Development of a microcontroller-based adaptive fuzzy controller for a two-wheeled self-balancing robot
The Anh Mai1 bull; D. N. Anisimov1 bull; Thai Son Dang2 bull; Van Nam Dinh2
- Department of Control and Informatics, Moscow Power Engineering Institute, Moscow, Russia
- Institute of Technique and Technology, Vinh University,Vinh, Vietnam
Abstract
In this paper, an intelligent system use an adaptation fuzzy controller using Mamdani algorithm modified by relation models for a two wheeled self-balancing robot is developed. Hardware model of the robot and sensor signal processing are described. The signals from sensors are filtered by a discrete complementary filter. A mathematical model of the robot is derived based on Newtonian mechanics. The proposed control system comprises two loops for regulation of the pitch angle and tracking the desired position of the robot. The inner loop uses a PD controller for position tracking. The outer loop is designed with an adaptive fuzzy controller to regulate balancing of the robot. The proposed controllers are tested in simulations using the mathematical model. These controllers are also designed and implemented in the real time system using a STM32F4 DISCOVERY kit which is equipped with a 32-bit ARM7 microprocessor. Simulations and experimental results show advantages of the adaptation fuzzy controller. Using the adaptive fuzzy controller for stability of the robot will allow a more effective and robust control to be implemented.
1 Introduction
Nowadays, robotic is one of the advanced fields of technology that provides many services to mankind (Mohareri et al. 2012). Robotic applications and their wide range of functionalities have create the attentions of many engineers. Two-wheeled self-balancing robots are a typical example of robot, which is inspired from the inverted pendulum system (Anisimov et al. 2017). Advantages as light weight, small footprint, rapid rotation, high maneuverability make it to really efficient for use in different areas (Campion and Chung 2008). Self-balancing robot is also an interesting example of an unstable and nonlinear system (Pathak et al. 2005) that has become a standard topic of research and exploration for young engineers and robotic enthusiasts (Yong et al. 2011). Engineers and researchers offer opportunity to develop control systems that are capable of maintaining stability of an otherwise unstable system. The applications of two-wheeled self- balancing robot vary with difference environment and requirements, but it provides a tool for comparing the success of various types of control systems for the typical stability control problem. And engineers and researchers are now able to test control algorithms on the inherent nonlinear behaviour of such systems.
Previous studies deploying basic conventional control techniques for a two-wheeled self-balancing robot have been widely used in Xu et al. (2011), Ren et al. (2008), Goher and Fadlallah (2017), Sun and Gan (2010), Lin et al. (2009), Yau et al. (2009), Wu et al. (2011), Thao et al. (2010), Filipescu et al. (2011) and Wang et al. (2005). However, implementation of these techniques requires the complete mathematical models of system to describe the dynamics of the system under study which is a tedious job to build. So in some cases, intelligent control techniques are considered such as more effect solution for the complex systems. The development of soft computing techniques brings an important improvement to design controllers without building the mathematical model of the system.Amongst these techniques, fuzzy logic is a promising solution because fuzzy logic controller uses simple mathematical calculations to simulate the knowledge of human experts. Fuzzy controller uses a control algorithm based on linguistic control strategy, it includes empirical rules that is especially useful in operator plants (Pedrycz and Gomide 1998). In fact, objects and industrial processes are highly nonlinear and have un-modeled plant dynamics and uncertainties, so expert knowledge is becoming more and more important in control systems design. So fuzzy controllers were used in many consumer products, such as washing machines (Lucas et al. 2006), video cameras (Liu et al. 1995), and rice cookers (Dote and Ovaska 2001), as well as industrial processes, such as cement kilns (Mamdani 1977; Fallahpour et al. 2007), underground trains and robots (Wang 1999). Design and implementation of a fuzzy controller using Mamdani algorithm based on relation models for a two-wheeled self-balancing robot had been implemented in Anisimov et al. (2017), but number of terms of the inputs and output variables were large, so size of relation matrix is large(25times;7): And implementation in real-time systems based on a microcontroller requires controllers must to be optimized. Hence, in this paper, a fuzzy controller for a two-wheeled self-balancing robot is designed with selecting and optimizing parameters of relation matrix to decrease number of terms of inputs and output variables, also size of relation matrix of fuzzy controller.
Adaptive control has been the subject of active research for many decades (Jager 1995). It is techniques which provide a approach for automatic adjustment of controllers in real time, in order to achieve or to maintain a desired level of control system performance when the parameters of the plant dynamic model are unknown or change in time. Adaptive control techniques can provide an automatic tuning procedure in closed loop for the controller parameters. There have been many theoretical successes, including the development of rigorous proofs of stability and an understanding of the dynamical properties of adaptive schemes. Adaptive controller has been used successfully in numerous control systems, many itrsquo;s applications have been als
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