CMOS design of a current-mode multiplier/divider circuit with applications to fuzzy controllers

Multiplier and divider circuits are usually required in the fields of analog signal processing and parallel-computing neural or fuzzy systems. In particular, this paper focuses on the hardware implementation of fuzzy controllers, where the divider circuit is usually the bottleneck. Multiplier/divider circuits can be implemented with a combination of A/D-D/A converters. An efficient design based on current-mode data converters is presented herein. Continuous-time algorithmic converters are chosen to reduce the control circuitry and to obtain a modular design based on a cascade of bit cells. Several circuit structures to implement these cells are presented and discussed. The one that is selected enables a better trade-off speed/power than others previously reported in the literature while maintaining a low area occupation. The resulting multiplier/divider circuit offers a low voltage operation, provides the division result in both analog and digital formats, and it is suitable for applications of low or middle resolution (up to 9 bits) like applications to fuzzy controllers. The analysis is illustrated with Hspice simulations and experimental results from a CMOS multiplier/divider prototype with 5-bit resolution. Experimental results from a CMOS current-mode fuzzy controller chip that contains the proposed design are also included

Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment

Integrated circuit implementation of fuzzy controllers

This paper presents mixed-signal current-mode CMOS circuits to implement programmable fuzzy controllers that perform the singleton or zero-order Sugeno’s method. Design equations to characterize these circuits are provided to explain the precision and speed that they offer. This analysis is illustrated with the experimental results of prototypes integrated in standard CMOS technologies. These tests show that an equivalent precision of 6 bits is achieved. The connection of these blocks according to a proposed architecture allows fuzzy chips with low silicon area whose inference speed is in the range of 2 Mega FLIPS (fuzzy logic inferences per second

Fuzzy controller optimization using a genetic algorithm for non-collocated semi-active MR based control of a three-DOF framed struture

This paper aims to explore the usefulness of a simple genetic algorithm (GA) optimized Fuzzy Logic Controller (FLC) to reduce the response of a three-DOF framed structure equipped with a MagnetoRheological (MR) damper. These actuators can be controlled in bi-state control mode and/or in a semi-active configuration by continuously adjusting the amount of damping according to the actual response. Generally, model based controllers are designed to determine the actuator output. In recent years, soft computing techniques have been implemented to deal with the highly non-linear nature of structural systems. Among others, fuzzy based controllers seem to be adequate approach for these cases due to the inherent ability to deal with uncertain systems. However, a FLC design requires a wide experience in operating the system. This can be very difficult to implement in complex systems and several optimization techniques have been suggested to enhance the design process of fuzzy controllers. In this paper, a genetic algorithm (GA) optimized semi-active fuzzy based controller is proposed to reduce the seismic response of a three degree-offreedom (DOF) structure using a MR damper at the first DOF. The uncontrolled and controlled structural responses are compared to evaluate the effectiveness of the semi-active fuzzy based controller.info:eu-repo/semantics/publishedVersio

Robust Stabilisation of T-S Fuzzy Stochastic Descriptor Systems via Integral Sliding Modes

This paper addresses the robust stabilisation problem for T-S fuzzy stochastic descriptor systems using an integral sliding mode control paradigm. A classical integral sliding mode control scheme and a non-parallel distributed compensation (Non-PDC) integral sliding mode control scheme are presented. It is shown that two restrictive assumptions previously adopted developing sliding mode controllers for T-S fuzzy stochastic systems are not required with the proposed framework. A unified framework for sliding mode control of T-S fuzzy systems is formulated. The proposed Non-PDC integral sliding mode control scheme encompasses existing schemes when the previously imposed assumptions hold. Stability of the sliding motion is analysed and the sliding mode controller is parameterised in terms of the solutions of a set of linear matrix inequalities (LMIs) which facilitates design. The methodology is applied to an inverted pendulum model to validate the effectiveness of the results presented

Enhanced Fuzzy Sliding Mode Control to MotionController of Linear Induction Motor Drives

[[abstract]]In this paper, an enhanced fuzzy sliding mode control system (EFSMC) is proposed for a linear induction motor (LIM) to achieve the position tracking. First, the dynamic model of LIM is investigated for considering the end effect and the friction force into the observer-based compensation design to cope with the time-varying uncertainties. Then, a sliding mode control (SMC) based on the backstepping control technique is presented with the combination of two fuzzy logic controllers. The first fuzzy logic controller is proposed, through a dynamic tune of the sliding surface slope constant of the SMC according to the controlled system states by a fuzzy logic unit. To relax the need of the upper bound of the lumped uncertainties in the SMC, the second fuzzy logic controller is presented, in which the upper bound of the lumped uncertainties can be estimated by a fuzzy inference mechanism. Finally, the experiments for several scenarios are conducted to demonstrate the effectiveness and robustness of the designed controller.[[conferencetype]]國際[[conferencedate]]20140711~20140713[[booktype]]電子版[[iscallforpapers]]

Design of Adaptive Sliding Mode Fuzzy Control for Robot Manipulator Based on Extended Kalman Filter

In this work, a new adaptive motion control scheme for robust performance control of robot manipulators is presented. The proposed scheme is designed by combining the fuzzy logic control with the sliding mode control based on extended Kalman filter. Fuzzy logic controllers have been used successfully in many applications and were shown to be superior to the classical controllers for some nonlinear systems. Sliding mode control is a powerful approach for controlling nonlinear and uncertain systems. It is a robust control method and can be applied in the presence of model uncertainties and parameter disturbances, provided that the bounds of these uncertainties and disturbances are known. We have designed a new adaptive Sliding Mode Fuzzy Control (SMFC) method that requires only position measurements. These measurements and the input torques are used in an extended Kalman filter (EKF) to estimate the inertial parameters of the full nonlinear robot model as well as the joint positions and velocities. These estimates are used by the SMFC to generate the input torques. The combination of the EKF and the SMFC is shown to result in a stable adaptive control scheme called trajectory-tracking adaptive robot with extended Kalman (TAREK) method. The theory behind TAREK method provides clear guidelines on the selection of the design parameters for the controller. The proposed controller is applied to a two-link robot manipulator. Computer simulations show the robust performance of the proposed scheme