The generation of dual wavelength pulse fiber laser using fiber bragg grating

A stable simple generation of dual wavelength pulse fiber laser on experimental method is proposed and demonstrated by using Figure eight circuit diagram. The generation of dual wavelength pulse fiber laser was proposed using fiber Bragg gratings (FBGs) with two different central wavelengths which are 1550 nm and 1560 nm. At 600 mA (27.78 dBm) of laser diode, the stability of dual wavelength pulse fiber laser appears on 1550 nm and 1560 nm with the respective peak powers of -54.03 dBm and -58.00 dBm. The wavelength spacing of the spectrum is about 10 nm while the signal noise to ratio (SNR) for both peaks are about 8.23 dBm and 9.67 dBm. In addition, the repetition rate is 2.878 MHz with corresponding pulse spacing of about 0.5 μs, is recorded

Modelling and control of inverted pendulum on the rotating disc

This research work studies about modelling and control of inverted pendulum on the rotating disc by using classical, modern, and intelligent control techniques. In this study, the classical control techniques use proportional-plus-derivative (PD) and proportionalplus- integral (PI) controllers, the modern control techniques that use Linear Quadratic Regulator controller (LQR), and intelligent control technique that use Fuzzy Logic (FL) controller. The main goal of this study is to model and control the dynamic modelling of the inverted pendulum on the rotating by using the above-mentioned control techniques. Among the problems identified for this project are balancing inverted pendulum on the rotating disc with the presence disturbance and establishing stability for a dynamical inverted pendulum on the rotating disc. The practical results in controlling the inverted pendulum and eliminating the disturbance are obtained via the following techniques: the MATLAB root locus for the PD and PI controllers; the optimal control for LQR controller; and the fuzzification and the defuzzification for the FL controller as a perspicuous view of its transient response stability. In the transient response, the balancing and stability of the inverted pendulum on the rotating disc are affected by the presence of the disturbances. The presence of the disturbance that is controlled by LQR controller shows the condition to the inverted pendulum on the rotating disc. Moreover, from the results obtained, it is found to be asymptotically stable by Lyapunov’s stability analysis. The mathematical model of the inverted pendulum on the rotating disc has been developed and the output response with disturbances show LQR controller have achieved good performance compared to PD, PI and FL controllers. This study applicable for the robot cycling transportation that delivers goods for customers

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization

Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization

In this study, a controller design method based on the LQR method and BAT algorithm is presented for the Furuta pendulum stabilization system. Determine the LQR controller, it is often based on the designer's experience or using trial and error to find the Q, R matrices. The BAT search algorithm is based on the characteristics of the bat population in the wild. However, there are advantages to finding multivariate objective functions. The BAT algorithm has an improvement for the LQR controller to optimize the linear square function with fast response time, low energy consumption, overshoot, and a small number of oscillations. Swarm optimization algorithms have advantages in finding global extrema of multivariate functions. Therefore, with a large number of elements of the Q and R matrices, they can also be quickly found and these matrices still satisfy the Riccati equation. The controller with optimal parameters is verified through simulation results with different scenarios. The performance of the proposed controller is compared with a conventional LQR controller and implemented on a real system

Development of deep reinforcement learning for inverted pendulum

This paper presents a modification of the deep Q-network (DQN) in deep reinforcement learning to control the angle of the inverted pendulum (IP). The original DQN method often uses two actions related to two force states like constant negative and positive force values which apply to the cart of IP to maintain the angle between the pendulum and the Y-axis. Due to the changing of too much value of force, the IP may make some oscillation which makes the performance system could be declined. Thus, a modified DQN algorithm is developed based on neural network structure to make a range of force selections for IP to improve the performance of IP. To prove our algorithm, the OpenAI/Gym and Keras libraries are used to develop DQN. All results showed that our proposed controller has higher performance than the original DQN and could be applied to a nonlinear system