This paper proposes the **artificial** **intelligence** (AI)-**based** **optimal** **PID** **controller** **design** optimization of brushless direct current (**BLDC**) **motor** speed control with **phase** **advance** approach. The proposed control system allows the speed adjustment of the **BLDC** **motor** by **phase** **advance** technique. In this paper, two selected AI algorithms, i.e., the adaptive tabu search (ATS) and the intensified current search (ICS) are conducted as the optimizer for the **PID** **controller** **design**. The proposed control system is simulated by MATLAB/SIMULINK. Results obtained by the ATS and ICS will be compared with those obtained by the Ziegler-Nichols (ZN) tuning rule and the genetic algorithm (GA). It shows that the speed response of the **BLDC** **motor** by **phase** **advance** with the **PID** **controller** optimized by the ICS outperforms better than the ZN, GA and ATS.

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In these works, the Model-**Based** **Design** approach in development of **BLDC** **Motor** **PID** **controller** using low cost embedded Arduino Mega **controller** has been presented. The comparison between simulated and real-time obtained data shows that the **motor** speed in actual implementation is not significantly different than the simulation result. However, both response are able to track their given input command and are acceptable. Performance indices using Root Mean Squared Error are evaluated and tuned **PID** **controller** compared between simulation and actual **motor** speed response. This work clearly demonstrates that Model-**Based** **Design** method which includes task of modeling, control **design** and rapid- prototyping of designing control system can be easier performed in MATLAB/ Simulink environment using any supported embedded microcontroller with their simulink block-set. The advantages of using Arduino **controller** with Simulink Arduino Target is an inexpensive, open- source microcontroller board and allows the creation of applications in the Arduino platform **based** on a visual programming environment with block diagrams. Furthermore, this method is feasible to the development of complex control system **design** such as **artificial** **intelligence** and **controller** optimization.

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Abstract Brushless DC motors are widely used for many industrial applications because of their high efficiency, high torque, higher speed ranges, noiseless operation and low volume. More advanced controllers are used to manage acceleration, control speed and fine-tune efficiency of **BLDC** **motor**. A proportional–integral–derivative **controller** is a generic control loop feedback mechanism widely used in industrial control systems because of its simple structure and easy implementation. The conventionally tuned **PID** **controller** is not providing optimum performance under nonlinear conditions and parameter variations. The aim of this research is to develop a complete model of the **BLDC** **motor** and to **design** an **optimal** **controller** for its control. The Genetic Algorithm is proposed as a global optimizer to find the optimized **PID** gains for control of **BLDC** **motor**.

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Hall Effect sensors were used for low cost, low resolution requirements and optical encoder for high resolution requirements [4]. Sensor signals are used to adjust PWM sequence of 3-**phase** bridge inverter . In sensor-less control back-emf sensing, back-emf integration, flux linkage- **based**, freewheeling diode conduction and speed independent position function technique are used for electronic commutation [5]. Due to electronical commutation, **BLDC** has more complex control algorithm compared to other **motor** types [3, 5]. In practice, the **design** of the BLDCM drive involves a complex process such as modeling, control scheme selection, simulation and parameters tuning etc. Recently, various modern control solutions are proposed for the speed control **design** of **BLDC** **motor** [1-3]. However, Conventional **PID** **controller** algorithm is simple, stable, easy adjustment and high reliability. Conventional speed control system used in conventional **PID** **controller**. But, in fact, most industrial processes have been with different degrees of nonlinear, parameter variability and uncertainty of mathematical model of the system. Tuning **PID** control parameters is very difficult due to its poor robustness; therefore, it is difficult to achieve the **optimal** state under field conditions in the actual production [6]. So far, there have been many different **design** methods and control schemes to overcome the uncertain nonlinear control problems such that neural network control system has a strong ability to solve the structure uncertainty but it requires more computing capacity and data storage space. For genetic algorithms, ant-colony algorithms, techniques can help improving performance but they also need longer computation time and larger storage capacity [7-11].

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Generally, in high-power applications, var compensation is achieved using multilevel inverters [2]. These inverters consist of a large number of dc sources which are usually realized by capacitors. Hence, the converters draw a small amount of active power to maintain dc voltage of capacitors and to compensate the losses in the converter. However, due to mismatch in conduction and switching losses of the switching devices, the capacitors voltages are unbalanced. Balancing these voltages is a major research challenge in multilevel inverters. Various control schemes using different topologies are reported in [3]–[7]. Among the three conventional multilevel inverter topologies, cascade H-bridge is the most popular for static var compensation [5], [6]. However, the aforementioned topology requires a large number of dc capacitors. The control of individual dc-link voltage of the capacitors is difficult. Static var compensation by cascading conventional multilevel/two level inverters is an attractive solution for high-power applications. The topology consists of standard multilevel/three level inverters connected in cascade through open-end windings of a three-**phase** transformer. Such topologies are popular in high-power drives [8]. One of the advantages of this topology is that by maintaining asymmetric voltages at the dc links of the inverters, the number of levels in the output voltage waveform can be increased. This improves PQ [8]. Therefore, overall control is simple compared to conventional multilevel inverters.

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[17]. Practical experiences suggest that they reach stagnation after certain number of generations as the population is not converged locally, so they will stop proceeding towards global **optimal** solutions. The stochastic search methods are proven in reaching global solutions for certain difficult real world optimization problems [18]. Hence this article comes up with a hybrid approach involving PSO-DE and BFOA algorithm for solving non-convex DED problem considering valve-point loading effects, ramp-rate limits, prohibited operating regions and spinning reserve capacity.

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Particle swarm optimization (PSO) is a metaheuristic algorithm **based** on swarm behaviour observed in nature such as in bird flocking or fish schooling. It attempts to mimic the natural process of group communication of individual knowledge, to achieve some optimum property. PSO searches the space of an objective function by adjusting the trajectories of individual agents, called particles. Each particle traces a piecewise path which can be modelled as a time-dependent position vector.

The simulation of **controller** for **BLDC** fan using controlled rectifier. The rectifier circuit consists of SCR to convert the AC voltage into a fixed DC voltage. The fixed DC voltage is converted to variable DC voltages of 8V, 10V, 12V using a chopper. The voltage obtained from the chopper is given to the **BLDC** fan from which we will achieve the different speeds

The **artificial** neural network has the ability of learning and function approximation. In addition, the **artificial** neural network learning processes are independent of human intervention and expert experiences. For such situations, many studies use ANN to approximate **PID** formula to realize ANN-**PID** **controller**. But the learning method of ANN usually adopts some traditional algorithm, including the delta rule, the steepest descent methods, Boltzman’s algorithm, the back-propagation learning algorithm, the standard version of genetic algorithm [9][10][11], etc. These traditional learning methods of ANN exists some deficiency including such as the problem of the slow speed of convergence, local minima, and the large amount of computation of network, etc, which lead to ANN-**PID** **controller** is difficult to use actually[15][16]. In this paper, a new ANN **PID** **controller** which is **based** on the differential evolution algorithm (DEA) is proposed. Here, **artificial** neural network is used to approximate **PID** formula and using DEA to train the weights of ANN. The simulation proves this **controller** can get better control effect, and it is easily realized and the less amount of computation.

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In spite of developed modern control techniques like fuzzy logic controllers or neural networks controllers, **PID** controllers constitute an important part at industrial control systems so any improvement in **PID** **design** and implementation methodology has a serious potential to be used at industrial engineering applications [9]. At industrial applications the **PID** controllers are preferred widespread due to its robust characteristics against changes at the system model. There is another reason why this project using **PID** **controller** instead another method. The first is the three terms are reasonable intuitive, allowing a no specialist grasp the essentials of the **controller**‟s action. Second, **PID** has a long history, dating back to a pre- digital, even pre-electronic period and lastly the introduction of digital control has enhanced **PID**‟s capabilities. In general the advantages of **PID** **controller** can be summarized as follows:

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Suppose that the feed forward **controller** is successfully trained so that the plant output y=d. Then the network used as the feed forward **controller** will approximately reproduce the plant input from y (i.e., t=U). Thus, training of the network by adapting its weights might be considered to minimize the error = U - t using the architecture as illustrated above, because, if the overall error E = d - y goes to zero, so does c l. The positive features of this arrangement would be the fact that the network can be trained only in the region of interest since it is started with the desired response d and all other signals are generated from it. Here it can be analysed that it is advantageous to adapt the weights in order to minimize the error directly at the output of the network. Unfortunately, this method, as described, is not a valid training procedure because minimizing cl does not necessarily minimize E. For instance, simulations with a simple plant showed that the network tends to settle to a solution that maps all d's to a single U = uo, which, in turn, is mapped by the plant to t = U for which E, is zero but obviously E is not. This training method remains interesting, however, because it could be used in conjunction with one of the procedures described below that minimize E.

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Control of **BLDC** **motor** has always remained an active area of research. Especially the **PID** control of **BLDC** **motor** has been studied extensively for the optimization of different parameters of proportional gain, integral time and derivative time. Researchers have used different optimization approaches to optimize these parameters. Researchers have drawn inspiration from naturally occurring phenomena in solving these optimization problems. mimicking the behavior of natural systems (or) naturally occurring phenomena have given rise to multiple optimization approaches like Particle Swarm Optimization (PSO) [1] Ant Colony Optimization (ACO) [2] Genetic Algorithm (GA) [3] Bacterial Foraging Optimization Algorithm (BFOA) [4] Differential evolution (DE) [5] Immune Algorithm (IA) [6] etc. These algorithms have adapted from naturally occurring process. They can be referred using different names with the names like Evolutionary Algorithms and metaheuristic approaches being commonly used. The metaheuristic approaches typically combine heuristic algorithms which are usually problem specific in a more generalized frame work. So, metaheuristics can be considered as processes which strategies to find an optimum (or) a near optimum solution. These metaheuristic approaches are approximate and non-deterministic and they usually employ mechanisms to have a good convergence and provide near optimum solutions.

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Induction **motor** is used in many fields of industrial production system, due to their advantages like quick start, less maintenance etc. Long term working failure is very crucial for production system. Like other types of electrical machines these motors are exposed to a wide variety of environmental conditions electrical and mechanical errors. Because of these **motor** defects will bring about labour and maintenance cost. Bearing problems are also caused by improperly forcing the bearing onto shaft or into the housing. This produces physical damage. However voltage applied to the **motor** are not exactly the same, unbalanced currents will flow in stator winding, the magnitude depend upon the amount of voltage unbalance this effect may overheat to the point of burnout. The main aim of this work is to develop software **based** model for prediction and detection of unbalance voltage condition and bearing faults in induction **motor**. The objective of this to present feed forward neural network and ANFIS techniques for accurate detection and classification of this fault.

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A systematic approach of achieving the speed control of brushless dc **motor** by means of adaptive neuro fuzzy inference control system has been investigated in this paper. Simulink model was developed in Matlab with the ANFIS **controller** for the torque control of BLDCM. The control strategy was also developed by writing a set of 9 fuzzy rules according to the ANFIS control strategy with the back propagation algorithm in the back end. The main advantage of designing the ANFIS coordination scheme is to increse the performance of the BLDCM & to increase the dynamic Performance, Simulations were run in Matlab & the results were observed on the corresponding scopes. The outputs take less time to stabilize, which can be observed from the simulation results. Due to the incorporation of the ANFIS **controller** in loop with the plant, it was observed that the **motor** reaches the rated speed very quickly in a lesser time compared to the Mamdani method.

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PXI. The servo systems are generally controlled by conventional Proportional Integral Derivative (**PID**) controllers. This system aimed to achieve some of the criteria when applied **PID** **controller** such as less steady state error, minimum settling time, minimum rising time and less overshoot at the system. To achieve the aims, the authors adjusted the **PID** **controller** parameters by using some other tuning techniques. The system provided output position in real time in order to obtain system responses of **PID** **controller**.

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Brushless DC(**BLDC**) motors are widely used for many industrial applications, In view of the problem that it is difficult to tune the parameters and get satisfied control characteristics by using normal conventional **PID** **controller**. a online identification method **based** on Radial Basis Function(RBF) has been proposed in this paper. In this method, connection weight of neural network was revised in time according to the speed of **motor** and **phase** current, the duty cycle of pulse width modulation (PWM) was adjusted to control the speed of **BLDC** **motor**. Conventional **PID** and RBF neural network **PID** algorithm were respectively adopted to make a comparison. the control approach was validated with simulation at first and then was implemented with a DSP TMS320F28035. Matlab simulations and experiment results showed that the proposed approach has less overshoot, faster response, stronger ability of anti-disturbance than the conventional **PID** **controller**.

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I declare that this thesis entitled “Analysis of Three **Phase** Inverter for Brushless DC(**BLDC**) **Motor**” is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.

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Abstract: Most of the industries used induction **motor** for various applications but nowadays induction motors are replaced by permanent magnet brushless DC (**BLDC**) **motor** because of its high speed-torque characteristic, reduced size and so on. **BLDC** **motor** is considered as DC **motor** but it runs on AC supply. **BLDC** **motor** is operated smoothly with a use of inverter whose gate pulses are given by feedback signal drawn from **motor** using hall sensors. Brushless DC (**BLDC**) engine control framework is comprised of a multi-variable, non-direct, solid coupling framework, which is utilized to show strong and versatile capacities. The enthusiasm for developing insightful **controller** for **BLDC** engine has been expanded essentially. Neural Control is an ANN (**Artificial** Neural Network) **based** control technique whereby the accessible information is the aftereffect of estimating the dynamic conduct of the framework. This capacity is appropriate to be connected to versatile control frameworks where the **controller** requires adjustment because of changes in framework conduct. ANN was utilized to manufacture the converse model of **BLDC** engine speed.

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The concept of the proposed system is to have closed loop control system of the **BLDC** **motor**. The electrical energy is generated by using solar photovoltaic array which converts energy of light into electrical energy. This signifies the adoption of renewable energy for the system. The solar photovoltaic array is followed by the Buck –Boost converter. The Buck Boost converter is a Dc-Dc converter. The output voltage magnitude of a buck-boost converter is either greater or lesser than the input voltage magnitude. The output of the buck boost converter is supplied to the driver circuitry. The driver circuitry is used **BLDC** **motor**. The Hall signals generated from the hall sensors of the **motor** is supplied to the **controller** which then controls the driver circuitry of the **motor**.

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