Adaptive Tracking Controller Based on the PID for Mobile Robot Path Tracking

Incremental Q -learning strategy for adaptive PID control of mobile robots

“…The ideas of auto-tuning methods were the first steps towards adaptive control ( Hägglund & Johan Åström, 1991;Jun, Jun, & Safonov, 1999 ). In short, classic control theory has contributed with several tuning methods to obtain the gains of the PID controllers for specific operation conditions, however, there is always a requirement of further tuning and it is here where adaptive techniques begin to arise ( Chang & Jin, 2013 ). One of the main disadvantages of the aforementioned methods is the necessity of prior knowledge about the system dynamics.…”

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

“…Another important issue in this field is the path-following problem, which concerns the ability to drive a mobile robot as autonomously as possible on a predefined reference path [21]. The path-following problem can be solved by different control methods, such as a PID controller [22,23], model predictive control [24][25][26], and the fuzzy logic approach [27]. In multi-robot systems, these processes become even more complex.…”

Optimal fuzzy tracking control with obstacles avoidance for a mobile robot based on Takagi-Sugeno fuzzy model

“…Its aim is making the robot follow a desired trajectory using a controller that generates tracking velocities. Many interesting studies about this issue have been published by different scholars since the early 1990s, such as the stable time-varying tracking controllers based on Lyapunov control theories (Kanayama et al, 1990; Kim and Oh, 1998; Kolmanovsky and McClamroch, 1995), the terminal sliding-mode technique for fast convergence toward the reference trajectory (Li and Tian, 2000), the back-stepping method for a global tracking control (Wu et al, 2001), the pole-assignment approach to design a tracking controller for a linear kinematic error model (Sun, 2005), the predictive control based on a linearized error dynamics model obtained around the reference trajectory (Klančar and Škrjanc, 2007), the adaptive output-feedback tracking control for mobile robots with uncertainties (Park et al, 2011), the fuzzy adaptive tracking control, where a fuzzy observer is employed to compensate for both kinematic and dynamic disturbances (Chwa, 2012), the adaptive tracking control based on the PID with fixed gain, where the control law is constructed on the basis of Lyapunov stability theory (Chang and Jin, 2013), the design of a reinforcement learning-based integrated kinematic and dynamic tracking control algorithm (Nguyen et al, 2014), the design of an improved second order sliding mode twisting algorithm for finite-time trajectory tracking (Yang et al, 2015), the robust adaptive tracking control, where the robustness of the controller has been enhanced against the system uncertainties using a disturbance observer and an adaptive compensator (Xin et al, 2016), the optimal universal dynamic tracking control (Miah et al, 2017), the adaptive fast nonsingular terminal sliding mode tracking control (Zhai and Song, 2018), and the improved linear quadratic tracker to track a given and a planned trajectory (Akka and Khaber, 2018). Despite the fact that all the previously mentioned works have shown good results, none of them has included the idea of tracking control in the presence of obstacles on the reference trajectory.…”