Abstract
We present a fractional order PI controller (FOPI) with SLAM method, and the proposed method is used in the simulation of navigation of NAO humanoid robot from Aldebaran. We can discretize the transfer function by the Al-Alaoui generating function and then get the FOPI controller by Power Series Expansion (PSE). FOPI can be used as a correction part to reduce the accumulated error of SLAM. In the FOPI controller, the parameters (Kp,Ki, and α) need to be tuned to obtain the best performance. Finally, we compare the results of position without controller and with PI controller, FOPI controller. The simulations show that the FOPI controller can reduce the error between the real position and estimated position. The proposed method is efficient and reliable for NAO navigation.
Highlights
More than 95% of the control loops are of PI/PID type in process control, but only a few of control loops work well [1]
We present a fractional order PI controller (FOPI) with simultaneous localization and mapping (SLAM) method, and the proposed method is used in the simulation of navigation of NAO humanoid robot from Aldebaran
Fractional order controllers have been used in industrial applications [8] and various fields such as power electronics [9], system identification [10], robotic manipulators, irrigation canal control [11], mechatronics systems [12], and heat diffusion systems [13]
Summary
More than 95% of the control loops are of PI/PID type in process control, but only a few of control loops work well [1]. There are some problems in PI/PID control, such as bad tuning, incorrect implementation techniques, some restrictions and limitations, and actuator and sensor problems. Using the notion of fractional order, it may be a step closer to the real world life, because many real physical systems are well characterized by fractional order differential equations [2]. Fractional order controllers can be expressed by fractional order differential equations. We will study fractional order PI (FOPI) controller combined with SLAM in the navigation of humanoid robot because of the limitation of PI controller. The rest of this paper is organized as follows: the FOPI controller used in the NAO robot navigation control is presented, the background knowledge of SLAM and the NAO robot are described, and the simulation results and conclusion are given in Sections 4 and 5, respectively The rest of this paper is organized as follows: the FOPI controller used in the NAO robot navigation control is presented in Section 2, the background knowledge of SLAM and the NAO robot are described in Section 3, and the simulation results and conclusion are given in Sections 4 and 5, respectively
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