptive wavelet neural network model constructed consists of four layers: input layer, preprocessing layer, hidden layer and output layer. The preprocessing layer is also called wavelet layer whose function is to extract features of power quality disturbances for recognition and classification; the other three layers just constitute the feedforward neural network whose function is to recognize and classify the types of power quality disturbances. The self-adaptive wavelet neural network has a good anti-interference performance, and the test and evaluation results demonstrate that utilizing it power quality disturbances can be recognized and classified effectively, accurately and reliably. [ABSTRACT FROM AUTHOR]

This paper provides a strategy at the system level to assess and mitigate power system cascading outages considering the probabilities of hidden failure in protection system, which affect the risk of cascading outages. Some risk indices are used to assess the risk of cascading outages. The fuzzy neural network (FNN) is used to obtain the risk indices and to propose a solution that can decrease the system cascading outage risk under limited budget. The IEEE 118-bus system is used to illustrate the methodology. [ABSTRACT FROM AUTHOR]

Static voltage security is one of the important items of power system security. This paper provides an approach to calculate risk of low voltage in power system using neural network ensemble. Risk is defined as a condition under which there is a possibility of an adverse deviation from a desired outcome that is expected or hoped for. Risk index is used as an indicator of the low voltage security. It is calculated as the product of the probability of contingency and the impact of low voltage. Neural network ensemble (NNE) is used for the low voltage risk assessment to get the desired speed, accuracy and efficiency. The New England 39-bus test system is used as an example to demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Through the analysis of function approximation with wavelet transformation, an adaptive wavelet neural network is introduced in the paper, which is applied in data compression of fault data in power system. In addition, the wavelet entropy is adopted to choose the hidden nodes in the wavelet neural network. The learning algorithm of the wavelet neural network based on wavelet entropy is proposed and discussed for data compression of fault data in power system. The simulation results show that it is feasible and valid in the end. [ABSTRACT FROM AUTHOR]

This paper proposes a novel neural network called Extension Neural Network-Type 1 (ENN1) for vibration fault recognition according to generator vibration characteristic spectra. The proposed ENN1 has a very simple structure and permits fast adaptive processes for new training data. Moreover, the learning speed of the proposed ENN1 is shown to be faster than the previous approaches. The proposed method has been tested on practical diagnostic records in China with rather encouraging results. [ABSTRACT FROM AUTHOR]

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a new approach for the recognition of power quality disturbances using wavelet transform and neural networks. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency. [ABSTRACT FROM AUTHOR]

The paper analyzes the structure character of a wind power generation and flywheel energy storage combined system, and presents a new control strategy—fuzzy neural network (FNN) based on genetic arithmetic (GA) for the nonlinear problem of the control system. The control strategy realizes automatic regulation of direct current (DC) bus voltage in the system, so it stabilizes the DC bus voltage of the system. The experimental results show that the controller has better self-learning and robustness, and realizes the satisfactory operation of the proposed system. [ABSTRACT FROM AUTHOR]

Automatical operation of space robots is a challenging and ultimate goal of space servicing. In this paper, we present a novel approach for tracking and catching operation of space robots based on learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parameterize the model of HCS, and then develop a new SVM-based leaning structure to improve HCS in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, which makes it efficient in modeling, understanding and transferring its learning process. The simulation results demonstrate that the proposed method is useful and feasible in tracking trajectory and catching objects autonomously. [ABSTRACT FROM AUTHOR]

Ship synchronous generator modeling is the basis of control, analysis and design in the ship power systems. According to the strong non-linear relation characteristics of ship synchronous generator, a dynamic modeling method based on rough set theory (RST) and radial basis function (RBF) neural networks is presented in this paper. With the advantage of finding useful and minimal hidden patterns in data, RST is first applied to intelligent data analysis in this algorithm, including incompatible data elimination, important input nodes selection and radial basis function centers initialization, followed by a second stage adjusting the network parameters and training the weights of hidden nodes. The experimental results proved that this method could achieve greater accuracy and generalization ability. [ABSTRACT FROM AUTHOR]

Artificial neural networks can be used as intelligent controllers to control non-linear, dynamic systems through learning, which can easily accommodate the non-linearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. Taking benefit of the characteristics of a Generalized Neuron that requires much smaller training data and shorter training time, the pseudo-linear neural network (PNN) based model predictive approach used in the single and multi-machine power system studies is proposed in this paper. A simulation is carried out. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems. [ABSTRACT FROM AUTHOR]

With the growing application of Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM), the coordinating problem of SVC and STATCOM controllers in joint operation must be considered in modern power systems. This paper firstly establishes the nonlinear differential-algebra equations model of a single-machine infinite-bus (SMIB) power system installed with a SVC and a STATCOM and points out the possibility of the negative interactions between SVC and STATCOM controllers in this SMIB power system. Hence, a self-adaptive single neuron (SSN) control approach based on genetic algorithm is designed to eliminate the negative interactions and improve the stability of the closed-loop SMIB power system. The detailed simulation results demonstrate the effectiveness of the proposed SSN controllers. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) pattern recognition is an important tool in HV insulation diagnosis. A PD pattern recognition approach of HV power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, two fractal features (fractal dimension and lacunarity) extracted from the raw 3-D PD patterns are presented for the neural- network-based (NN-based) recognition system. The system can quickly and stably learn to categorize input patterns and permit adaptive processes to access significant new information. To demonstrate the effectiveness of the proposed method, the recognition ability is investigated on 150 sets of field tested PD patterns of epoxy resin power transformers. Different types of PD within power transformers are identified with rather encouraged results. [ABSTRACT FROM AUTHOR]

Adopting the fundamentals of PWM voltage source rectifier and the PID-control technology based on error-back-propagation neural network, an electronic power load simulator is designed in this paper, which can simulate the exact Volt-Ampere characteristics of power load and supply high quality feedback electric power. In order to enable the PID controller to perform better, genetic algorithms are used to accumulate the priori knowledge of the neural network's connection weights, and the system voltages and the controlled parameters are forecasted. The experimental results show that the electronic power load simulator runs well when the Volt-Ampere characteristics simulated are time-variable or voltage disturbances occur in power system. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary neural network (ENN) approach for solving the power system unit commitment problem. The proposed ENN approach combines a genetic algorithm (GA) with a back-propagation neural network (BPNN). The BPNN is first used as a dispatch tool to generate raw unit combinations for each hour temporarily ignoring time-dependent constraints. Then, the proposed decoding algorithm decodes the raw committed schedule of each unit into a feasible one. The GA is then used to find the finally optimal schedule. The most difficult time-dependent minimal uptime/downtime constraints are satisfied throughout the proposed encoding and decoding algorithm. Numerical results from a 10-unit example system indicate the attractive properties of the proposed ENN approach, which are a highly optimal solution and faster rate of computation. [ABSTRACT FROM AUTHOR]

Market clearing price (MCP) is one of the most important factors impacting on power system. Taking into account the features of deregulation and fluctuation, this paper uses artificial neural network to forecast next-day MCP, with period-decoupled data sequence and wavelet transform. For the purpose of better performance, an improved learning algorithm of artificial fish-swarm is proposed. By simulating fish-swarm actions, in random searching for foods, artificial fish-swarm based neural network (AFNN) achieves global optimum. Comparing with traditional next-day MCP forecasting methods, the suggested method could achieve better adaptability and greater predictive accuracy, which was proved by the experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes an artificial neural network (ANN) model to predict m-daily-ahead electricity price using direct forecasting approach on European Energy Exchange (EEX) market. The most important characteristic of this model is the single output node for m-period-ahead forecasts. The potentials of ANNs are investigated by employing cross-validation schemes. Out-of-sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are more robust multi-step-ahead forecasting method than autoregressive error models (AUTOREG). Moreover, ANN predictions are quite accurate even when the length of forecast horizon is relatively short or long. [ABSTRACT FROM AUTHOR]

Accurate Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors in order to develop the accuracy of predictions. Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical system, we tested the accuracy of forecasting in specific days with comparison. [ABSTRACT FROM AUTHOR]

In this paper a novel variant activation (transform) sigmoid function with three parameters is proposed, and then the improved BP algorithm based on it is educed and discussed, then Embedded Chaos-BP algorithm is proposed by means of combining the new fast BP algorithm and chaos optimization algorithm, Embedded chaos-BP algorithm converges fast and globally, and has no local minimum. The efficiency and advantage of our method is proved by simulation results of nonlinear function and prediction results of short-term load based on the improved and traditional BP ANNs. [ABSTRACT FROM AUTHOR]

Biological control systems have long been studied as possible inspiration for the construction of robotic controllers. In this paper, we present a control of voluntary movements using a cortical network within constraints from neurophysiology. Neural controller is proposed to control desired joint trajectories for multi-joint opponent muscle control of a robot finger. Each joint is controlled by an agonist-antagonist muscle pair. Neural model proposes functional roles for pre-central cortical cell types in the computation of a descending command to spinal alpha and gamma motoneurons. Neurons in anterior area 5 are proposed to compute the position of the link in question using corollary discharges and feedback from muscles spindles. Neurons in posterior area 5 use this position perception to compute a desired movement direction. Through experimental results, we showed that neural controller exhibits key kinematic properties of human movements, dynamics compensation and including asymmetric bell-shaped velocity profiles. Neural controller suggests how the brain may set automatic and volitional gating mechanisms to vary the balance of static and dynamic feedback information to guide the movement command and to compensate for external forces. [ABSTRACT FROM AUTHOR]

Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper, which consists of local GPCs designed using the local linear models of the neuro-fuzzy network. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant, in which much better performance than the traditional cascade PI controller or the linear GPC is obtained. [ABSTRACT FROM AUTHOR]

A novel dynamic control scheme for nonholonomic mobile robots is developed in this paper. The dynamics of mobile robot based on improved radial basis function neural network (IRBFNN) is modeled online by the improved algorithm of resource allocating network (IRAN). The control scheme of mobile robot integrates a velocity controller based on backstepping technology and a torque controller based on the IRBFNN and robust compen-sator. The simulations have shown that the control system is competent for the robust tracking control of mobile robot. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of controlling an acrobot, an under-actuated robotic systems, using fuzzy neural network approach. A five-layer Takagi-Sugeno fuzzy neural network control (TSFNNC) is proposed to swing up the acrobot from the low stable equilibrium to approach and balance around its top unstable equilibrium position. By analyzing the system dynamics, total energy and potential energy of the system are introduced in the second layer, with the system states as the inputs to the first layer. Fuzzy membership functions and rules are depicted in the third and fourth layers respectively. The fifth layer works as the final output. A modified particle swarm optimizer (PSO) is adopted to train the consequents in the fourth layer. Simulation results indicate that the integrated TSFNNC approach can control the acrobot system from upswing to balance process effectively. This approach provides an easy and feasible solution for similar control problems. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy inference system based on support vector machi- nes is proposed for nonlinear system control. Support vector machines provides a mechanism to extract support vectors for generating fuzzy if-then rules from the training data set, and a method to describe the fuzzy inference system in terms of kernel functions. Thus it has the inherent advantages that the model doesn't have to decide the number of fuzzy rules in advance, and has universal approximation ability and good generalization ability. The simulation results for stabilizing control of double inverted pendulum system are provided to show the validity and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Hyper-redundant manipulator has more degrees of freedom than the least necessary to perform a given task, thus it has the features of overcoming conventional industrial robot's limitation to carry out a designated difficult task. When the manipulator carries out the missions such as brushing or writing on a surface, drilling or inspection in a hole, the end-effector of the manipulator usually has both position and orientation requirement. Effective control of the hyper-redundant manipulator with such constrained end-effector is difficult for its redundancy. In this paper, a novel approach based on RBF neural network has been proposed to kinematically control the hyper-redundant manipulator. This technique, using variable regular polygon and RBF neural networks models, is completely capable of solving the control problem of a planar hyper-redundant manipulator with any number of links following any desired direction and path. With the shape transformation of variable regular polygon, the manipulator's configuration changes accordingly and moves actively to perform the tasks. Compared with other methods to our knowledge, this technique has such superiorities as fewer control parameters and higher precision. Simulations have demonstrated that this control technique is available and effective. [ABSTRACT FROM AUTHOR]

Recently, based on multi-resolution analysis, wavelet neural networks (WNN) have been proposed as an alternative to NN for approximating arbitrary nonlinear functions in L2(R). Discontinuous friction function is an unavoidable nonlinear effect that can limit control performance in mechanical systems. In this paper, adaptive WNN is used to design a friction compensator for a single joint mechanical system. Then asymptotically stability of the system is assured by adding a PD controller and adaptive robust terms. The simulation results show the validity of the control scheme. [ABSTRACT FROM AUTHOR]

The basic assumption that the angular velocity of the input crank is constant in much mechanism synthesis and analysis may not be validated when an electric motor is connected to driven then mechanism. First, the controller-motor-mechanism coupling system is studied in this paper, numerically simulation result demonstrate the crank angular speed fluctuations for the case of a constant voltage supply to DC motor. Then a novel algorithm of motor-mechanism adaptive PID control with BP neural network is proposed, using the approximate ability to any nonlinear function of the neural network. The neural network are used to predicted models of the controlled variable, this information is transferred to PID controller, through the readjustment of the pre-established set. The simulation results show that the crank speed fluctuation can be reduced substantially by using feedback control. [ABSTRACT FROM AUTHOR]

A hybrid actuator is presented in this paper. Hybrid actuator is a new type of planar parallel robot, and requires precise control of the position of the mechanism. In order to achieve the desired accuracies, nonlinear factors as friction must be accurately compensated in the real-time servo control algorithm. According to the characteristics of the hybrid actuator, a hybrid intelligent control algorithm based on PID control and cerebellar model articulation control (CMAC) techniques was presented and used to perform control of hybrid actuator for the first time. Simulation results show that this method can improve the control effect remarkably compared with the tradi- tional control strategy. [ABSTRACT FROM AUTHOR]

A novel dynamic time delay neural network is proposed for ultrasonic motors identification and control in this paper. By introducing time delay neurons, the neural network identifier and controller of ultrasonic motors are constructed. Both of them are trained online by using an improved back-propagation algorithm. The usefulness and validity of the presented algorithm is examined by the experiments. [ABSTRACT FROM AUTHOR]

In this paper, the automatic balancing method is studied at constant speed or acceleration operation for rotor systems. The magnitude and phase of original imbalance is determined by the influence coefficient method through measurement of vibrations. In addition, a self-tuning neural-PD controller is designed to control the angular positions of correction masses for the automatic balancer. The equations of motion for a cylindrical rotor with radial imbalances are modeled to simulate two-plane balancing by means of axis-passed balancers. The dynamic responses before and after automatic balancing are investigated to justify the validity of the present method. [ABSTRACT FROM AUTHOR]

Since Magneto-rheological (MR) suspension has nonlinearity and time-delay, the application of linear feedback strategy has been limited. This paper addresses the problem of control of MR suspension with time-delay when transient dynamics are presented. An adaptive Fuzzy-Neural Network Control (FNNC) scheme for the transient course is proposed using fuzzy logic control and artificial neural network methodologies. To attenuate the adverse effects of time-delay on control performance, a Time Delay Compensator (TDC) is established. Then, through a numerical example of a quarter car model and a real road test with a bump input, the comparison is made between passive suspension and semi-active suspension. The results show that the MR vehicle with FNNC strategy can depress the peak acceleration and shorten the setting time, and the effect of time-delay can be attenuated. The results of road test with the similarity of numerical study verify the feasibility of the control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a feedforward active noise control (ANC) system using a mode-free neural network (MFNN) controller based on simultaneous perturbation stochastic approximation (SPSA) algorithm is considered. The SPSA-based MFNN control algorithm employed in the ANC system is first derived. Following this, computer simulations are carried out to verify that the SPSA-based MFNN control algorithm is effective for a nonlinear ANC system. Simulation results show that the proposed scheme is able to significantly reduce disturbances without the need to model the secondary-path and has better tracking ability under variable secondary-path. This observation implies that the SPSA-based MFNN controller frees the ANC system from the modeling of the secondary-path. [ABSTRACT FROM AUTHOR]

Study of maintaining safe operation of marine main engine while one or several faults occur in marine systems has been put a high value recently in field of shipbuilding industry. The paper establishes a dynamic mathematical model of marine main engine system and analyses its characteristics while it is in abnormal conditions. Afterwards, a fault tolerant control system with artificial neural network algorithm is designed for improvement of operation of main engine that is in abnormal operation under consideration that a fault tolerant control system is able to fulfill function of fault toleration. The results of simulation experiments show that this fault tolerant control system is suitable for safe operation of marine main engine system. [ABSTRACT FROM AUTHOR]

To improve BP algorithm in overcoming the local minimum problem and accelerating the convergence speed, a new improved algorithm based on global revision factor of BP neural network is presented in this paper. The basic principle is to improve the formula of weight adjusting used momentum back propagation. A global revision factor is added in the weight value adjusting formula of momentum BP. Faster learning speed is obtained by adaptive adjusting this factor. The new BP algorithm is compared with other improved BP algorithms on many aspects. Simulation and applications for complex nonlinear function approximation, neural PID parameter tuning indicates that it has better training speed and precision than momentum back propagation and adaptive learning rate. [ABSTRACT FROM AUTHOR]

A pseudo-linear radial basis function (PLRBF) network is developed in this paper. This network is used to model a real process and its weights are on-line updated using a recursive orthogonal least squares (ROLS) algorithm. The developed adaptive model is then used in model predictive control strategy, which is applied to a pilot multivariable chemical reactor. The first stage of the project, simulation study, has been investigated and is presented. The effectiveness of the adaptive control in improving the closed-loop performance has been demonstrated for process time-varying dynamics and model-process mismatch. [ABSTRACT FROM AUTHOR]

This paper presents the application of an evolutionary neural network controller in a stabilisation problem involving an inverted pendulum. It is guaranteed that the resulting continuous closed-loop system is asymptotically stable. The process of training the neural network controller can be treated as a constrained optimisation problem where the equality constraint is derived from the Lyapunov stability criteria. The decision variables in this investigation are made up from the connection weights in the neural network, a positive definite matrix required for the Lyapunov function and a matrix for the stability constraint while the objective value is calculated from the closed-loop system performance. The optimisation technique chosen for the task is a variant of genetic algorithms called a cooperative coevolutionary genetic algorithm (CCGA). Two control strategies are explored: model-reference control and optimal control. In the model-reference control, the simulation results indicate that the tracking performance of the system stabilised by the evolutionary neural network is superior to that controlled by a neural network, which is trained via a neural network emulator. In addition, the system stabilised by the evolutionary neural network requires the energy in the level which is comparable to that found in the system that uses a linear quadratic regulator in optimal control. This confirms the usefulness of the CCGA in nonlinear system stabilisation applications. [ABSTRACT FROM AUTHOR]