Ferryawan Harris Kristanto’s scientific contributions

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Publications (1)


Figure 2 Lines Segment In order to comprehend the measurement model, we use an analogy if the sensor location point is the initial measured distance/range sensor observed. Therefore, logically when the sensor's beam emits towards its bearing and does not detect anything, then the vector formed is just a line vector with a magnitude of max range in the direction according to the bearings.
Table 2 Parameter Constraints
Figure 7 Set Point and Process Value
Figure 9 Bat Algorithm Optimization The main steps of the BA consist of a single loop with some probabilistic switching during the iteration. As an overview of the proposed method, the optimization problem is finding PID parameters: í µí°¾í µí±, í µí°¾í µí±–, í µí°¾í µí±‘ minimizing Root Mean Square Error (RMSE) as an objective function (fitness function). Based on Table 1 and Figure 9, we see the flow process. The bat algorithm executes the computation based on the upper-lower bound initial parameter set. Refers to the function objective; a solution is initialized. While the loop is started, the bats will update frequency and loudness, and later the selection criteria are raised. When the stopping criteria are reached, the final optimum values are generated according to its function objective. The new PID gain value is obtained and later evaluated through mobile robot behaviour.
BAT Algorithm Parameter
Performance of a Wall-Following Robot Controlled by a PID-BA using Bat Algorithm Approach
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December 2022

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91 Reads

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8 Citations

International Journal of Engineering Continuity

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Ferryawan Harris Kristanto

A wall-following robot needs a controller that applies the closed-loop concept to move actively without hindrance. Some controllers with good capabilities can act as controllers for wall follower robots, such as PID controllers. Conceptually, this controller's good performance depends on tuning the three gains before use. Instead of giving the expected and appropriate output, wrong settings will provide inaccuracies for the controller, so applying the manual method at the tuning stage is not recommended. For this reason, PID controllers are often implemented in a system supported by appropriate optimization methods, such as Genetic Algorithm or Particle Swarm Optimization. Furthermore, different from this, in this study, the Bath Algorithm is used as an alternative optimization algorithm. Its application begins with a realistic simulation of a wall-following robot. This is done to provide the possibility to implement online PID controllers and BAs. In the end, several methods are compared to find out the performance of this type of approach. Moreover, based on the observed comparative results, the proposed method gives a better value in accumulative error and convergence speed in the PID optimization process.

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Citations (1)


... This approach involves the controller accumulating error as the robot moves. When the robot approaches the wall [12] and the error approaches zero, the accumulated negative error starts to exert a significant effect in the opposite direction, thereby aligning the robot along the wall. ...

Reference:

Autonomous Navigation of a Mobile Robot Using Overhead Camera and Computer Vision Methods for Time-Critical Tasks
Performance of a Wall-Following Robot Controlled by a PID-BA using Bat Algorithm Approach

International Journal of Engineering Continuity