While **quadrotors** are becoming more popular, their controllers should be improved. In this study, **neural** **network** **control** of **quadrotors** is aimed to obtain an artificial intelligence based controller. Firstly, the quadrotor is modeled according to quadrotor dynamics. Then, PD controllers for x, y, yaw and z **control** of quadrotor are implemented as classical controllers. The results for these controllers are recorded as training data of NN controllers. As the proposed controllers, NN controllers are trained according to these data and performance of these results are examined. The results verify that NN controllers achieve good trajectory tracking results.

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data over the other procedures using traditional **neural** net- work. Furthermore, enhancements to the SpikeProp learn- ing algorithm are presented. These enhancements provide additional learning rules for the synaptic delays, time con- stants and for the neurons thresholds. Simulated experi- ments have been conducted and the achieved results show a remarkable improvement in the overall performance. This work can also be extended to investigate online learning and address the effect of costs on the decisions in terms of computational time and complexity.

That is, first a **control** time series u is chosen. Using a model we look at what would happen if this series of **control** signals is used: the plant output y ˆ can be predicted, then next a cost function (1) is used which shows how good or bad the outcome is. Then let the optimization algorithm choose a new **control** time series u that is better by means of the cost function (1). Then number of iterations of these steps is done in order to find a series of **control** signals that is optimal with respect to the cost function (1).

Recently, **neural** **network** technology attracts many attentions in the design of robot controllers. It has been pointed out that multi-layered **neural** **network** can be used for the approximation of any nonlinear function. Other advantages of the **neural** networks often cited are parallel distributed structure, and learning ability. They make such the artificial intelligent technology attractive not only in the application areas such as pattern recognition, information and graphics processing, but also in intelligent **control** of nonlinear and complicated systems such as robot manipulators (Sanger, 1994), (Kim and Lewis, 1999), (Kwan and Lewis, 2000), (Jung and Yim, 2001) (Yu and Wang, 2001). A new field in robot **control** using **neural** **network** technology is beginning to emerge to deal with the issues related to the dynamics in the robot **control** design. A **neural** **network** based dynamics compensation method has been proposed for trajectory **control** of a robot system (Jung and Hsia, 1996). A combined approach of **neural** **network** and sliding mode technology for both feedback linearization and **control** error compensation has been presented (Barambones and Etxebarria, 2002). Sensitivity of a **neural** **network** performance to learning rate in robot **control** has been investigated (Clark and Mills, 2000).

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For all feed forward **neural** networks, the training sets and the test sets are prepared. The training set consists of the inputs of the **neural** **network**, for example source node, destination node, link costs and queue lengths, and the desired outputs, such as best neighbor node, best path from source to destination. In training stage, random values between – 0.5 and 0.5 are used as initial weights of connections of the **neural** networks. The **neural** networks are trained on 30 training sets with the back propagation algorithm for each computer networks (CN1, CN2). For any set, the training is continued until the mean squared error (MSE) becomes acceptable. The **neural** networks are trained on all possible routing decisions. In this stage, values of learning rate (η) and momentum rate (α) are selected by trial and error, which are given in Table (4). The obtained results are shown in Figures (6)_ (11). These Figures view mean squared error versus number of epochs, where this error is decreased when number of epochs is increased.

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From last few decades traditional ON-OFF controllers and conventional Proportional plus Integral plus Derivative controllers (PID) were used for controlling the Air Conditioning system. But these controllers don’t always produce desired and fast response due to dynamic characteristics of the systems. And tuning of conventional PID controllers was difficult. So in recent years intelligent **control** system are designed for controlling the air conditioning (AC) system. **Neural** **network** controllers are best option for controlling the system. By choosing suitable **neural** networks, learning method and input- output data the **neural** **network** can be learn the system states, to predict the future behavior of the system. **Neural** **network** controllers give the fast response and these controllers are reliable and robustness. The system which are linear or non-linear can be controlled by **neural** **network** because of **neural** networks have the ability to approximate arbitrator value through self-learning property. Due to this property of **neural** **network** the designed controllers make up the system uncertainties and system nonlinearities and make system response stable.

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the information. However, early TCP implementations had very bad retransmission behavior. When this packet loss occurred, the end points sent extra packets that repeated the information lost, doubling the data rate sent, exactly the opposite of what should be done during congestion. This pushed the entire **network** into a 'congestion collapse' where most packets were lost and the resultant throughput was negligible. Congestion is directly related to the amount of traffic that is present in the **network**. More the traffic more will be congestion. Congestion results when the traffic on the **network** exceeds the channel capacity. A router is able to handle optimum amounts of traffic but if the traffics exceeds the channel capacity, the entire system collapses and all or a majority of packets transmitted will be lost. To avoid congestion, we either increase the resource or decrease the load. Factors resulting in congestion are limited queue size, insufficient memory ,low band width, low CPU processing speed of router etc. II. R OUTING WITH CONGESTION **CONTROL**

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Underwater glider is an important equipment for ocean research, water quality detection and other ocean missions. It needs very high precision requirements to meet underwater glider motion **control**. When the position of buoyancy system changes, the balance parameters will change significantly. This paper presents a method for calculating the balance parameters of underwater glider based on **neural** **network**. In order to verify the effectiveness of the **neural** **network** **control**, the South China Sea experiment was carried out. By comparing the analysis results with the actual situation, the experiment shows that the **neural** **network** model is feasible.

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The sliding mode **control** is robust to plant uncertainties and insensitive to external disturbances. It is widely used to obtain good dynamic performance of controlled systems. However, the chattering phenomena due to the finite speed of the switching devices can affect the system behavior significantly. Additionally, the sliding **control** requires the knowledge of mathematical model of the system with bounded uncertainties. Another method, popular in recent years, is based on [7-10].

A pair of ordinary differential equations represents the mathematical modeliig of the chemical reactor used in this simulation 14] ' By manipulating the coolant jacket temperature T,, tr[r]

It is well known that imperfect robot model will lead to degradation of tracking performance. So it is necessary to approximate the un-modeled dynamics and external disturbance. Next, the **neural** **network** and disturbance observer are employed to approximate the un-modeled dynamic and external disturbance respectively

Because of its capability to approximate arbitrary non- linear mapping, **neural** **network** has been actively used in nonlinear system identification and **control** [9-12]. The multilayer feed-forward **neural** **network** (MFNN) is one of the most widely used **neural** networks as a system model in the design of a model-based controller. A dy-

Dynamic Positioning (DP) of ships is a **control** mode that seeks to maintain a specific position (stationkeeping) or perform low-speed maneuvers. In this paper, a static **Neural** **Network** (NN) is proposed for **control** allocation of an over-actuated ship. The thruster force and commands are measured during a trial run of the simulated vessel to gather data for training of the NN. Then the **network** is trained and used to transform the virtual force commands from a motion controller into individual thruster commands. A standard Proportional Integral Derivative (PID) controller, using wave-filtered position and heading measurements, is implemented as motion controller for each Degree Of Freedom (DOF) of the ship. For a DP application the controllable DOFs are the translational motion in surge and sway directions, as well as the rotation about its up/down axis. Simulation tests were performed to verify the feasibility of this approach.

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all the other conditions, the **neural** **network** controller shows a fast response speed with low current and voltage oscillations. The standard vector controller shows similar performance for power transmission **control** between the two VSC stations (Fig. 10). Compared to the **neural** **network** vector controller, the standard vector controller shows higher oscillations (Figs. 9c and 10c, Figs. 9d and 10d). This is due to the fact that the **control** action generated by the standard PI controller is determined by the error between the **control** parameter and the corresponding reference value. Hence, there must be overshoot and settling time issues associated with a PI-based controller. However, the **neural** **network** controller is designed and trained based on the DP-based optimal **control** principle. For an ideal optimal controller, a reference command can be reached immediately without any delay and overshoot. But, this cannot be achieved practically because of physical system constraints. The **neural** **network** controller tries to approximate an ideal optimal controller within the physical system constraints. Therefore, the **neural** **network** controller has the advantages for fast power transmission **control** between VSC stations with small oscillations as shown by Figs. 9 and 10. B. Power Transmission and PCC Voltage **Control**

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This paper brings out an innovative idea called as mouse for handless human (MHH) to use the camera as an alternative to the mouse. The mouse operations are controlled by the hand gesture captured by the camera Gesture Recognition Technology for Games Poised for Breakthrough on the website of Venture Bet ( http://venturebeat.com) [9].In the near future, for example, users will likely be able to **control** objects on the screen with empty hands, as shown in Figure 1.

The main problem in controlling DC motor as a position **control** drive is to achieve the desired position, reduce the steady-state errors and oscillation problem. Most of position controllers present several problems because of the nonlinearities system. Moreover, these nonlinearities are often unknown. These nonlinearities system affect the performance of position **control** of the DC motor system. The commonly used Proportional-Integral-Derivative (PID) controller is simple and easy to practice, but this controller suffers from poor performance if it has nonlinearities and uncertainties issues. The PID controller is not able to work well for non-linear system, and particularly complex and vague system that has no precise mathematical models. To overcome these difficulties, the **neural** **network** controller is developed in this project to ensure the DC motor is operable in any circumstance of situations.

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300 rpm at 8.4E-3 s It’s observed that the proposed two methods of **neural** controller succeeded to enhance the ripples. This reduction was around 1.26% with the value of ripples equal 0.0196 N.M for the **neural** **network**, 0.46% with the value of ripples equal 0.0072 N.M for **neural** **network** based estimator for torque constant and stator winding resistance. Moreover, this result is compared to other publication for more validation. V. Petrovic et al [4], Torque ripple percentage was ≈ 4% which is still higher than a proposed two methods of **neural** **network**. While W. Qian and Panda [18], Torque ripple percentage was 3.9% which is still higher than a proposed two methods of **neural** **network**. P. Mattavelli et al. [19], Torque ripple percentage was ≈ 3.8% which is still higher than the proposed two methods of **neural** **network**. H.M. Hasanien et al. [13]. Their result was 12%. Torque ripples in compression to our two methods of **neural** **network** are actually higher. M.Tarnik et al. [20], Torque ripple percentage was ≈ 4% which is also higher than the proposed two methods of **neural** **network**. Table 4, presents the torque ripples than percentage, which have been calculated from different works. The table shows that the proposed **control** reduced the percentage torque ripples to a good value rather the previous work [14]-[15]. We can further reach to optimize the output for torque and reduce the amount of percentage error by combining two **neural** networks first method, and second method.

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Motivated by the above discussion, this paper puts forward an adaptive **neural**- sliding model compensation **control** scheme. A RBF **neural** **network** is used to approximate the unknown nonlinear dynamics of the robot manipulators. But the local generalization of the RBF **network** is considered by the paper, because accuracy of **control** system is effected by approach errors of **neural** **network**, Sliding model controller is designed to eliminate approach errors to improve **control** accuracy and dynamic features. The adaptive laws of **network** weights are designed to ensure adjustment online-time, offline learning phase is not need; Globally asymptotically stable(GAS) of the closed-loop system is proved based on the Lyapunov theory. The simulations show this controller can speed up the convergence velocity of tracking error, and has good robustness.

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After the **neural** **network** architecture is modelled, the next stage defines the learning model to update **network** parameters. By this learning capability, it makes the ANN suitable to be implemented for the system with motor parameters which are difficult to define and vary against with environment. The training process minimizes the error output of the **network** through an optimization method. Generally, in learning mode of the **neural** **network** controller a sufficient training data input-output mapping data of a plant is required. Since the motor parameters of the induction motor drive vary with temperature and magnetic saturation, the online learning Back propagation algorithm is developed. Based on the first order optimization scheme, updating of the **network** parameters is covered. The performance index sum of square error is given by

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