Chanchal Dey

Bio: Chanchal Dey is an academic researcher from Jadavpur University. The author has contributed to research in topics: Adaptive control & Fuzzy control system. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

A Gain Adaptive Fuzzy Logic Controller

Abstract: We propose a PI-type gain adaptive fuzzy logic controller (GAFLC) Input scaling factors (SFs) of the GA-FLC for error (e) and change of error (?e) are updated on-line, by a single non-linear parameter computed through a unipolar sigmoid function defined on the current process states (e and ?e) The performance of the proposed GA-FLC for step set-point change and load disturbance is compared with those of a conventional PI-type FLC (FPIC) and a Ziegler-Nichols tuned (ZN-tuned) non-fuzzy PI-controller (NFPIC) Results for various higher order processes show that GA-FLC provides much improved performance over both FPIC and ZN-tuned NFPIC

Design and real-time implementation of a fuzzy PI controller on a servo speed control application

Abstract: Servo control systems are very popular in process automation. Towards achieving and maintaining the desired speed in close-loop application, fuzzy controllers are more competent in comparison with their conventional counterparts. Based on the prior knowledge, a rule-base is designed here comprising of forty-nine if-then rules using simple triangular membership functions. The non-linear behaviour of such servo systems can also be efficiently managed by proper designing of rule-base with appropriate choice of scaling factors. Superiority of the designed fuzzy PI controller is verified through simulation study on a servo speed control system and in addition it is successfully implemented in real-time on a hardware setup.

Self-tuned approximated simplest fuzzy logic controller based shunt active power filter

Abstract: This paper proposes a self-tuned approximated simplest fuzzy logic controller (ST-ASFLC) for shunt active power filter (APF). The approximated simplest fuzzy logic controller (ASFLC) is a 4-rule fuzzy logic controller (FLC), which successfully approximates the control actions of a 49-rule FLC using some compensating factors. The input scaling factors (SFs) of ASFLC are tuned online by a single nonlinear parameter using bipolar sigmoid activation function. The online tuning of SFs of ASFLC results in better dynamic response of shunt APF. The scheme is tested under randomly varying nonlinear loads. The simulation results presented under transient and steady-state operating conditions demonstrate that dynamic performance of proposed ST-ASFLC is better than 49-rule FLC, ASFLC with fixed SFs, and ST-ASFLC using the log sigmoid (unipolar) activation function. The proposed ST-ASFLC shows a remarkable improved performance in terms of peak overshoot, settling time, harmonic mitigation and reactive power compensation over other FLCs with fixed as well as self-tuned SFs using log sigmoid activation function.

An adaptive PD type FLC with its real-time implementation on a servo position control system

Abstract: A non-fuzzy adaptation scheme is proposed for a PD type fuzzy logic controller (FLC) by continuously modifying its input scaling factors (G, GΔe). Both Ge and GΔe are updated online by a nonlinear parameter computed through a mono-polar sigmoid function defined on the process error (e) and change of error (Δe). Performance of the proposed adaptive fuzzy PD controller (AFPD) is compared with conventional as well as self-tuning fuzzy PD controller (FPD and STFPD). Simulation study and hard-ware based experimental results clearly establish the superiority of the proposed AFPD over FPD and STFPD during set point tracking and load rejection. Unlike STFPD which uses 49 control rules and additional 49 expert's defined gain rules, our proposed AFPD uses only 25 control rules along with an online non-fuzzy adaptation mechanism of the input scaling factors Ge and GΔe. In spite of almost 75% reduced rules compared to the well studied STFPD both the transient and steady state responses justify the effectiveness of the proposed AFPD.

Performance evaluation of a fuzzy logic controller on a servo speed control system

Abstract: The study is a metonym for designing a Fuzzy Logic Controller (FLC) using various types of membership functions (MFs) and variable rule-base (RB) for a DC servo speed control application. FLCs are more useful in comparison to their crisp counterparts for attaining a desired speed in a closed loop process. Based on prior experience, the control methodology i.e. RB of a FLC is designed which comprises of 49 rules in if-then form. Initially the FLC was designed using Triangular type MF. The RB and scaling factors (SFs) were accordingly chosen to deal with system non-linearity. Next, the system is tested with other types of MFs i.e. Trapezoidal, Gaussian and Gaussian-Bell shaped. Moreover, the system is tested for all the aforesaid types of MF initially with 50% reduction in its RB and then further with 80% reduction in the RB i.e. now the system is designed with 25 rules and 9 rules in its RB respectively. This work presents the simulation results of various MFs implemented in a FLC and comparison of their results in terms of the following performance indices (PIs): Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE), Integral Time Square Error (ITSE) And Rise Time (tr).

Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

Abstract: This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.