Abstract— This paper presents a simulation study of hybrid control strategy to control the motion of wheeledmobilerobot (WMR) in the restrictedenvironment. The implementation of the robot path planning in restricted environments needs a suitable robust controller to avoid known/unknown disturbances and guarantee zero track errors. A proportional derivative active force control (PDAFC) scheme is incorporated with artificial intelligent techniques, namely, fuzzy logic (FL) to effectively estimate the actual torque applied on the robot wheels. Several kinds of trajectories in the restrictedenvironment with unexpected changes in their trajectory has been used to test the proposed control system. The simulation study was carried out using software interface (MATLAB / SIMULINK). The results demonstrate a better performance and higher capability of PDAFC controller for disturbance rejection in comparison with PD and AFC controllers.
ABSTRACT This research presents an improved mobile inverted pendulum robot called Two-wheeled Self-balancing robot (TWSBR) using a Proportional-Derivative Proportional-Integral (PD-PI) robustcontrol design based on 32-bit microcontroller in a sensed environment (SE). The robot keeps itself balance with two wheels and a PD-PI controller based on the Kalman filter algorithm during the navigation process and is able to stabilize while avoiding acute and dynamic obstacles in the sensed environment. The Proportional (P) control is used to implement turn control for obstacle avoidance in SE with ultrasonic waves. Finally, in a SE, the robot can communicate with any of the Internet of Things (IoT) devices (mobile phone or Personal Computer) which have a Java-based transmission application installed and through Bluetooth technology connectivity for wireless control. The simulation results prove the efficiency of the proposed PD-PI controller in path planning, and balancing challenges of the TWSBR under several environmental disturbances. This shows an improved control system as compared to the existing improved Adaptive Fuzzy Controller.
The modern world that we are living in today, robots play a role more than attending imaginative stories or children's toys. We now see a widespread use of robots, and especially mobile robots, with a wide variety of applications in our environment. Types of intelligent robots come to help humans in difficult and dangerous environments such as space or at the bottom of the ocean. Also, the use of mobile robots is very common in order to serve humans with interesting and various applications and sometimes taking on very important and complex responsibilities such as disaster relief or help patients and people with disabilities or doctor's assistant in surgical operations. But the most basic issue with mobile robots is the performance of these robots in dynamic environments. The autonomous mobile robots must be planned in such a way that they can automatically find a path without collision with obstacles and reach the specified destination from a certain beginning point in order to properly carry out their duties. Therefore, an intelligentmobilerobot should be programmed with knowledge and reasoning to have independent behavior. Our goal of this research is to making intelligent an autonomous mobilerobot in a dynamic environment. The dynamic environment means an environment in which the obstacle position is changing at any moment. Using fuzzy logic is one of the methods to control the robot, in which lots of researches have been done so far. A robot arm system for rehabilitation is designed for patients with neurological and muscular disorders by force and position control using fuzzy logic . Also, adaptive fuzzy controllers are designed for industrial applications . In another research, a decentralized fuzzy controller for robot arms has been developed . Also, a robust fuzzy controller is designed for robot arms . Fuzzy logic in static environments is developed in  for mobilerobot navigation. A sensor fusion technique is proposed to enhance the navigation rules using a modified fuzzy associative memory in .
Problems like pollution, congestion, parking availability, which are caused by conventional vehicles, have made life difficult these days. To overcome the situation, Two Wheeled Inverted Pendulum (TWIP) mobile robots (Figure 1) have been introduced [1-7] to overcome these problems. Due to its much smaller in size compared with conventional four wheeled vehicles, TWIPs can occupy less parking space than other vehicles, hence reducing congestion and solving availability of parking space issue. Also TWIP uses DC motors for operation hence eliminating carbon pollution, hence safer environment. However, they are categorized as under actuated mobile robots which makes it difficult to control. Many researchers in the past two decades have been working in developing
A two wheeled inverted pendulum (TWIP) mobilerobot is a three-degrees-of-freedom under-actuated mechanical system with highly nonlinear dynamics. This makes it a perfect test- bed for various control algorithms ranging from conventional theoretical control algorithms to intelligentcontrol algorithms. In recent years, various control methods have been reported in literature for controlling the TWIP mobilerobot. In the early work by Ha and Yuta , a linear state feedback and feed forward controller was designed and implemented for the posture and velocity control of the TWIP mobilerobot. Grasser et al  proposed a linear state feedback controller based on pole placement technique for the robot. In  Nawawi et al a TWIP mobilerobot was developed and stabilized using pole placement controller. Kim et al  investigated the exact dynamics of the TWIP, and developed a linear quadratic regulator (LQR) controller for balancing the robot. Fiacchini et al  proposed linear and nonlinear controllers for stabilizing a personal pendulum vehicle. In Seo et al  the performance of the two wheeled inverted pendulum on tilted road was investigated, and an LQR controller was proposed for the system. Nasir et al  compare the performance of LQR and PID in stabilizing two-wheeled balancing robot. In Jones and Stol , the performance of the two wheeledmobilerobot in low- traction environment investigated by designing an LQR controller based on linearized model of the robot which includes wheel slip effects. Pathak et al  proposed velocity and position controllers for the TWIP robot via partial feedback linearization. In Li and Luo  an adaptive controller was proposed for the TWIP system which deals with model uncertainties. Huang et al  proposed three fuzzy controllers based on Takagi-Sugeno and Mamdani architectures for the balancing, traveling and steering of the TWIP mobilerobot. Kausar et al , investigated the effect of terrain inclination on the performance and stability region of a two-wheeledmobilerobot. In Muhammad et al  compare the performance of a partial feedback linearization and an LQR controller in balancing and velocity tracking control of the TWIP mobilerobot.
A robustcontrol algorithm for tracking a wheeledmobilerobot navigating in a pre-planned path while passing through the road’s roundabout environment is presented in this article. The proposed control algorithm is derived from both the kinematic and dynamic modelling of a non-holonomic wheeledmobilerobot that is driven by a differential drive system. The road’s roundabout is represented in a grid map and the path of the mobilerobot is determined using a novel approach, the so-called laser simulator technique within the roundabout environment according to the respective road rules. The main control scheme is experimented in both simulation and experimental study using the resolved- acceleration control and active force control strategy to enable the robot to strictly follow the predefined path in the presence of disturbances. A fusion of the resolved-acceleration control–active force control controller with Kalman Filter has been used empirically in real time to control the wheeledmobilerobot in the road’s roundabout setting with the specific purpose of eliminating the noises. Both the simulation and the experimental results show the capability of the proposed controller to track the robot in the predefined path robustly and cancel the effect of the disturbances.
Abstract. The paper describes a new and novel approach to control a nonholonomic wheeledmobilerobot (WMR) robustly with reference to its trajectory tracking capability in the wake of introduced disturbances. The workspace of a mobilerobot is not always ideal but more often than not, filled with disturbances (known or unknown) such as inherent friction, irregular surface terrain, uncertainties, and parametric changes. An intelligent active force control (IAFC) scheme incorporating fuzzy logic has been proposed in the study to counter the disturbances and consequently improve the trajectory tracking characteristic of the system. In the study, IAFC scheme is employed together with a resolved acceleration control (RAC) that has been shown to provide a very robust and accurate performance of the WMR. Fuzzy logic is explicitly used for the estimation of the inertia matrix that is required in the inner feedback control loop of the IAFC scheme. The robustness and effectiveness of the proposed control scheme are investigated considering various forms of loading and operating conditions. The IAFC scheme has also been compared to two other control methods for the purpose of benchmarking.
Self-balancing robot  developed as early as 1986, originated in Japan, the initial idea is to design a machine that can stand automatically. In 1986, Professor K.Y. of Nippon Telekom University conceived an automatic standing robot , but the robot can only move forward on a fixed track. In 1996, Y.S.H. and S.Y. of Tsukuba University designed and implemented a two-wheeledrobot. It uses a three-stage closed-loop control system to control the autonomous cruising of the robot in a plane . In 2002, F.G. developed the JOE . It can walk freely on slopes due to its centralized structure. In 2005, Professor R.H. at Carnegie Mellon Institute of Robotics and his team successfully developed the first ball-wheel robot, and named BALLBOT . In 2008, The Murata girl was introduced in the Japan Robot Exhibition . It maintains the lateral balance by turning the flywheel equipped in the robot body. In 2010, the REZERO was introduced by University of Zurich , which using the LQR control algorithm. This is a representative of the spherical self-balancing robot.
Abstract – Intelligent Space (iSpace) is a space (room, corridor, or street), with distributed sensory and mobile agents, that are capable of providing intelligent services. In this chapter, the iSpace prototyping project at North Carolina State University is introduced. The current infrastructure of the project has the capability to command the mobile agent to automatically move payload from current position to a desired position. The research topic to be focused on is the remote mobilerobot path-tracking problem where the mobile agent is controlled over communication network. The presence of a network time-delay, more often than not, compounds to the challenging nature of the topic. A network-based control technique called Gain Scheduler Middleware is employed in the control loop to alleviate the effect of time delay. A high fidelity simulation model is proposed in order to simulate the iSpace project at NCSU. The simulation results of the mobilerobot path-tracking is used to demonstrate the effectiveness of using Gain Scheduler Middleware to compensate the time delay effect on remote mobilerobot path-tracking control over the Internet. Variations of the GSM method with one-dimensional and two-dimensional gain tables are compared in order to show the effectiveness and limitations of the method.
In this section, we present experiments results and algorithm illustration of the presented techniques .When the system starts, servo motor rotates the ultrasonic sensor with five corners as shown in Figure 8, and at every angle the robot calculate the distance of nearest obstacle, and then compares the distances to know where the obstacles are, after that the robot getting its direction by using compass sensor and looking for the desired path by calculating the angle of steering, Figure 7 shows the flowchart of the process.
In the integer PID controller, the real order for the derivation and integration that we want to control are both unity. But in fractional-order PID controllers, a fractional order was used in the integration and differentiation parts of this controller to improve the conventional PID controller(Buniyamin, 2011). The P is an expansion of traditional PID controller with a new integral order and a new derivative order have fractional values that let the system less sensitive to the change in parameters and better control of dynamic systems (Ameer, 2014; Mouwafak, 2014). The differential equation of the P controller can be represented as follows:
Owing to its virtues of small volume, light weight, simplicity, reliable structure as well as technical maturity, wheeled rovers continue to gain popularity in the space ex- ploration research; and various rovers, in form of mobile robots, have been developed in this century. The Field In- tegrated Design & Operations (FIDO) rover was developed and tested by the JPL for NASA 2003 Mars exploration Rovers(MER) mission, as reported in Tunstel et al. (2002) and Schenker et al. (2003), the latest MER robot is the Cu- riosity rover launched in 2011, see Arvidson (2016). The “Micro 5”, a light-weight rover was designed and constructed with a panted grade assist suspension system aiming for lunar exploration, see Takshi et al. (2003). ESA developed an pro- totype ExoMars rover to search for evidence of life on Mars, see Michaud et al. (2008); In December 2014, ESA member states approved funding for the rover, to be sent on the sec- ond launch in 2018, but insufficient funds had already started to threaten a launch delay until 2020. And more recently, the “Yutu”, a lunar rover as called “Jade Rabbit”, reported in Ip et al. (2014), was developed and sent to the moon as part of the Chang’e-3 mission.
For understanding the Kinematics of car like mobilerobot first we must know the type of wheels we are using in our case and also the constrained applied due to it. In common case disk type wheels are used for a carlike wheeledmobilerobot. In case of simulation, a car like model can be designed using simple geometric and trigonometric concept for more understanding please refer . Many control approaches have been put forward to solving the motion control problem such game theory, model predictive control and so on. We have use Fuzzy logic (type-2) in our case. Seong-Gon Kong in  presented a work in type1 fuzzy logic controller having two input and one output state variable. x coordinate and vehicle orientation where the input to the controller whereas the output is the vehicle steering angle. The rule base used is a very common rule base used for mobilerobot case, nearly similar kind of rule base is given in -.We have designed a new rule base having 25 rules for our case. Many times it is seen that the data that is used to develop the rules for a fuzzy system are uncertain. Due to the uncertainty, like an uncertainty in collection of information for a same case by different people, it becomes tough to decide what the exact value of membership function is. The overall structure of fuzzy sets in a fuzzy system is to allow the handling and modeling of much of uncertainty using type-1 fuzzy sets. But Type-1 fuzzy logic systems are not able to completely handle the linguistic or numerical uncertainties . In such a case we need another kind of fuzzy set for a fuzzy system called as type 2 fuzzy set. A type-2 fuzzy logic system or controller uses notation that are very much similar to that are used in a type-1 fuzzy logic controller such as membership functions, fuzzy rules, t-norms operations, fuzzification, inference, defuzzification. The only difference is that addition of type reduction process before defuzzification. The basic ideas of IT2-FL in Matlab is explained by Dongrui Wu in  whereas Hani hagras  explains the theoretical concept of type2 FLS in simple language. The paper is arranged as follow Section II gives kinematic information of mobilerobot along with kinematic constraints, also telling why we are having these constraint and how our mobilerobot move in its environment, section III is focused on our leader-follower formation problem. Section IV gives the theoretical concept of Type2 fuzzy logic along with the proposed controller. Section V shows the simulation results whereas Section VI tells the conclusion.
As a kind of wheeledmobilerobot used in intelligent logistics system, AGV is mainly used for automatic material transportation, the precise positioning and path tracking is the assu- rance of accurate material transportation. In this article, the laser coordinate positioning technology is used to realize accurate positioning for AGV, a new method of target reference point selection is put forward, and path tracking is implemented in combination with the kinematics model of single steering wheel AGV, the objective function that AGV successfully reaches the destination accurately according to the preset trajectory is completed finally. The study is in trial stage, and obtains good operation effectiveness.
A two-wheeled inverted pendulum (TWIP) mobilerobot is a three-degree-of-freedom under- actuated mechanical system with highly nonlinear dynamics. It is quiet challenging to control such system due to its unstable and under-actuated nature. Numerous works on modeling and control of TWIP mobilerobot have been presented in literature. Kim et al  investigated the exact dynamics of the TWIP mobilerobot, and a Linear Quadratic Regulator (LQR) controller was developed for balancing the robot. Fiacchini et al  proposed linear and nonlinear controllers for stabilizing a personal pendulum vehicle. To compensate for the measurable disturbances, the work in  compared the performance of Model Predictive Controller and LQR. Multipoint pole placement control for velocity tracking of the TWIP is shown in . In Jones and Stol , the performance of the two wheeledmobilerobot in low-traction environment was investigated by designing a LQR controller based on linearized model of the robot which includes wheel slip effects. Pathak et al  proposed velocity and position controllers for the TWIP robot via partial feedback linearization. Dai et al  proposed sliding mode controllers for self-balancing and yaw motion and designed independently. While Kim et al  investigated a nonlinear motion control using the State-Dependent Riccati Equation (SDRE) control framework. Kharola et al  discussed a fuzzy logic control strategy for control and stabilization of TWIP.
The third category is related to the robust and adaptive dynamic design approaches. Their main objective is to overcome the weakness of the previous approaches by taking into account the unavoidable disturbances caused by the navigation environment [12,21]. Most recent pub- lications on the reference trajectory tracking with non- holonomic platforms use adaptive dynamic approaches.  studied the effect of wheel skidding on platform stability when a Lyapunov based kinematic controller is used. The system is shown to be stable under certain conditions. The sliding mode control provides fast re- sponse and good robustness in presence of platform model uncertainties [23,24]. In , an adaptive sliding mode design approach using a self recurrent wavelet neural network is described and validated through simu- lation. In , an adaptive dynamic control scheme using the nonlinear stochastic control is proposed. This scheme is mainly based upon the dual control principle originally proposed by Fel’Dbaum . The advantage of this method seems to be its ability to achieve at the same time the control of the platform and parameter estimations. Although successfully extensive simulations were pro- vided, no experimental validation was shown. Adaptive fuzzy control can be used to approximate nonlinear func- tions. This method has been extensively studied in con- trol theory. Hou  designed an adaptive controller based on the backstepping  and fuzzy logic ap- proaches. It takes into account the uncertainty in the platform kinematic and simulation results suggest good performance. In , the backstepping technique is also used to design a stable dynamic controller for car-like platform and the reference trajectory tracking perform- ance is evaluated in simulation. However, the perform- ance of backstepping control is dependent upon knowl- edge of the exact platform model. Hence, it applications on real mobile platforms may be limited . Venelinov  proposed another adaptive fuzzy approach using a kinematic controller. This method was able to reduce in simulation the effect of unmodeled disturbances. In , a dynamic Petri recurrent fuzzy neural network was pro- posed and its performance for the reference trajectory tracking were compared with other similar methods. The tracking error was demonstrated to convergence to an equilibrium point through different learning rates ob- tained by applying Lyapunov stability theory to the sys- tem. In , a robustcontrol of a nonholonomic mobile
In Chapter Two, literature review will be discussed related to the history and general knowledge on mobilerobot. Some theories and methods applied in this project will be explained. Some case studies including the perspective and techniques used in previous researches are discussed. After that, it will discuss the fuzzy logic technique and the comparison between Mamdani and Sugeno’s method on dealing a system. Finally, it also reviews some characteristic and function of Simulink which will be used later.
The research on trajectory tracking problem of wheeledmobilerobot (WMR) gains a great interest in the recent years due to its promising applications in many fields such as factory automation, transportation, room cleaning , security and space exploration. The main motivations behind these considerable interests are the unexpected growth in the areas of wireless communication, computing machinery and sensors technology. The purpose of the path tracking controller is to force the WMR to achieve a desired path such that the tracking error is stabilized to zero. However, the tracking error is mostly unavoidable since the performance of the WMR can be affected by different types of uncertainties such as sensors and actuators faults, slippage, friction and unmodeled dynamics. Thus, design of robust path tracking controller for WMR is still an open issue in robotics community .