A big bang-big crunch optimization based approach for interval type-2 fuzzy PID controller design

In this paper, we will present a big bang-big crunch optimization (BB-BC) based approach for the design of an interval type-2 fuzzy PID controller. The implemented global optimization algorithm has a low computational cost and a high convergence speed. As a consequence, the BB-BC method is a very efficient search algorithm when the number of the optimization parameters is relatively big. The optimized type-2 fuzzy controller is compared with PID and type-1 fuzzy PID controllers which were optimized with either the BB-BC optimization method or conventional design strategies. The paper will also show the effect the extra degrees of freedom provided by the antecedent interval type-2 fuzzy sets on the closed loop system performance. We will present a comparative study performed on the highly nonlinear cascaded tank process to show the superiority of the optimized interval type-2 fuzzy PID controller compared to its optimized PID, type-1 counterparts. © 2013 IEEE

Design of an Interval Fuzzy Type-2- PID Controller for a Gas Turbine Power Plant

In this paper, an interval fuzzy type-2 PID controllers are designed for speed and Exhaust temperature in a heavy duty Gas Turbine (HDGT) power plant and the model selected is Rowen’s model to present the mechanical behavior of the gas turbine, the work is aimed to improve the system dynamic performance of speed and Exhaust temperature for a 56.6 MW, 50 HZ, simple cycle, single shaft heavy duty gas turbine, all gains for conventional  PID and interval fuzzy type-2 PID are tuned using Social Spider Optimization(SSO) technique, we show the performance improvement for interval fuzzy type -2 PID controller in comparison with conventional PID via simulation

Design and implementation of an interval Type-2 Fuzzy Logic and type-1 fuzzy logic controls for Magnetic Levitation System

The aim of this paper is the synthesis of an interval type-2 fuzzy logic PID and type-1 fuzzy logic controllers for magnetic levitation system to keep a metal ball suspended in mid-air by changing the field strength of an electromagnet coil, Performances of the suggestion controller are estimated and compared that controller with those of type-1 fuzzy logic PID controller by using the same structure and under similar operating conditions. Simulation results showed that the interval type-2 fuzzy logic PID controller has better performances than those of type-1fuzzy logic PID controller. The parameters of PID controller have been modifying through particle swarm optimization (PSO).The simulation of magnetic levitation system based on its Mathematical model is carried out in MATLAB

A novel dual surface type-2 fuzzy logic controller for a micro robot

Over the last few years there has been an increasing interest in the area of type-2 fuzzy logic sets and systems in academic and industrial circles. Within robotic research the majority of type-2 fuzzy logic investigations has been centred on large autonomous mobile robots, where resource availability (memory and computing power) is not an issue. These large robots usually have a variation of a Unix operating system on board. This allows the implementation of complex fuzzy logic systems to control the motors. Specifically the implementation of interval and geometric type-2 fuzzy logic controllers is of interest as they are shown to outperform type-1 fuzzy logic controllers in uncertain environments. However when it comes to using micro robots it is not practical to use type-1 and type-2 fuzzy logic controllers, due to the lack of memory and the processor time needed to calculate a control output value. The choice of motor controller is usually either fixed pre-set values, a variable scaled value or a PID controller to generate wheel velocities. In this research novel ways of implementing type-1 and interval type-2 fuzzy logic controllers on micro robots with limited resources are investigated. The solution thatis being proposed is the use of pre-calculated 3D surfaces generated by an off-line Fuzzy Logic System covering the expected ranges of the input and output variables. The surfaces are then loaded into the memory of the micro robots and can be accessed by the motor controller. The aim of the research is to test if there is an advantage of using type-2 fuzzy logic controllers implemented as surfaces over type-1 and PID controllers on a micro robot with limited resources. Control surfaces were generated for both type-1 and average interval type-2 fuzzy logic controllers. Each control surface was then accessed using bilinear interpolation to provide the crisp output value that was used to control the motor. Previously when this method has been used a single surface was employed to hold the information. This thesis presents the novel approach of the dual surface type-2 fuzzy logic controller on micro robots. The lower and upper values that are averaged for the classic interval type-2 controller are generated as surfaces and installed on the micro robots. The advantage is that nuances and features of both the lower and upper surfaces are available to be exploited, rather than being lost due to the averaging process. Having conducted the experiments it is concluded that the best approach to controlling micro robots is to use fuzzy logic controllers over the classical PID controllers where ever possible. When fuzzy controllers are used then type-2 fuzzy controllers (dual or single surface) should be used over type-1 fuzzy controllers when applied as surfaces on micro robots. When a type-2 fuzzy controller is used then the novel dual surface type-2 fuzzy logic controller should be used over the classic average surface. The novel dual surface controller offers a dynamic, weighted, adaptive and superior response over all the other fuzzy controllers examined

Embedded Interval Type-2 Neuro-Fuzzy Speed Controller for Marine Diesel Engines

Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the uncertainties encountered in these environments. The current speed controllers for marine diesel engines are based on PID and type-1 Fuzzy Logic Controllers (FLCs) which cannot fully handle the uncertainties encountered in such environments. Type-2 FLCs can handle such uncertainties to produce a better control performance. However, manually designing a type-2 FLC is a difficult task. In this paper, we will introduce an embedded type-2 Neuro-Fuzzy Controller (T2NFC) which learns the parameters of interval type-2 FLC to control marine diesel engines. We have performed numerous experiments on a real diesel engine testing platform in which the T2NFC operated on an industrial embedded controller and handled the uncertainties to produce an accurate and robust speed controller that outperformed the currently used commercial engine controller, even though we have trained the T2NFC with data collected from the commercial controlle

Real-time interval type-2 fuzzy control of an unmanned aerial vehicle with flexible cable-connected payload

This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

Fuzzy logic control for energy saving in autonomous electric vehicles

Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested