Thenon-linear characteristics of **wet** **scrubbing** **process** have led to the application of intelligent **control** technique to adequately deal with these complexities by manipulating the liquid droplet size for the eﬀective **control** of particulate matter (PM) contaminants. This includes the use of **adaptive** **neuro**-**fuzzy** inference system (ANFIS) to design an intelligent controller based on direct inverse model **control** strategy using default input and output membership functions (gaussmf and linear) and diﬀerent number of input membership functions. This is followed by training of the **fuzzy** inference system to obtain inverse model which was tested as the intelligent controller. The controller developed using two-input membership functions have successfully achieved the main target of setting the PM concentration (**process** output) below the set point which is the allowable World health organization (WHO) emission level for 20g/μm within a short settling time of 2s. © 2015 IEEE.

Show more
A suspension system is a mechanism which consist of spring and damping element connected between wheel and car body. The suspension plays an important role to **control** the vertical dynamics of car body. The performance and characteristics of suspension system mainly depends on ride comfort and stability **control** of vehicle [1]. A better ride comfort can be achieved by using soft suspension, whereas better stability can be achieved with the help of hard suspension. The design of suspension involves optimization **process** where the elements are selected between soft and hard suspension. A suspension is normally classified into passive suspension, semi-active suspension and active suspension. Nowadays, lot of research works are going on [2-5] active suspension system because of its ability to operate wide range of frequency and forces. The performance of active suspension system obtained by measuring suspension travel and acceleration of vehicle body. Due to the development of microcontroller and computers [6-8], the real time implementation of active suspension can be done more effectively. The effect of ride comfort on suspension can be measured with the help of body acceleration of vehicle. Similarly, the performance of stability can be measured with the help of suspension travel.

Show more
The flowchart of ANFIS procedure is shown in Figure 4. AN FIS distinguishes itself from normal **fuzzy** logic systems by the **adaptive** parameters, i.e., both the premise and consequent parameters are adjustable. The most remarkable feature of the ANFIS is its hybrid learning algorithm. The adaptation **process** of the parameters of the ANFIS is divided into two steps. For the first step of the consequent parameters training, the Least Squares method (LS) is used, because the output of the ANFIS is a linear combination of the consequent parameters. The premise parameters are fixed at this step. After the consequent parameters have been adjusted, the approximation error is back-propagated through every layer to update the premise parameters as the second step. This part of the adaptation procedure is based on the gradient descent principle, which is the same as in the training of the BP neural network. The consequence parameters identified by the LS method are optimal in the sense of least squares under the condition that the premise parameters are fixed.

Show more
(22.2 m 3 s 1 ). Parameter a takes the value 0.129524267 and b T = 0.015 kg min 1 m 2 . The heat transfer coefﬁcient is UA = 25 kW K 1 . Finally, we assume that unknown system and sensor dynamics contribute an overall dead time of 0.5 min in both temperature and humidity measurements (i.e., d T = d w = 0.5 min). Also, we assume that no crop was present in the greenhouse at the time of experiment, but the concrete ﬂoor was continuously wetted to simulate a greenhouse with a **wet** soil surface. Therefore, the results pre- sented here are supposed to apply to a greenhouse with small seedlings, which do not inﬂuence the greenhouse climate. The greenhouse climate **control** variables consisted of humidiﬁca- tion and forced ventilation. Suppose this study focuses on daytime **control** under summer conditions, heating was not considered. A ﬁrst simulation experiment has been conducted to demonstrate the ability of the proposed **control** schemes to provide interacting **control** and smooth closed-loop response to set point step changes. For the proposed G-ANFIS con- troller, the dual parameters for each controller are obtained using GA through 50 generations by minimizing the mean square errors,

Show more
13 Read more

Navigation and obstacle avoidance are very important issues for the successful use of an au- tonomous mobile robot in a dynamic and unstructured environment. Mobile robot researchers aim to build an autonomous and intelligent robot which can plan its motion in a dynamic en- vironment. A successful use of an autonomous mobile robot depends on its controller. Mobile robot **control** is diﬃcult as they are subjected to non-holonomic (non-integrable) kinematic con- straints involving the time derivates of conﬁguration variables [12] and dynamic constraints. Both analytical like potential ﬁeld method as well as graph-based techniques have been used to solve the navigation problems of robot involving both static and dynamic obstacles. But, all such methods may not be suitable for on-line implementations due to their inherent computational complexity and limitations. Mobile robot researchers have carried out various researches in this direction using various intelligent techniques methods such as **fuzzy** logic, neural network and genetic algorithm and their diﬀerent hybrids. Because of the non-linear kinematics of the robot, the uncertainty in sensors readings, and unstructured environmental constrains in the **control** of mobile robot navigation; researchers have found **fuzzy** logic as one of the best intelligent tech- nique for handling the constraints. However, **fuzzy** logic needs tuning for optimal performance. Hand tuning is very diﬃcult and time consuming therefore there is need for automation of the tuning **process**. The **process** of tuning requires learning brought about by training or adaptation of the robot to adapt to its dynamic environment. The poor learning capability of **fuzzy** logic is compensated for by hybridizing **fuzzy** logic with other soft computing techniques with excellent learning features such as neural network. In this paper, we present an **adaptive** **neuro**-**fuzzy** controller with genetic algorithm learning for the navigation of Khepera mobile robot.

Show more
12 Read more

The basic learning rule of **adaptive** network is back- propagation algorithm where the model parameters are updated by a gradient descent optimization technique. However, due to the slowness and tendency to become trapped in local minima its application, gradient descent optimization technique, is limited. A hybrid learning algorithm, on the other hand is an enhanced version of the back propagation algorithm [7]. It is applied to adapt the premise and consequent parameters to optimize the network [8]. The hybrid learning rule combines the back- propagation gradient descent method and the least squares estimate (LSE) to update the parameters in the **adaptive** network. Each epoch of the hybrid learning procedure is composed of a forward pass and a backward pass. The forward pass of the learning algorithm stop at nodes at layer 5 and the consequent parameters are identified by least squares method. After identifying the consequent parameters, the functional signals keep going forward until the error measure is calculated. In the backward pass, the error rate, i.e., the derivative of the error measure with respect to each node output propagates backward from the output end toward the input end, and the premise parameters are updated by the gradient descent method. Heuristic rules are used to guarantee fast convergence. The details of the hybrid rule are given by [9]. The activities in each pass are summarized in Table 1 and flow diagram of ANFIS computations are shown in Fig.2.

Show more
The neurofuzzy network used in the structure of the proposed hybrid filter acts like a mixture operator and attempts to construct an enhanced output image by combining the information from the new tri-state median (NTSM) filter. The rules of mixture are represented by the rules in the rule base of the **neuro**-**fuzzy** network and the mixture **process** is implemented by the **fuzzy** inference mechanism of the **neuro**- **fuzzy** network. These are described in detail later in this subsection. The **neuro**-**fuzzy** network is a first order Sugeno type **fuzzy** system [49] with one input and one output. In **neuro**-**fuzzy** network, there are two types of **fuzzy** inference systems are widely used. Mamdani method is widely accepted for capturing expert knowledge. It allows us to describe the expertise in more intuitive, more human-like manner. However, mamdani-type **fuzzy** inference entails a substantial computational burden. On the other hand, the Sugeno method is computationally effective and works well with optimization and **adaptive** techniques, which makes it very attractive in **control** problems, particularly for dynamic nonlinear systems. Sugeno-type **fuzzy** systems are popular general nonlinear modeling tools because they are very suitable for tuning by optimization and they employ polynomial type output membership functions, which greatly simplifies defuzzification **process**. The input-output relationship of the **neuro**-**fuzzy** network is as follows. Let A 1 denote the inputs of the **neuro**-

Show more
The paper includes the prediction and **control** of engine air-fuel ratio. Modeling is done by **fuzzy** clustering and ANFIS. Firstly, inputs and outputs factors of a gasoline engine are replaced as part of system. Later, these factors are grouped into optimal numbers independently by using **fuzzy** clustering algorithm as a preprocessing step. Later on, these optimal numbers of clustered parameters are used as inputs and outputs of ANFIS for the prediction and **control** **process**. Inputs of the system are Manifold Air Pressure (MAP), Throttle Position (TPS), Manifold Air Temperature (MAT), Engine Temperature (CLT), Engine Speed (RPM), and Injection Opening Time (PW) whereas output is AFR, as shown in Figure 2.

Show more
They are applied to important fields such as variable speed drives, **control** systems, signal processing, and sys- tem modeling. Artificial Intelligent systems, means those systems that are capable of imitating the human reasoning **process** as well as handling quantitative and qualitative knowledge. It is well known that the intelligent systems, which can provide human like expertise such as domain knowledge, uncertain reasoning, and adaptation to a noisy and time-varying environment, are important in tackling practical computing problems. ANFIS has gain a lot of interest over the last few years as a powerful technique to solve many real world problems. Compared to conven- tional techniques, they own the capability of solving prob- lems that do not have algorithmic solution. Neural net- works and **fuzzy** logic technique are quite different, and yet with unique capabilities useful in information **process**- ing by specifying mathematical relationships among nu- merous variables in a complex system, performing map- pings with degree of imprecision, **control** of nonlinear system to a degree not possible with conventional linear systems [5-11]. To overcome the drawbacks of Neural networks and **fuzzy** logic, **Adaptive** **Neuro**-**Fuzzy** Infer- ence System (ANFIS) was proposed in this paper. The ANFIS is, from the topology point of view, an implemen- tation of a representative **fuzzy** inference system using a Back Propagation neural network structure.

Show more
Real-time experiment configuration consists of computer with MATLAB, Simulink and Quanser Toolbox used as a controller, Q8 data acquisition board and Quanser IP02 Linear Motion Servo Module. Some hardware limitations should be concerned in the cart- pendulum system. The Digital-to-Analog voltage for data acquisition board is limited between -10 V and 10 V. The safety watchdog is turned on where the allowable cart displacement is 0.35 m from the centre of the track. When the pendulum or cart touches the limit switch, the **control** **process** is aborted. Figures 11 to 13 show the SESIP **control** system experimental results.

Show more
Furthermore, the parameter requirement of the varying reluctance machine controller is determined. So the **process** consists a standardized recurrence of the switch just as inspection with low recurrence. In [8], a new flexible globally SM **control** approach based on a global SM **control** framework and a versatile tracker is implemented to gain predominance after execution **control** of unsafe and nonlinear time-fluctuating frameworks. Given the normal and exponential term of the primary request, a flexible variable-evaluated exponential methodology law is implemented that has the ability to adjust the sliding surface and the shift in the framework state. The presented method can normally regulate the velocity and weaken the chattering of the structure. Furthermore, on the assumption of the novel arriving at law, AESM **control** is suggested for BIM velocity **control**. In order to enhance the execution of sliding mode **control** against obstruction, an eyewitness aggravation SM is constructed, and its outcome is utilised as AESM **control** feed forward compensation.

Show more
metrological data and (2) Make **fuzzy** and **neuro** **fuzzy** model and their results will be evaluated. The **fuzzy** rule- based approach is applied for the construction of **fuzzy** models. To remove the weaknesses of **fuzzy** models that are not trained during the modeling, **adaptive** **neuro**-**fuzzy** inference system (ANFIS) with given input/output data sets will be use for **neuro**-**fuzzy** model. To do this, **Fuzzy** models was studied with input parameters such as daily maximum and minimum temperature, relative humidity percent, sunshine hours, wind speed. As well as output of this **fuzzy** system such as Evapotranspiration will studied in this research. After determining effective parameters in

Show more
ANFIS is the **fuzzy** logic based paradigm that grasps the learning abilities of ANN to enhance the intelligent system’s performance using a priori Knowledge. Using a given input/output data set, ANFIS constructs a **fuzzy** inference system (FIS) whose membership function parameters are tuned (adjusted) using either a backpropagation algorithm alone, or in combination with a least squares type of method. This allows the **fuzzy** systems to learn from the modeled data. The parameters associated with the membership functions will change through the learning **process**. The computation of these parameters (or their adjustment) is facilitated by a gradient vector, which provides a measure of how well the **fuzzy** inference system is modeling the input/output data for a given set of parameters. Once the gradient vector is obtained, any of several optimization routines could be applied to adjust parameters that will reduce some error measure (usually defined by the sum of the squared differences between actual and desired response).

Show more
where wn is the output of layer three and [pi and rj] is the i node parameter set. Finally, every node in layer five sums all the incoming signals so that a weighted sum defuzzification technique is performed. The parameter sets o f the FLC antecedents and consequents are tuned or learned using the BP learning algorithm. In [Jang, 1992], the same FFNN configuration was employed but the learning algorithm was a hybrid algorithm. This learning algorithm combined both the BP and the least-square estimation algorithm. In [Yaochu et al., 1995], two interconnected NN were employed to represent a TS-model based FLC. One network represents the antecedent part and the other represents the consequent linear equation. The two NN are then connected through n or product neurons. The BP learning algorithm was employed for learning the parameters o f the consequents and antecedents. Using a TS-model based FFNN has the advantage that it allows a relatively easy mathematical design and stability analysis [Wang and Langari, 1996]. Also, it allows a straight forward application of powerful learning algorithms such as BP due to its differentiable inference functions. On the other hand, a disadvantage o f this model is that the interpretation o f the **fuzzy** linear rules is difficult compared to that for linguistic rules. Also, the rule base o f this model can only be constructed using only numerical input/output data and it is not possible to incorporate linguistic information from human experts to construct such a model.

Show more
317 Read more

The responce of the system when we use ANFIS **control** is better than FLC **control** but with an overshooting in the dynamic response. In the both system of controls produce a maximum power point voltage of 20.17V as shown in Fig. 10a and Fig. 11b corresponding to characteristic P-V and I-V. The outputs of FLC and ANFIS regulators are connected to the boost converter, so they produce a duty cycle as shown in Fig. 13d. At steady state conditions output power reached the value of 112 W.