In this thesis, an **aerial** **manipulation** **system** (AMS) consisting of a quadrotor with a 2-DOF manipulator was designed and constructed. The entire mathemat- ical model of the **system** that incorporates the coupling between the quadrotor and robotic arm, and the physical interaction with the environment was derived through the Euler-Lagrange formulation. Based on this mathematical model, a **nonlinear** **adaptive** **control** framework was developed. It was applied on a high fidelity AMS model which includes mass and inertia uncertainties, wind distur- bances, and measurement noises. In order to **control** the positional dynamics of the AMS in the presence of mass uncertainty and reaction forces, model reference **adaptive** **control** (MRAC) was utilized. Using the command signals generated by the MRAC and making small angle approximations, desired attitude angles were calculated analytically for the low-level controller. Attitude dynamics of the quadrotor and 2-DOF manipulator dynamics were combined as a 5-DOF fully actuated **system**. **Nonlinear** **adaptive** **control** was implemented for this 5-DOF rotational dynamics where uncertainties in inertias were considered.

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Abstract. **Nonlinear** singular systems present a general mathematical framework for the **modeling** and controlling of complicated systems, however the complex nature of this type of systems causes many difficulties in **control** strategy. In this paper, a model reference **control** approach is addressed for **nonlinear** affine singular systems. First, a basic **control** **system** is proposed based on the Lyapunov stability theorem so that **nonlinear** singular **system** can asymptotically track the desired linear reference model. After that, in the second design, it has been considered that systems’ parameters are unknown and two **adaptive** approaches are investigated. For better illustration, simulation has been done and the results show the tracking performance for both presented **control** systems.

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This work then considers the task of a robot assistant preparing an experiment for collaboration with wet-laboratory research scientists. The task is to efficiently pour fluid between an open pouring and a receiving container. An efficient pour is characterized as both quick and precise. It is assumed that there is no spillage and that the properties of the poured fluid are known. The contributions of this work for this task are two fold. In the case where the geometry of all containers are known, and the receiver can be observed during the pour, an analytical solution is provided which considers the expected flow-rate of the fluid given both containers geometry. With this analytical solution, the robot is able to pour very quickly and precisely leveraging a hybrid controller that both ensures steady flow and considers the **system** dynamics in order achieve the desired receiver volume. In the case where the geometry of the pouring container is not known analytically, it is assumed that the geometry can be approximated by an initial external scan of the container. While for simplicity this work considers symmetric pouring containers, this is not a limitation as long as a distance metric can be defined between container geometries. During the pour only the receiving container is observed, and the **system** dynamics must be adjusted based on an initial estimate obtained from a single scan of the pouring container geometry. The main contribution in this case is that both classical **system** identification and learning are used simultaneously to predict the pouring container behavior. This prediction is a necessity because the time delay between the rotational velocity **control** input of the pouring container and the observed volume in the receiver is substantial for certain pouring container geometries. This predicted model for the pouring container is then used in a hybrid controller to achieve the **control** objective by anticipating the **nonlinear** dynamics. This is implemented on the Rethink Robotics Sawyer and KUKA intelligent industrial work assistant (iiwa) manipulators.

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One final **control** method to discuss for lightweight manipulators is MRAC con- trol. MRAC **control** is fairly straightforward to implement in two-link manipulators due to the ideal **system** dynamics at a set trajectory point being relatively easy to find [33]. This provides a relatively simple calculation, unlike the high compu- tational load required to calculate the actual **system** dynamics in real time [33]. However, many modelling assumptions must be made, such as known payload mass and perfect joint structure, resulting in a large error between the actual and ideal **system** responses that must be corrected by the MRAC algorithm. Furthermore, the linearization of the ideal **system** may not be valid for all manipulator configurations, due to straying too far from the operating point [50]. This in turn limits the valid operating range of the manipulator, reducing its efficacy in the tasks asked of it. Hence, ASMC is the much more popular **control** algorithm for robotic **manipulation** [50].

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linearization is popular as well in other areas. Oriolo et al [12] propose an implementation of FLC in wheeled mobile robots tracking task in [10]. From [12], a good trajectory tracking performance of PUMA 560 robot manipulator is achieved by using discontinuous feedback linearization rather than a PID **control**, which makes the controller more suitable for an electrically driven high speed robot manipulator. Fuzzy **control** is a powerful tool for handling **system** uncertainties and noises, and feedback linearization needs an inversion of the **system**. When the **system** and environment are uncertain, feedback linearization **control** alone might not be suitable enough as the controller due to its sensitivity to **modeling** errors, uncertainties, and noises, and thus reference [13] provides a possible solution by the combination of these two methods. In reference [14], a popular pendulum problem is solved by an input-output feedback linearization cascade controller.

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hydraulic pump. In order to develop **control** algorithms to achieve the above objective, we must first develop a dy- namic model for the spreader. Specifically, we need a relationship between the input, i.e. the auger speed and the gate opening size, and the output, i.e. the material discharge rate. Recall that the material is a highly inho- mogeneous mix of liquids and solids with unknown per- centage of each phase. The weight and viscosity of the material affect the dynamics of the hydraulic **system** that drives the auger. As the spreading proceeds, the amount of material remaining in the tank changed. All these fac- tors attribute to a **nonlinear** and time-varying dynamics of the spreader. As discussed in Section 1, analytical mod- eling of such a **system** is a difficult task. In this study, we propose to develop an on-line numerical model of the input-output relationship, known as the **system** transfer function. The controller of on-line model has an Atmel processor 89S51 with 33MHz frame rate. It communi- cates with a laptop computer via RS232 at 9600 baud. This controller can interface with and **control** a wide range of equipment including variable rate applicators for precision agriculture. The on-line **system** model fits the experimental data to a pre-determined numerical model with undetermined coefficients. A very common numeri- cal model can describe a large.

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Various equivalent circuits have been reported in the literature for PV cell **modeling**. For instance, there is one-diode, two-diode and three-diode model [7, 18]. The equivalent circuit of two-diode and three-diode model can be used to improve the PVG model accuracy [18]. However, due to their strongly **nonlinear** character, it is very difficult to exploit these model for the **control** law design [7]. Therefore, the one-diode model is adopted in this study, since it provides sufficient accuracy besides an easier adaptation to the **control** design. Figure-3 depicts the corresponding electrical circuit [19].

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In this work, a novel **nonlinear** **modeling** technique in NANC is proposed to overcome the drawbacks of NLFXLMS algorithm. Sequentially, the proposed model is used to develop a controller algorithm based on Tangential hyperbolic function (THF). The work is restricted to single input, single output (SISO) ANC **system**. The feedforward strategy is used to **control** the noise at the observer [30]. All the transfer function and filters are assumed to be linear except the loudspeaker which is represented by a memory less saturation nonlinearity. The work involves designing and simulating the proposed **modeling** technique. At the **control** stage, an alternative THF-NLFXLMS algorithm is proposed and compared with NLFXLMS and FXLMS when ANC **system** deals with loudspeaker nonlinearity. Figure1.1, illustrates the research scope which is covered in this argumentation.

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analyses are much more difficult. This paper presents the use of neuro-fuzzy networks as means of implementing algorithms suitable for **nonlinear** black-box prediction and **control**. In engineering applications, two attractive tools have emerged recently. These two attractive tools are: the artificial neural networks and the fuzzy logic **system**. One area of particular importance is the design of networks capable of **modeling** and predicting the behavior of systems that involve complex, multi-variable processes. To illustrate the applicability of the neuro-fuzzy networks, a case study involving air-fuel ratio is presented here. Air-fuel ratio represents complex, **nonlinear** and stochastic behavior. To monitor the engine conditions, an **adaptive** neuro-fuzzy inference **system** (ANFIS) is used to capture the **nonlinear** connections between the air-fuel ratio and **control** parameters such manifold air pressure, throttle position, manifold air temperature, engine temperature, engine speed, and injection opening time. This paper describes a fuzzy clustering method to initialize the ANFIS.

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Two phenomena can produce chattering: switching of input **control** signal and the large amplitude of this switching (switching gain). To remove the switching of input **control** signal, dynamic sliding mode **control** (DSMC) is used. In DSMC switching is removed due to the integrator which is placed before the plant. However, in DSMC the augmented **system** (**system** plus the integrator) is one dimension bigger than the actual **system** and then, the plant model should be completely known. To overcome this difficulty, a fuzzy **system** is employed to identify the unknown **nonlinear** function of the plant model and then, a robust **adaptive** law is developed to train the parameters of this fuzzy **system**. The other problem is that the switching gain may be chosen unnecessary large to cope on the unknown uncertainty. To solve this problem, another fuzzy **system** is proposed which does not need the upper bound of the uncertainty. Moreover, to have a suitable small enough switching gain an **adaptive** procedure is applied to increase and decrease the switching gain according to the **system** circumstances. Then, chattering is removed using the DSMC with a small **adaptive** switching gain (ASG). As a case study, **nonlinear** chaotic Duffing-Holmes **system** is selected.

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Moreover, intelligent based techniques such as fuzzy logic, neural network and genetic algorithm have been applied to the active steering **system**. M.K.Park et al. (1996) presented a fuzzy-rule-based cornering force estimator to avoid using an uncertain highly **nonlinear** expression, and neural network compensator is additionally utilized for the estimator to correctly find cornering force. The result indicated that the proposed **control** **system** is robust against the uncertainty in vehicle dynamic model disturbances such as a side wind gust and road conditions.

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Abstract: Twin Rotor Multi-input-multi-output **System** (TRMS) resembles a desktop version of a non-linear helicopter model. It consists of two rotors namely tail rotor and main rotor. A model based on first principle **modeling** approach is designed in MATLAB Simulink environment. An **adaptive** **control** scheme based on Model Reference **Adaptive** Systems (MRAS) theory is implemented for tracking the desired trajectory with changing **system** parameters and disturbances. MRAC based Lyapunov Theory is used to adjust the gains for guaranteeing stability and converges the error to zero under stochastic conditions. The output of the TRMS Model is far better for MRAC based Lyapunov Approach than compared to PID. The main objective of this project is to stabilize the non-linear TRMS model.

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For the estimation of preferences, it divided into two steps which are initial estimation and dynamic estimation. We have to pretest the learners’ interests before them using the **system** for learning and also initialize learner’s interests. With the proceed of learner’s learning activities, we can discover learner’s interests. Then correct and maintain those values through data mining about the data of learners’ searching concepts, browsing websites’ types and topics of discussions. For the initializing values, we can gather user’s interests information through user’s registration forms or scale, and use direct or indirect matching methods to process those original data to acquire the initial values. For dynamic values, it concerns about learner’s learning procedure information and historical testing information. Then processes the acquired values through data mining model to obtain the needed data.

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This paper is organized as follows. In Section 2, the Bai's **system** for which we are developing the **adaptive** controler is presented. In Section 3 the proposed technique using the approximate inverse method is given. In Section 4 the proposed technique using the estimate of the plant noise is presented and Section 5 includes illustrative example and the simulation results for the developed technique.

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The issue of energy saving & energy conservation is prime concern in electrical power **system**. Researchers start taking interest in efficient use of renewable energy sources & improvement in its implementation techniques. Apart from the different renewable energy sources, photovoltaic systems are prime renewable energy source. PV cells are pollution & noise free sources of energy. Due to increasing demand in energy, government in developing countries like India also shown keen interest to individual solar power plants. PV **system** used in a solar cell connected in array like structure. Major advantage of integrating PV **system** in array format is that they can be interfaced with energy converter **system** easily. The use of PV **system** can be found in both AC converters as well as DC inverters. The topology has easier controller design as the two converters have independent **control** goals and architectures. Yet, the **system** has poor efficiency, due to a large number of devices, excessive size, heavy weight, and high cost. [1]

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Our objective is to define a basic scenario in order to understand various problems faced by traditional traffic light **control** **system** and to provide technical solution to amend these adversities to improve the speed of transport. Congestion due to vehicular traffic intersection at junction is one the major issues to be considered. To overcome such issues an intelligent traffic signal network is required. **Adaptive** traffic light **control** (ATLC) **system** deals with **manipulation** of the green light timings according to the number of vehicles present at each entry. Our **system** consists of ARM Cortex based KL25Z controllers, Laser Sensors and Xbee transmitters- receivers. The supporting program of our **system** is compiled in ‘mbed portal’ and the simulation is carried out with the help of ‘Proteus’ application. Our **system** ensures the effective use of allocated delays to deal with these adversities.

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During the last phase of gear shift procedure, just after the gear changed and gearbox synchronized, the clutch is going to lock so that the torque can flow from the engine to the wheels. After changing gear, there are two different speeds on the flywheel and the clutch plate and this speed difference have to be ironed out, i.e. the clutch synchronizes. As mentioned, there have been researches based on gear shifting just by engine **control** or Integrated Power **Control** scheme [14- 18]. These approaches are feasible mainly in hybrid drive-trains in which the difference can helpfully be lowered by adjusting the speed difference “externally” i.e. by engine or electric motor actuation. However, in general case of **control** of an AMT, what the present study aims at, this difference has to be mostly disappeared by making frictional contact which clutch plate produces while it touches flywheel. In other words, clutch synchronization would synchronize the speeds by itself. This makes the **control** program much more complicated as an appalling shuffle would emerge by an unsuccessful engagement.

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The block diagram of proposed **system** is illustrated in Figure 1 and **system** parameters is described in Table 1. The **system** parameters consist of boost converter, **nonlinear** observer, controller, and reference model parameter. Boost converter parameters are calculated based on continuous conduction mode (CCM). The method to select boost converter parameters can be found in [18]. The selection of **nonlinear** observer parameters are based on (11) and (16). Eq. (11) mentioned that Lyapunov function has to be positive definite, thus **adaptive** law gains are selected to be positive definite. Eq. (16) are obtained to be negative definite, thus in order to ensure (16) to be negative definite, the observer gains has to be selected into positive definite. Therefore, in Table 1, **adaptive** law gains and observer gains are selected to be positive definite. The selection of proposed controller paramaters follows (4) and (21). The photovoltaic rating is provided in Table 2. The polycrystalline solar panel is used for this paper. The numerical simulation is conducted to prove the effectiveness of the proposed method. The **system** will be tested under six scenarios, which in detail will be described in Section 3.

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ABSTRACT: Level **control** in a hemispherical tank is a challenging task in process industries due to **nonlinear** variation of level with height. The need for an accurate and appropriate online or off line model to **control** such **nonlinear** process is on huge demand to avoid process complications in practical applications such as oil refineries, dye industries etc. In this work, a hemispherical tank of 40 liter capacity was subjected to dynamic analysis and level measurement was done using an on-line Honeywell capacitance sensor. **Modeling** was performed using first principle of mathematics, open loop analysis and Skogestad technique. The various models were validated using standard performance indices and it was observed that Skogestad model performed better with minimum model error. Real time level **control** was implemented using various robust, **adaptive** and intelligent controllers such as Smith, IMC and NMPC. The performance of the controllers was evaluated using time domain specifications. Error analysis was also performed and it was observed that NMPC and IMC controller outperformed the other controllers.

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