A Modular Programmable CMOS Analog Fuzzy Controller Chip

We present a highly modular fuzzy inference analog CMOS chip architecture with on-chip digital programmability. This chip consists of the interconnection of parameterized instances of two different kind of blocks, namely label blocks and rule blocks. The architecture realizes a lattice partition of the universe of discourse, which at the hardware level means that the fuzzy labels associated to every input (realized by the label blocks) are shared among the rule blocks. This reduces the area and power consumption and is the key point for chip modularity. The proposed architecture is demonstrated through a 16-rule two input CMOS 1-μm prototype which features an operation speed of 2.5 Mflips (2.5×10^6 fuzzy inferences per second) with 8.6 mW power consumption. Core area occupation of this prototype is of only 1.6 mm 2 including the digital control and memory circuitry used for programmability. Because of the architecture modularity the number of inputs and rules can be increased with any hardly design effort.This work was supported in part by the Spanish C.I.C.Y.T under Contract TIC96-1392-C02- 02 (SIVA)

A modular CMOS analog fuzzy controller

The low/medium precision required for many fuzzy applications makes analog circuits natural candidates to design fuzzy chips with optimum speed/power figures. This paper presents a sixteen rules-two inputs analog fuzzy controller in a CMOS 1 /spl mu/m single-poly technology based on building blocks implementations previously proposed by the authors (1995). However, such building blocks are rearranged here to get a highly modular architecture organized from two high level blocks: the label block and the rule block. In addition, sharing of membership function circuits allows a compact design with low area and power consumption and its highly modular architecture will permit to increase the number of inputs and rules in future chips with hardly design effort. The paper includes measurements from a silicon prototype of the controller

Using Building Blocks to Design Analog Neuro-Fuzzy Controllers

We present a parallel architecture for fuzzy controllers and a methodology for their realization as analog CMOS chips for low- and medium-precision applications. These chips can be made to learn through the adaptation of electrically controllable parameters guided by a dedicated hardware-compatible learning algorithm. Our designs emphasize simplicity at the circuit level—a prerequisite for increasing processor complexity and operation speed. Examples include a three-input, four-rule controller chip in 1.5-μm CMOS, single-poly, double-metal technology

A new dynamic approach for non-singleton fuzzification in noisy time-series prediction

Non-singleton fuzzification is used to model uncertain (e.g. noisy) inputs within fuzzy logic systems. In the standard approach, assuming the fuzzification type is known, the observed [noisy] input is usually considered to be the core of the input fuzzy set, usually being the centre of its membership function. This paper proposes a new fuzzification method (not type), in which the core of an input fuzzy set is not necessarily located at the observed input, rather it is dynamically adjusted based on statistical methods. Using the weighted moving average, a few past samples are aggregated to roughly estimate where the input fuzzy set should be located. While the added complexity is not huge, applying this method to the well-known Mackey-Glass and Lorenz time-series prediction problems, show significant error reduction when the input is corrupted by different noise levels

A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions

Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems

Most applications of both type-1 and type-2 fuzzy logic systems are employing singleton fuzzification due to its simplicity and reduction in its computational speed. However, using singleton fuzzification assumes that the input data (i.e., measurements) are precise with no uncertainty associated with them. This paper explores the potential of combining the uncertainty modelling capacity of interval type-2 fuzzy sets with the simplicity of type-1 fuzzy logic systems (FLSs) by using interval type-2 fuzzy sets solely as part of the non-singleton input fuzzifier. This paper builds on previous work and uses the methodological design of the footprint of uncertainty (FOU) of interval type-2 fuzzy sets for given levels of uncertainty. We provide a detailed investigation into the ability of both types of fuzzy sets (type-1 and interval type-2) to capture and model different levels of uncertainty/noise through varying the size of the FOU of the underlying input fuzzy sets from type-1 fuzzy sets to very “wide” interval type-2 fuzzy sets as part of type-1 non-singleton FLSs using interval type-2 input fuzzy sets. By applying the study in the context of chaotic time-series prediction, we show how, as uncertainty/noise increases, interval type-2 input fuzzy sets with FOUs of increasing size become more and more viable

Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs

Input uncertainty, e.g., noise on the on-board camera and inertial measurement unit, in vision-based control of unmanned aerial vehicles (UAVs) is an inevitable problem. In order to handle input uncertainties as well as further analyze the interaction between the input and the antecedent fuzzy sets (FSs) of non-singleton fuzzy logic controllers (NSFLCs), an input uncertainty sensitivity enhanced NSFLC has been developed in robot operating system (ROS) using the C++ programming language. Based on recent advances in non-singleton inference, the centroid of the intersection of the input and antecedent FSs (Cen-NSFLC) is utilized to calculate the firing strength of each rule instead of the maximum of the intersection used in traditional NSFLC (Tra-NSFLC). An 8-shaped trajectory, consisting of straight and curved lines, is used for the real-time validation of the proposed controllers for a trajectory following problem. An accurate monocular keyframe-based visual-inertial simultaneous localization and mapping (SLAM) approach is used to estimate the position of the quadrotor UAV in GPS denied unknown environments. The performance of the Cen-NSFLC is compared with a conventional proportional integral derivative (PID) controller, a singleton FLC (SFLC) and a Tra-NSFLC. All controllers are evaluated for different flight speeds, thus introducing different levels of uncertainty into the control problem. Visual-inertial SLAM-based real time quadrotor UAV flight tests demonstrate that not only does the Cen-NSFLC achieve the best control performance among the four controllers, but it also shows better control performance when compared to their singleton counterparts. Considering the bias in the use of model based controllers, e.g. PID, for the control of UAVs, this paper advocates an alternative method, namely Cen-NSFLCs, in uncertain working environments

A multiplexed mixed-signal fuzzy architecture

Analog circuits provide better area/power efficiency than their digital counterparts for low-medium precision requirements. This limit in precision as well as the lack of design tools when compared to the digital approach, imposes a limit of complexity, hence fuzzy analog controllers are usually oriented to fast low-power systems with low-medium complexity. The paper presents a strategy to preserve most of the advantages of an analog implementation, while allowing a notorious increment of the system complexity. Such strategy consists in implementing a reduced number of rules, those that really determine the output in a lattice controller, which we call analog core, then this core is dynamically programmed to perform the computation related to a specific rule set. The data to program the analog core are stored in a memory, and constitutes the whole knowledge base in a kind of virtual rule set. HSPICE simulations from an exemplary controller are shown to illustrate the viability of the proposal