Real-time path planning for Mecanum-wheeled robots with type-2 fuzzy logic controller

A Type-2 Fuzzy logic controller adapted with quantum genetic algorithm, referred to as type-2 quantum fuzzy logic controller (T2QFLC), is presented in this article for robot manipulators with unstructured dynamical uncertainties. Quantum genetic algorithm is employed to tune type-2 fuzzy sets and rule sets simultaneously for effective design of interval type-2 FLCs. Traditional fuzzy logic controllers (FLCs), often termed as type-1 FLCs using type-1 fuzzy sets, have difficulty in modeling and minimizing the effect of uncertainties present in many real time applications. Therefore, manually designed type-2 FLCs have been utilized in many control process due to their ability to model uncertainty and it relies on heuristic knowledge of experienced operators. The type-2 FLC can be considered as a collection of different embedded type-1 FLCs. However, manually designing the rule set and interval type-2 fuzzy set for an interval type-2 FLC to give a good response is a difficult task. The purpose of our study is to make the design process automatic. The type-2 FLCs exhibit better performance for compensating the large amount of uncertainties with severe nonlinearities. Furthermore, the adaptive type-2 FLC is validated through a set of numerical experiments and compared with QGA evolved type-1 FLCs, traditional and neural type-1 FLCs.

Magnetic Levitation System (MLS) is a typical multi-variable, nonlinear and unstable system. The basic aim of our research work is for suspending the steel ball without any mechanical support in desired position with help of an efficient controller. The simulation of MLS based on its mathematical model is carried out in MATLAB and its real time implementation is also achieved. In this paper an interval type-2 single input fuzzy logic controller (IT2SIFLC) is designed for a Magnetic Levitation System. For this task we have proposed an interval type-2 single input fuzzy logic controller (IT2SIFLC) based on theory of type-2 fuzzy logic systems (T2FLS) and single input theory of fuzzy logic control, which has the advantage of the total number of rules are abruptly reduced compared to interval type-2 fuzzy logic controller (IT2FLC). The tracking performance and robustness are also checked for this system. For tracking, two type of reference trajectory are modelled. One is sine wave and other is a set of constant point varying at different levels. Lastly for robust performance, disturbance is applied in MLS. The simulation and experiment results are indicating that the proposed control method has good control ability.

In this study, interval type2 fuzzy logic (IT2FL) and PID controller is designed for swing-up position control of double inverted pendulum (DIP) system. The double inverted pendulum system consists of two rigid bars connected by a revolute joint. Mass of the revolute joint is included in the dynamic model.Rigid bars in the system are assumed to experience planar motion. The pendulum system is connected to the base by means of a revolute joint. Torque provided through a motor mounted to the base is used for position control of the system. PID (Proportional-Derivative-Integral) and interval type2 fuzzy logic controllers are developed by using the same performance criteria for position control of double inverted pendulum system. IT2FL controller is similar with type1 fuzzy logic controller. IT2FL system provides soft decision boundaries, whereas a type-1 fuzzy logic system provides a hard decision boundary. Membership function in interval type2 fuzzy logic set as an area called Footprint of Uncertainty (FOU) which limited by two type1 membership function those are upper membership function (UMF) and lower membership function (LMF).System behaviour is obtained by computer simulation using developed controllers respectively. Computer simulation results are compared in order to evaluate applicability of developed controllers. MATLAB/Simulink software is used in computer simulations.

A Type-2 Fuzzy logic controller adapted with genetic algorithm, called type-2 genetic fuzzy logic controller (T2GFLC), is presented in this paper to handle uncertainty with dynamic optimal learning. Genetic algorithm is employed to simultaneous design of type-2 membership functions and rule sets for type-2 fuzzy logic controllers. Traditional fuzzy logic controllers (FLCs), often termed as type-1 fuzzy logic systems using type-1 fuzzy sets, cannot handle large amount of uncertainties present in many real environments. Therefore, recently type-2 FLC has been proposed. The type-2 FLC can be considered as a collection of different embedded type-1 FLCs. However, the current design process of type-2 FLC is not automatic and relies on human experts. The purpose of our study is to make the design process automatic. The evolved type-2 FLCs can deal with large amount of uncertainties and exhibit better performance for the mobile robot. Furthermore, it has outperformed their type-1 counterparts as well as the traditionally designed type-2 FLCs.

Adaptive fuzzy controller is a fuzzy controller that has ability to change its parameters when the plant’s operating conditions vary. In this paper, design and implementation of model reference adaptive fuzzy control are presented. Interval type-2 fuzzy logic controller with PD-like action is employed and its performance is studied. The fuzzy controller structure is applied to control an inverted pendulum. Simulation and experimental study shows that by using similar membership function, fuzzy rules and scaling, interval type-2 adaptive fuzzy logic controller provides better control system performance compared to type-1 fuzzy controller.

This paper presents several non-singleton general type-2 fuzzy logic controllers (NGT2FLCs) for an under-actuated mobile two-wheeled self-balancing robot to improve the anti-interference capability of the system. Four kinds of fuzzifiers, including singleton fuzzifier, type-1 non-singleton fuzzifier, interval type-2 non-singleton fuzzifier and general type-2 non-singleton fuzzifier, are considered to construct different general type-2 fuzzy logic controllers (GT2FLCs). In order to show the superiority of the GT2FLCs, three kinds of interval type-2 fuzzy logic controllers (IT2FLCs), including singleton IT2FLCs, type-1 non-singleton IT2FLCs (N1IT2FLCs) and interval type-2 non-singleton IT2FLCs (N2IT2FLCs), are also presented. A comparative study between singleton fuzzy controllers and non-singleton fuzzy controllers, and IT2FLCs and GT2FLCs is also shown. All simulation results show that the performance of non-singleton fuzzy logic controllers is better than that of singleton fuzzy logic controllers. The NGT2FLCs get the best performance in the presence of uncertainties and external disturbances.

Uncertainty handling is a major issue for the control of real-world systems. Traditional singleton type-1 Fuzzy Logic Controllers (FLCs) with crisp inputs and precise fuzzy sets cannot fully cope with the high levels of uncertainties present in real world environments (e.g. sensor noise, environmental impacts, etc.). While non-singleton type-1 fuzzy systems can provide an additional degree of freedom through non-singleton fuzzification of the inputs, it is unclear how this capability relates to singleton type-1 and specifically interval type-2 FLCs in terms of control performance (also because the application of non-singleton type-1 FLCs is quite rare in the literature). In recent years interval type-2 FLCs employing type-2 fuzzy sets with a Footprint of Uncertainty (FOU) have become increasingly popular. This FOU provides an additional degree of freedom that can enable type-2 FLCs to handle the uncertainties associated with the inputs and the outputs of the FLCs. One of the main criticisms of singleton type-2 FLCs is that they outperform (the usually singleton-) type-1 FLCs because they - respectively their type-2 fuzzy sets, employ extra parameters, thus making improved performance an obvious result. In order to address this criticism, we have implemented a non-singleton type-1 FLC which allows a more direct comparison between the non-singleton type-1 FLC and singleton interval type-2 FLC as the number of parameters for both controllers is very similar. The paper details the implementation details of the FLCs for the application of a nonlinear servo system and provides the experimental simulation results which were performed to study the effect of increasing levels of uncertainty (in the form of input noise) and the capability of the individual FLCs to cope with them. We conclude by providing our interpretation of the results and highlighting the essential differences in the uncertainty handling between the (non-) singleton type-1 and singleton interval type-2 FLCs.

Most real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, there is a need to develop different approaches that can handle the available uncertainties and reduce their effects on the given application. To date, Type-1 Fuzzy Logic Controllers (FLCs) have been applied with great success to many different real world applications. The traditional type-1 FLC which uses crisp type-1 fuzzy sets cannot handle high levels of uncertainties appropriately. Nevertheless it has been shown that a type-2 FLC using type-2 fuzzy sets can handle such uncertainties better and thus produce a better performance. As such, type-2 FLCs are considered to have the potential to overcome the limitations of type-1 FLCs and produce a new generation of fuzzy controllers with improved performance for many applications which require handling high levels of uncertainty. This paper will briefly introduce the interval type-2 FLC and its benefits. We will also present briefly some of the type-2 FLC real world applications.

This paper presents an implementation of a new robust control strategy based on an interval type-2 fuzzy logic controller (IT2-FLC) applied to the wind energy conversion system (WECS). The wind generator used was a variable speed wind turbine based on a doubly fed induction generator (DFIG). Fuzzy logic concepts have been applied with great success in many applications worldwide. So far, the vast majority of systems have used type-1 fuzzy logic controllers. However, T1-FLC cannot handle the high level of uncertainty in systems (complex and non-linear systems). The amount of uncertainty in a system could be reduced by using type-2 fuzzy logic since it offers better capabilities to handle linguistic uncertainties by modeling vagueness and unreliability of information. A new concept based on an interval type-2 fuzzy logic controller (IT-2 FLC) was developed because of its uncertainty management capabilities. Both these control strategies were designed and their performances compared for the purpose of showing the control most efficient in terms of reference tracking and robustness. We made a comparison between the performance of the type-1 fuzzy logic controller (T1-FLC) and interval type-2 fuzzy logic controller (IT2-FLC). The simulation results clearly manifest the height robustness of the interval type-2 fuzzy logic controller in comparison to the T1-FLC in terms of rise time, settling time, and overshoot value. The simulations were realized by MATLAB/Simulink software.

Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers

In this paper, we propose a novel interval type-2 fuzzy logic controller (IT2FLC) for interval system. The precise mathematical model of the controlled plant is not required to design this IT2FLC. Design procedure of the IT2FLC is explored in detail. A typical linear interval system with 50% parameter variations is adopted to demonstrate the effectiveness of the proposed IT2FLC. For better understanding the performance of IT2FLC, numerous shapes of membership functions and variation of rule numbers of rule table are evaluated for simulation thoroughly. The simulation results are compared with those from type-1 fuzzy logic controller (T1FLC) under same conditions. It shows that the performance of IT2FLC is much better than that of T1FLC if the number of rule is reduced.

The standard fuzzy logic controllers, also known as type-1 fuzzy logic controllers, have often been criticized for their inability to handle uncertainties in the control processes. Therefore, a lot of attention is being focused on type-2 fuzzy logic controllers, especially, the interval type-2 fuzzy logic controllers. This paper aims at developing both type-1 and type-2 fuzzy logic controllers for an airplane altitude control problem and comparing their performances. Both the controllers have similar knowledge bases, and both of them are tested in two simulation setups, an ideally modeled setup and a setup with uncertainties. The results show that the type-2 fuzzy logic controller outperforms the type-1 fuzzy logic controller, especially, in the environment with uncertainties. Therefore, this research seems to validate the superiority of type-2 fuzzy logic control in making the controllers independent of uncertainties in intricate model details.

Autonomous vehicles are aimed to reduce accidents and traffic congestion. Since hybrid electric vehicles offer feasible solutions to reduce energy consumption and emission to the environment, it is expected that autonomous vehicles will be powered through a hybrid electric system compared to other alternatives. In this paper, a hybrid electric autonomous vehicle is studied under significant amount of uncertainty and ambiguity in the road environment and driver behavior. A Type 1 fuzzy logic controller is constructed here to address the uncertainties of driving conditions. The design involves building an intelligent energy management system for the hybrid electric autonomous vehicle. We have also examined the potentials of the Interval Type 2 fuzzy logic control, especially for energy consumption management. Two simulations are implemented, to demonstrate that the intelligent system, proposed trough Type 1 and Interval Type 2 fuzzy logic control, decreases the fuel usage of the vehicle from 6.74 to 6.58 L/100km, respectively. It is also demonstrated that the Interval Type 2 fuzzy logic controller saves more battery life compared to the Type-1 when the vehicle works under uncertain and ambiguous road conditions. Finally, Interval Type-2 fuzzy logic controller facilitates a reduction of carbon footprint in the autonomous vehicle as desired by the automotive industry stakeholders.

In this study, Fuzzy Logic (FL) and Interval Type-2 FL (IT-2FL) controllers were applied to a mobile robot in order to determine which method facilitates navigation and enables the robot to overcome real-world uncertainties and track an optimal trajectory in a very short time. The robot under consideration is a non-holonomic unicycle mobile robot, represented by a kinematic model, evolving in two different environments. The first environment is barrier-free, and moving the robot from an initial to a target position requires the introduction of a single action module. Subsequently, the same problem was approached in an environment closer to reality, with objects hindering the robot's movement. This case requires another controller, called obstacle avoidance. This system allows the robot to reach autonomously a well-defined target by avoiding collision with obstacles. The robustness of the structures of the defined controllers is tested in Matlab simulations of the studied controllers. The results show that the IT-2FL controller performs better than the FL controller.

Underactuated mechanical systems have their own difficulties within the control criterion. As a particular and complex underactuated mechanical system, underactuated truss-like robotic finger(UTRF) is studied by establishing its dynamic model. The control problems include high nonlinearity, model inaccuracy and uncertainties. Type-2 fuzzy logic control method is supposed to be a proper way to solve these problems, because fuzzy logic control itself does not depend on an accurate model of the controlled object, and type-2 fuzzy logic control is able to handle uncertainties. The basic principle of type-2 fuzzy logic control is then analyzed. Moreover, an interval type-2 fuzzy logic controller is designed for UTRF to accomplish the goal of stabilization in its equilibrium point. Simulation result shows that the designed interval type-2 fuzzy logic controller is correct and effective.