Direct yaw-moment control of an in-wheel-motored electric vehicle based on body slip angle fuzzy observer

A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental settin

Attitude and Altitude Control of Trirotor UAV by Using Adaptive Hybrid Controller

The paper presents an adaptive hybrid scheme which is based on fuzzy regulation, pole-placement, and tracking (RST) control algorithm for controlling the attitude and altitude of trirotor UAV. The dynamic and kinematic model of Unmanned Aerial Vehicle (UAV) is unstable and nonlinear in nature with 6 degrees of freedom (DOF); that is why the stabilization of aerial vehicle is a difficult task. To stabilize the nonlinear behavior of our UAV, an adaptive hybrid controller algorithm is used, in which RST controller tuning is performed by adaptive gains of fuzzy logic controller. Simulated results show that fuzzy based RST controller gives better robustness as compared to the classical RST controller

Fuzzy Model for IMC Based PI Controller for a nonlinear pH Process

The control of pH is one of the most difficult challenges in the process industry because of the severe nonlinearities in the behaviour of the system. Different approaches for the pH control are proposed in various literatures. In the present study, control of pH using fuzzy model for internal model controller based PI is proposed. Modelling of the pH process is supposed to be a difficult task because one needs to have knowledge about the components and their nature in the process stream in order to model its dynamics. In this work, fuzzy model is proposed using the first principle equation of the nonlinear pH process and used for controlling the pH process effectively using IMC based PI. DOI: 10.17762/ijritcc2321-8169.15034

Fuzzy System Identification Based Upon a Novel Approach to Nonlinear Optimization

Fuzzy systems are often used to model the behavior of nonlinear dynamical systems in process control industries because the model is linguistic in nature, uses a natural-language rule set, and because they can be included in control laws that meet the design goals. However, because the rigorous study of fuzzy logic is relatively recent, there is a shortage of well-defined and understood mechanisms for the design of a fuzzy system. One of the greatest challenges in fuzzy modeling is to determine a suitable structure, parameters, and rules that minimize an appropriately chosen error between the fuzzy system, a mathematical model, and the target system. Numerous methods for establishing a suitable fuzzy system have been proposed, however, none are able to demonstrate the existence of a structure, parameters, or rule base that will minimize the error between the fuzzy and the target system. The piecewise linear approximator (PLA) is a mathematical construct that can be used to approximate an input-output data set with a series of connected line segments. The number of segments in the PLA is generally selected by the designer to meet a given error criteria. Increasing the number of segments will generally improve the approximation. If the location of the breakpoints between segments is known, it is a straightforward process to select the PLA parameters to minimize the error. However, if the location of the breakpoints is not known, a mechanism is required to determine their locations. While algorithms exist that will determine the location of the breakpoints, they do not minimize the error between data and the model. This work will develop theory that shows that an optimal solution to this nonlinear optimization problem exists and demonstrates how it can be applied to fuzzy modeling. This work also demonstrates that a fuzzy system restricted to a particular class of input membership functions, output membership functions, conjunction operator, and defuzzification technique is equivalent to a piecewise linear approximator (PLA). Furthermore, this work develops a new nonlinear optimization technique that minimizes the error between a PLA and an arbitrary one-dimensional set of input-output data and solves the optimal breakpoint problem. This nonlinear optimization technique minimizes the approximation error of several classes of nonlinear functions leading up to the generalized PLA. While direct application of this technique is computationally intensive, several paths are available for investigation that may ease this limitation. An algorithm is developed based on this optimization theory that is significantly more computationally tractable. Several potential applications of this work are discussed including the ability to model the nonlinear portions of Hammerstein and Wiener systems

A Review on ANFIS based Linearization of Non Linear Sensors

Low cost sensors having high sensitivity, better resolution and linear characteristics are required for industrial applications based on instrumentation and control. Unfortunately, the natural non linear characteristic of sensor itself and also the dynamic nature of the environment, aging effect, inherent sensor’s noise and data loss due to transients or intermittent faults affects the sensor characteristics non linearly. As the transfer characteristic of most sensors is nonlinear in nature, obtaining data from such a nonlinear sensor, by using an optimized device, has always been a design challenge. Linearization of nonlinear sensor characteristic in digital environment, is a vital step in the instrument signal conditioning process. This paper gives a brief review about how to overcome this nonlinear characteristic of the sensor using artificial intelligence such as  Hybrid Neuro Fuzzy Logic (HNFL) based on digital linearization technique using VLSI technology such as Field Programmable Gate Array (FPGA)

Accretion Control in Sponge Iron Production Kiln using Fuzzy Logic

Sponge iron is the product generated by direct reduction of iron ore with aid of either carbon or natural gas under controlled temperatures and pressures within a rotary kiln. However the performance of rotary kilns is usually adversely affected by the formation of accretion. This accumulation of sintered solid particles which form rings along the length of the kiln hinders material flow, lowers productivity and life span of the kiln. To maintain the quality of sponge iron and improve on performance of rotary kilns, there is need to minimize accretion build up in the kiln. However due to the nonlinear nature of the reduction process dynamics, it is difficult to control accretion build up in the kiln. This paper proposes the control of accretion build up through the application of Fuzzy Logic control. The controller receives partial and imprecise data from field instruments and is able to maintain product quality under dynamic process conditions. MATLAB Fuzzy-Toolbox was used to model and simulate the design. A comparison is made between the designed Fuzzy system and the PID controller. Simulation results show that it is possible to reduce accretion build up from 27 % when using PID controller to 14.6 % with the use of Fuzzy control. Keywords: Accretion, Fuzzy Logic, Kiln, Sponge iro

PSO BASED TAKAGI-SUGENO FUZZY PID CONTROLLER DESIGN FOR SPEED CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR

A permanent magnet synchronous motor (PMSM) is one kind of popular motor. They are utilized in industrial applications because their abilities included operation at a constant speed, no need for an excitation current, no rotor losses, and small size. In the following paper, a fuzzy evolutionary algorithm is combined with a proportional-integral-derivative (PID) controller to control the speed of a PMSM. In this structure, to overcome the PMSM challenges, including nonlinear nature, cross-coupling, air gap flux, and cogging torque in operation, a Takagi-Sugeno fuzzy logic-PID (TSFL-PID) controller is designed. Additionally, the particle swarm optimization (PSO) algorithm is developed to optimize the membership functions' parameters and rule bases of the fuzzy logic PID controller. For evaluating the proposed controller's performance, the genetic algorithm (GA), as another evolutionary algorithm, is incorporated into the fuzzy PID controller. The results of the speed control of PMSM are compared. The obtained results demonstrate that although both controllers have excellent performance; however, the PSO based TSFL-PID controller indicates more superiority

CONTROL OF PARTICULATE MATTER (PM) EMISSIONS FROM INDUSTRIAL PLANT USING ANFIS BASED CONTROLLER

In recent times, the negative effect of air pollution such as particulate matter (PM) emitted from industrial plants has compelled researchers in finding efficient control system to control such pollutants in order to keep the environment safe. The aim of this study is to develop a reliable method of controlling the emissions of PM using wet scrubber system as a control device. The process of a wet scrubber is nonlinear in nature. Due to difficulty in selecting optimum scrubbing liquid droplet size in wet scrubbing process, the system becomes complex. Thus, Adaptive Neuro Fuzzy Inference System (ANFIS) based control technique is employed in this paper to handle the nonlinearities. ANFIS control technique has the advantage to integrate fuzzy logic systems and learning ability of neural network, thus able to handle nonlinear systems better. The controller is developed using data of PM emission from cement kiln. The system is simulated using triangular and trapezoidal membership function (MF) with 2 and 3 input MF in each case. The performance of the controller is evaluated based on settling time. The results indicated that the developed controller was able to maintain the PM emission below a set point of 20µg/m3 which is the maximum allowable PM emission limit recommended by world health organization (WHO). The controller with 2 input triangular membership functions indicated a better performance with a settling time of 5.2 seconds

Nonholonomic Mobile Robot Trajectory Tracking using Hybrid Controller

ABSTRACT A control scheme is being presented for the trajectory tracking of a nonholonomic kinematic model of mobile robots. As a kinematic model of mobile robots is nonlinear in nature, therefore, it is controlling is always being a difficult task. Thus, a control hybrid scheme comprises of fuzzy logic and PID (Proportional Integral Derivative) is being proposed, in which adaptive gains of PID controller is being tuned by a fuzzy logic controller. Moreover, the effectiveness of this innovative technique is also proved using the simulations by adding model uncertainties and external disturbances in the system. Besides, the fuzzy logic control system is also being compared by the proposed control system. Resultsattained shows that the fuzzy based PID controller drivesimproved results than fuzzy logic controller