that has capability to move in a certain kind of environment and generally used to accomplish certain tasks with some degrees of freedom (DoF). Applications of **mobile** robots cover both industrial and domestic area. It may help to reduce risk to human being and to the environment. **Mobile** **robot** is expected to operate safely where it must stay away from hazards such as obstacles. Therefore, a **controller** needs to be designed to make the system robust and adaptive. In this study, **PID** **controller** is chosen to control a **mobile** **robot**. **PID** is considered as simple yet powerful **controller** for many kind of applications. In designing **PID**, user needs to set appropriate **controller** gain to achieve a desired performance of the control system, in terms of time response and its steady state error. Here, an optimization **algorithm** called **Bat** **Algorithm** with **Mutation** (BAM) is proposed to optimize the value of **PID** **controller** gain for **mobile** **robot**. This **algorithm** is compared with a well- known optimization **algorithm**, Particle Swarm Optimization (PSO). The result shows that BAM has better performance compared to PSO in term of overshoot percentage and steady state error. BAM gives 2.29% of overshoot and 2.94% of steady state error. Meanwhile, PSO gives 3.07% of overshoot and 3.72% of steady state error.

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In this project is to **design** and implementation of **PID** based two wheeled self-balancing **robot** to solve the inclination angle problem to balance the movement of **robot** and to implement in real time. We are designing the code and implement an efficient self-balancing **PID** **algorithm** **using** the embedded **controller** and to implement in real time. Accelerometer is fitted on the **robot** to measure the angle of tilt during load imbalance .It gives a summary of the work done in the fields of mechanical **design**, electronics, software **design**, system characterization and control theory. This wide array of fields necessary for the realization of the project holds the project up as a leading example in the field of mechatronics. Here special focus will be on the modelling of the robotic system and the simulation results of various control methods required for the stabilization of the system.

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The **PID** **controller** has been used to control about 90% of industrial processes worldwide [3]. The main problem of that simple **controller** is the correct choice of the **PID** gains and the fact that by **using** fixed gains, the **controller** may not provide the required control performance, when there are variations in the plant parameters and operating conditions. Therefore, a tuning process must be performed to insure that the **controller** can deal with the variations in the plant [4]. To tune the **PID** **controller**, there are numbers of strategies, the most famous, which is frequently used in industrial applications, is the Ziegler-Nichols method [3] , genetic **algorithm** GA, etc. Moreover, PSO was another method for tuning procedure. PSO first introduced by Kennedy and Eberhart is one of the modern heuristic algorithms, it has been motivated by the behavior of organisms, such as fish schooling and bird flocking [5]. Generally, PSO is characterized as a simple concept, easy to implement, and computationally efficient. Unlike the other heuristic techniques, PSO has a flexible and well-balanced mechanism to enhance the global and local exploration abilities [6]. In this paper, a novel PSO-based approach to optimally **design** a **PID** **controller** for a **mobile** **robot** trajectory tracking is proposed. This paper has been organized as follows: in section 2 both kinematics and dynamic models of **mobile** **robot** are described. In section 3, the particle swarm optimization method is reviewed. Section 4, describes how PSO is used to **design** t`he **PID** **controller** optimally for **mobile** **robot** to control the velocity and

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Kinematics refers to the evolution of the position, and velocity of a mechanical system, without referring to its mass and inertia. The kinematic scheme of the **mobile** **robot** consists of a platform driven by two driving wheels mounted on same axis with independent actuators and one free wheel that is called a castor. The movement of **mobile** **robot** is done by changing the relative angular velocities of driving wheels. The assumptions are that the whole body of **robot** is rigid and motion occurs without sliding. Its wheel rotation is limited to one axis. Therefore, the navigation is controlled by changing speed on either side of the **robot**. The kinematics scheme of the differential drive **mobile** **robot** is as shown in Figure 1 where {O , X , Y } are the global coordinate and {P, , } are the local coordinate which are fixed to the **robot** with its centre P between the two wheels, r is the radius of each wheel and 2L is the distance between two driving wheels, ω is the angular velocity of the **mobile** **robot**, and are the velocity of the left and right driving wheel, the angle θ indicates the orientation of the **robot**, C represented the centre of the **mobile** **robot**, d is the distance from center of **mobile** **robot** to origin P.

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The fundamental essence of the contribution of this work is to modify and improve the performance of traditional **PID** and modern controllers by employing the theory of cognitive neural network topology as a basis for a learning and adapting system with the capability of planning fairly optimal trajectories that are desired to guide and manoeuvre a nonholonomic wheeled **mobile** **robot** through pre-defined trajectories (Lemniscates and Circular as a continuous and Square as a non-continuous gradients path) with collision- free navigation. This is done by finding the optimal torque control action that will minimise the tracking error (the travel time and travelling distance) of the **mobile** **robot** by utilising an optimisation predictive **algorithm** that works to curtail the error between desired trajectory and actual **mobile** **robot** trajectory, in addition to reducing the control effort (i.e. reducing the spike of torque control signals and thus saving battery energy of the **mobile** **robot** system) encountered, even in the presence of obstacles in the path.

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literature reviews [4-8] In section II, we will discuss theories related to this paper include of fractional calculus, fractional order PI λ D µ **controller**, digital IIR filter, and Kalman filter that necessary to eliminate the measurement error from the tilt sensor. Section III discusses in mechanical structure, mathematical model, and state - space of the **robot**. Section IV demonstrates **PID** and FOPID **controller** **design** and their simulation results. Section V demonstrates to realization implemented both controllers on the real system and result of **PID** **controller** on the real system.

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The problem of the control synthesis for a microwave exposure system has been considered. After describing the system main components, a suitable control-oriented mathematical model has been derived by making use of experimental data. As a result, a model-based **PID** control has been designed by **using** classical **PID** tuning methods in order to satisfy the given requirements. The control system performance have been finally verified via closed-loop experimental tests.

In this paper a trial has been made to **design** a simple self-tuning LabVIEW-based **PID** **controller**. The **controller** uses an open –loop relay test, calculates the tuned parameters in an open loop mode of operation before it updates control- ler parameters and runs the process as a closed-loop system. The **controller** reacts on a persistent offset error value as a result of load disturbance or a set point change. Practical results show that such a **controller** may be recommended to control a variety of industrial processes. A GUI was developed to facilitate control-mode selection, the setting of con- troller parameters, and the display of control system variables. GUI makes it possible to put the **controller** in manual or self-tuning mode.

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With the continuous development of information science, the issues that need to be addressed have become increasingly difficult, the requirements of control system have also been more and more demanding. Besides, as the multiple analytic methods require convenient operability, the traditional control system tends to be a bit powerless in implementing various functions. In the traditional control system the signal processing circuit **design** is very complicated and has great deal of difficulty in its updating process. The traditional instruments have no way to upgrade or update to the new calculating method or computing requirements that arises from time to time. Even cost of implementation is high. Based on the rapid development of microelectronic technology, computer technology, software technology, network technology and modern measurement technology, a new kind of advanced instrument called the virtual instrument, has been a hot spot of current system research. As a combination of various technologies and computer

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Nowadays, there are many controllers have been designed to achieve desired performance for a coupled tank system liquid level, such as sliding mode **controller**, fuzzy logic **controller**, proportional-integral (PI) **controller** and proportional-integral-derivative (**PID**) **controller**. **PID** control is one of the controllers that can be applied for Single-input Single-output (SISO). In industry, several factors need to be considered in **controller** **design** such as time response characteristics, set point tracking and load disturbance to make a system with high productivity. Actually, it is very hard to find the best value for the **PID** parameters, so optimization technique is required to simplify this problem. For this research, **Bat** **Algorithm** will be used as an optimization technique to get the best parameter of **PID** for this system. **Bat** **algorithm** is that the new **algorithm** that has been discovered by Xin-She Yang but not applied yet into coupled tank system. Based on previous research, **Bat** **algorithm** shows the best way of finding parameter of the system. Apart from that, it gives a robust performance for a wide range of operating conditions. Furthermore, it is very familiar and easy to implement **using** analogue or digital hardware.

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This paper discusses the **design** of 4 Degree of Freedom (DOF) arm **robot** manipulator in term of the **controller**, mechanics, and motion analysis. The **controller** **design** combines the conventional **PID** **controller** with FLC to create robust and stable **robot** motion. The most straightforward control system on robots is the **PID** system functioning as a regulator of servo motor movement installed on the links of the arm **robot**; therefore, error in moving the **robot** can be reduced. **Robot** motion inside its workspace is simulated by SciLab’s RSTX toolbox [20] by deriving Denavit-Hartenberg parameters [21] from mechanical **design**.

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Trial and error method can be used to find a suitable **design** of a fuzzy **controller**. However, there are many options in- cluding fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimiza- tion **algorithm** facilitates this process and finds an optimal **design** to provide a desired performance. This paper presents a novel application of the Bacterial Foraging Optimization **algorithm** (BFO) to **design** a fuzzy **controller** for tracking control of a **robot** manipulator driven by permanent magnet DC motors. We use efficiently the BFO **algorithm** to form the rule base and MFs. The BFO **algorithm** is compared with a Particle Swarm Optimization **algorithm** (PSO). Per- formance of the **controller** in the joint space and in the Cartesian space is evaluated. Simulation results show superiority of the BFO **algorithm** to the PSO **algorithm**.

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Choi et al. [73] solved the navigation problem in a simple way. He has described whenever a **robot** challenges large, non-convex or dispersed obstacles as well as to find appropriate local minimum points within this area, always difficulties appear. Accordingly, he suggested **algorithm**, which covers two layer hierarchical systems to solve the problem and provide the name of the layer as, lower layer for avoiding or approaching and upper layer to combine this logic. Silva et al. [74] has proposed work for navigation of **mobile** **robot** **using** fuzzy logic. In this paper researchers describe how a **robot** uses its local information to control the steering and velocity while moving inside unknown environment. The proposed method is direct and effective and uses sensory data in order to **design** the fuzzy logic **controller**. Park and Zhang [75] developed behavior based dual fuzzy approach to navigate the **mobile** **robot** in unknown environment. Eight ultrasonic sensors, a GPS sensor and two fuzzy logic controllers with separate ‘81’ rules were used to realize this navigation system. Here two fuzzy control algorithms is used one for navigation and other for avoiding obstacle and edge detection. Qian and Song [76] have presented a research article based on sonar ring and its implementation for autonomous navigation. The local trap problem describe in this paper and uses sonar sensor to obtain the environmental information.

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Thus, the fire extinguisher **robot** senses fire immediately with the help of radar and simultaneously the fire is vanished by the centrigugal pump, pumping water out of the water tank. The **robot** can withstand even at very high temperature and it senses fire around 100meters. The LCD placed, shows the functioning of the **robot** step by step. It can easily travel between any obstacles and this is specially modelled for indoors. As the Fire Fighter **Robot** has to endure different situation, this effectiveness test will help us to make a better model in preventing local and indoor areas and prevents the atmosphere from catching fire.

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In the earlier research, position control has been widely used over force control to produce fast, accurate and repeatable motion. Moreover, position control works best in a well- organized and controlled work space because the controlled robots operate repeatedly in the same working area. Typical examples of tasks can be found in the automation manufacturing industry such as polishing, deburring, machining and assembly. However, position control will not suffice to extend the application of the **robot** outside of the controlled working environment. The use of pure position control can result in fluctuation of the contact force ultimately leading to dangerous behaviors such as breakage or instability. Therefore, many compliance controls for robotic hands were introduced by researchers in order to replicate safe human grasping during the interaction. Compliance control can be defined as a measure of the ability of a manipulator to react to interaction forces [1]. One way to attain compliance control is via an active compliance control by devising a suitable interaction control strategy. Force control is the key element of active compliance control. In force control, the desired force trajectory is commanded, and force is measured in real time to realize the feedback control [1].

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In [28] , a robust **PID**-based PSS is suggested to appropri- ately function over a wide range of operating conditions. Doubts in plant parameters, due to deviation in load patterns and generation, are expressed in the form of a polytopic struc- ture. The problem of **PID** control is initially decreased to a generalized static output feedback synthesis. In [29] , the authors suggested a simple analytical method for computing the set of three terms of robust stabilizing PSSs. Therefore, stabilization of the proposed interval plant by a **PID** **controller** and a phase lead compensator based PSS is dealt with **using** generalized Kharitonov’s theorem. Furthermore, necessary and sufﬁcient constraints for characterizing the robust stabilizing three term controllers are derived by applying the Routh–Hurwitz criterion to a set of segment/vertex plants.

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The software **design** of SLAM system mainly completes the transplantation of Cartographer **algorithm** to the self-built SLAM **mobile** **robot** platform. Cartographer **algorithm** is mainly divided into two parts: front-end and back-end. The front-end mainly completes the work of establishing submaps. A certain number of submaps can be used as basic units to form closed-loop detection. Finally, the process of graph optimization is accelerated by branch and bound **algorithm**. The program implementation process is as follows: First, the function AdHorizonLaserFan() of the local_trajectory_builder is called. This function calculates the **robot** pose predicted by the pose interpolator as the initial pose to the real-time scanner matcher. Then, the attitude optimized by the real-time scanner matcher is used as the initial pose of the Ceres solver. Finally, scan-to-map is implemented through the Ceres solver to get the final result, and the pose interpolator is updated.

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comparison with the conventional differentiator. Afterward, **PID** **controller** has been widely proposed in the literature for LFC of power systems. In [11], an intelligent **PID** **controller** based on the principle of anthropomorphic intelligence was suggested. Designing **PID** **controller** **using** particle swarm opti- mization **algorithm** is presented in [12] for LFC in an intercon- nected power system. In [13], Artiﬁcial Bee Colony (ABC) **algorithm** has been used to tune the automatic generation con- trollers in an interconnected reheat thermal power system. The results of this paper show the better performance of ABC in comparison with PSO. Several novel heuristic stochastic search techniques are presented in [14] for optimizing **PID** gains used in Sugeno fuzzy logic based automatic generation control (AGC) of multi-area system with thermal generating plants. In [15], uniﬁed tuning of **PID** was proposed for LFC in power systems via internal model control. LFC has been carried out by a new decentralized robust optimal MISO **PID** **controller** based on matrix eigenvalues and Lyapunov method in [16].

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ABSTRACT: This paper presents the simplest approach for the DC voltage control **using** the single phase single switch AC to DC flyback converter with isolation. This method has the combination of uncontrolled diode rectifier with capacitor filter and then DC to DC converter topology which here is flyback topology used. This converter can be used for step up voltage control can be achieved **using** this by choosing the value of duty cycle. The input 230 V AC supply is converted into 240 V DC voltage with 100 W power output. The open loop and closed loop simulation and designing of flyback converter in CCM are performed in MATLAB which matches well with the calculated value.

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