Search Results - (( java implementation path algorithm ) OR ( parameter activation control algorithm ))

Refine Results
  1. 1

    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

    Published 2017
    “…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
    Get full text
    Get full text
    Thesis
  2. 2

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
    Get full text
    Get full text
    Thesis
  3. 3

    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment by Shaliza Hayati A. Wahab, Nordin Saad, Azali Saudi, Ali Chekima

    Published 2021
    “…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  4. 4

    Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

    Published 2009
    “…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
    Get full text
    Get full text
    Thesis
  5. 5

    The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robotics flexible manipulator by Moloody, Abbas, As’arry, Azizan, Hong, Tang Sai, Raja Kamil, ., Zolfagharian, Ali

    Published 2025
    “…Here, in this research by comprising three of the most effective variational techniques now, a Modified Differential Evolutionary Optimization Algorithm (MDEOA) method is suggested to handle the challenge of adjusting the PID controller parameters for the Intelligent Active Vibration Control (IAVC) of a Combined Single Link Robotics Flexible Manipulator (CSLRFM) in order to reduce the undesired effects of vibration. …”
    Get full text
    Get full text
    Get full text
    Article
  6. 6
  7. 7

    Optimization of robust and LQR control parameters for half car model using genetic algorithm by Kaleemullah, Mohammed, Faris, Waleed Fekry, Hasbullah, Faried, Ghazaly, Nouby M.

    Published 2019
    “…The weights of Robust H-infinity and LQR controller are obtained using Genetic Algorithm on a half car model with two different types of usually existing road disturbance.The design parameters of both the active controller varies with various road profiles. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  8. 8

    A comparative evaluation of PID-based optimisation controller algorithms for DC motor by Ahamed S.R., Parumasivam P., Hossain Lipu M.S., Hannan M.A., Ker P.J.

    Published 2023
    “…Controllers; DC motors; Electric control equipment; Particle swarm optimization (PSO); Proportional control systems; Three term control systems; Two term control systems; Backtracking search algorithms; Comparative analysis; Comparative evaluations; Controller algorithm; Industrial activities; Optimum parameters; Particle swarm optimisation; Proportional integral derivative controllers; Electric machine control…”
    Article
  9. 9
  10. 10

    Active force control with iterative learning control algorithm for a vehicle suspension by Rosmazi, Rosli

    Published 2013
    “…The new control scheme named active force control with iterative learning control algorithm (AFCIL) is complemented by the classic proportionalintegral-derivative (PID) control incorporated and designed as the outermost control loop. …”
    Get full text
    Get full text
    Thesis
  11. 11

    A hybrid intelligent active force controller for robot arms using evolutionary neural networks by Hussein, S.B, Jamaluddin, H, Mailah, M, Zalzala, A.M.S

    Published 2000
    “…In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. …”
    Get full text
    Get full text
    Article
  12. 12

    Hybrid intelligent active force controller for robot arms using evolutionary neural networks by Hussein, S. B., Jamaluddin, H., Mailah, M., Zalzala, A. M. S.

    Published 2000
    “…In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. …”
    Get full text
    Get full text
    Article
  13. 13

    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar by Md. Rasel, Sarkar

    Published 2019
    “…In addition, ACO algorithm has been used for optimization of PID controller parameters to obtain within rated smooth output power of WT from fluctuating wind speed. …”
    Get full text
    Get full text
    Get full text
    Thesis
  14. 14

    A simulation study of a new rate-and-queue-based active queue management algorithm by Ali Asghar, Jomah Adham, Razman, Mat Tahar

    Published 2012
    “…Thus, the main feature of the design is to use coefficients of both proportional rate control and proportional-integral queue length control, and to simplify parameter setting, the control parameters were scaled by the link capacity C to normalize the rate and by the bandwidth-delay product BDP to normalize the queue length, respectively.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  15. 15

    Evaluation of reference signal estimation techniques for the control of shunt active power filter by Atan N.B., Yunus B.B.

    Published 2023
    “…Different methods are used to control the active power filters. The reference current to be detected from the load current and processed by the active power filter controller is obtained from two different control algorithms, namely the Instantaneous Reactive Power Theory (PQ Theory) and Synchronous Reference Frame Theory (SRF Theory). …”
    Conference paper
  16. 16

    Significant insights into the operation of DC-link voltage control of a shunt active power filter using different control algorithms: a comparative study by Abdul Rahman, Nor Farahaida, Mohd Radzi, Mohd Amran, Che Soh, Azura, Mariun, Norman, Abd Rahim, Nasrudin

    Published 2017
    “…A comparative study between a conventional DC-link voltage control algorithm (CDVCA) and a self-charging DC-link voltage control algorithm (SDVCA) is presented. …”
    Get full text
    Get full text
    Get full text
    Article
  17. 17

    Intelligent adaptive active force control of a robotic arm with embedded iterative learning algorithms by Mailah, Musa, Ong, Miaw Yong

    Published 2001
    “…The paper highlights a novel and robust method to control a robotic arm using an iterative learning technique embedded in an active force control strategy. …”
    Get full text
    Get full text
    Get full text
    Article
  18. 18
  19. 19

    Evaluation of different control policies of semi-active MR fluid damper of a quarter-car model by Rahman, Mahmudur, Rashid, Muhammad Mahbubur, Abdul Muthalif, Asan Gani, Kasemi, Banna

    Published 2012
    “…The quarter-car model is executed using step input with two most common and effective control algorithm in vehicle suspension control which are linear quadratic regulator control, and Proportional Integral Derivative control algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  20. 20