1.hybrid neuro fuzzy

8/7/2019 1.Hybrid neuro fuzzy

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ABSTRACT

In this paper we described DANIELA (Digital Analog Neuro-fuzzy Interface for

Enhanced Learning Applications), a real time neuro-fuzzy controller that

implements intelligent control algorithms mixing neuro-fuzzy paradigms with Finite

State Automata (FSA). The FSA tracks the state of the plant and selects the

parameter (e.g. weights) of the controller accordingly. This mixing allows the

system to change the control strategy according to the actual state of the plant which

leads to the use of a number of very simple controllers, instead of a more complex

single one which covers all possible plant conditions. For instance, our system

applied to a six legged walking robot uses specialized controllers for every different

plant state (for instance, forward and backward steps, curves,etc.).Adaptive learning

through neral networks have been highly enhanced. The tight link with a DSP

allows he NN hardware to be very simple since several operations related with the

NNs (learning, weights ,refresh etc..,) can be performed by the DSP .Moreover the

system can implement intelligent control paradigms mixing Neuro Fuzzy algorithms

with finite static automata and/or digital control algorithms.


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INTRODUCTION

Real-time control of non-linear plants is often a hard and computationally

intensive task. Neural networks and neuro-fuzzy systems are raising more and more

interest in the field of real-time control. Advantages of neuro-fuzzy techniques are

their non-linear characteristics, the capability of learning from examples, the human

friendly description language, the adaptation capability, etc. Furthermore the

availability of dedicated neuro-fuzzy processors permits a very fast implementation

of neuro-fuzzy algorithms, and therefore it allows to use these methods in real-time

applications, where a quite high sampling rate (namely, >1kHz) is required.

The main reasons for the use of those methods instead of more traditional

controllers are four-folds:

i) we wanted to test the validity of neuro-fuzzy control techniques;

ii) we had a lot of \human knowledge" on theproblem and fuzzy logic seemed

to be the most straightforward way to include it into the design of the

controller;

iii) we used this application as a testbed for the tight integrationof neuro-fuzzy

controllers with finite state automata; iii) we had available a neuro-fuzzy

computing engine (DANIELA) with good real time performance which wasmore suited to implement neuro-fuzzy than other algorithms.

EXAPODE WALKING MACHINE

Aim of this paper is to design, built and test a small hexapode walking

machine controlled by purposely developed neural chips, which can be used as a

remotely controlled observation device and can evolve as a true mobile Robert. The

machine can also be used as experimental test bed for different control strategies toinvestigate the possibility of small walking vehicles on different types of terrain.

The main characteristic of the machine are simple and light weight

mechanical architecture to contain overall cost, modularity to use the machine as a


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research tool; flexibility leading to the possibility of adapting the gain to a variety of

terrains, possibility of working as an autonomous system without the need of an

umbilical cord for either energy supply or control.

For the reasons, we chose to use low cost permanent magnets electric

motors in connection with rechargeable batteries and to adopt a gait in which the

vehicle is always in conditions of static equilibrium. A reptilian stance is adopted as

its energetic disadvantages are of small importance. The design chosen allows to

assume an insect stance even to switch to quadruped mammalian configuration.

The mass of the mechanical components including the electric motors is 21.3 kg .

The machine is already assembled and the first test are going on.

Each leg is made of a shinbone and a thighbone both 50cm long it has three

degree of freedom and is equipped with three current controlled motors. To obtain

high efficiency, switching amplifiers are employed which are perfectly matched

with the CPWM encoding used in the Neuro-Fuzzy core. All the actuators are

directly controlled by power drivers. One of the actuator must carry a significant

fraction of the hexapode weight(depending on the number of legs that support the

body) and so its driver has maximum output current of 20A (namely 480MW),

while for the other two drivers the output current is only 3A(namely 72W).

Since the control task is complex, the idea is to build a hierarchy of Neuro-

Fuzzy controllers have been chosen because of their good behavior in the presence

of nonlinear systems and for their intrinsic generalization capability.

PROTOTYPE BOARD

A general purpose system based on a TMS320CXX. These DSP is available.

The systems can support upto 256 channels for A/D and D/A conversion. Neuro-

Fuzzy core units, PWM encoders, digital I/O, memory expansions an so on.

The core of the system is a custom TMS320C31 board with up to 8Mbyte of

RAM and 128Kbyte of Non Volatile RAM.This board is interfaced to the additional

modules of the system by using a proprietary I/O bus. In this way each module is

mapped into the DSP memory space. Special module provides a communication


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with a host computer (eg a PC via a parallel port ) these modules connect a PC serial

or parallel port with the DSP board via the I/O bus.

This system is suitable both for industrial high performance control

applications and educational tools depending on the amount and kind of modules

plugged into it. Each module can be added with an extremely easy procedure and

can be controlled by the software environment.

So far, we have manufactured several module ; the main DSP board ,a

reconfigurable Neuro-Fuzzy computing core, the last host computer interface

modules an 8 channel 12 bit A/D and D/A converters, bus the complete system can

accept many others as special purpose processors, drivers and actuator interfaces,

special sensors modules and others.

The Neuro-Fuzzy module is described in section 2, while converters module are

classical Analog to Digital and Digital to Analog converters and also special

converter module that convert analog and digital signals into Pulse Width

Modulation signals and vice versa, the modules can be configured by the DSP by

setting some parameter as bits numbers for digital converters and full scale ranges

for the analog ones.

NEURO-FUZZY COMPUTING COREThe proposed Neuro-Fuzzy core implements on hardware several Neuro-Fuzzy

paradigms, its operation can be viewed either s a slave operation of the DSP or as an

independent one. For its operatin Neuro-Fuzzy core takes as inputs either external

signals(through dedicate A/D converters) or signals from the DSP subsystem via the

field bus and also Neuro-Fuzzy core outputs can be sent either to external outputs or

to the DSP.

The Neuro-Fuzzy computing core presented in this section is based on a custom

Neuro-Fuzzy processor called AMINAH and implements different paradigms as

Multilayer Preceptron(MLP), Linear Networks(LN),Radial Basis Function (RBF)

and also Weighted Radial Basic Function(WBRF) that are used to implement Fuzzy

inference systems. As shown in fig Neuro-Fuzzy processor core has also a local non


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volatile memory used to stores several banks of synaptic weights and some logic

interface to connect AMINAH and the non volatile memory to the main system bus,

it also has special analog circuits that provide timing, biases and reference signals.

Every synaptic weights bank define a particular Neuro-Fuzzy system that

includes synaptic weights and configuration information; only one of this bank is

stored into the internal volatile AMINAH memory and every time that the active

bank is changed the new weight and configuration information is automatically

loaded into it.

Neuro-Fuzzy core is considered as a slave device the DSP can perform several tasks

on it such as:

- DSP can write/read a bank of synaptic weights into/from the Neuro-Fuzzy

core non volatile memory.

- DSP can select a single bank to be the active one. The Neuro-Fuzzy

subsystem will then automatically write active bank content into AMINAH


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- DSP can read inputs and outputs of Neuro-Fuzzy subsystem to execute

learning algorithms or other supervision tasks.

Architecture of AMINAH

The AMNAH chip has two cascaded neural network layers called hidden and

output layer called hidden and output layer respectively. Architecture of one layer of

AMINAH shown in figure is based on N x M synaptic array and a vector of M

neurons. Each neuron j is connected to a row of N synapse plus a threshold (Sj,1

Sj,y , Sj,o) while each column (S1,I .Sy,I , So,I ) is connected to the same input X,

The hidden layer has N-7 inputs and M-16 outputs while the output layer has N-15

inputs and M-8 outputs.

AMINAH output signal are coded using Coherent Pulse Width Modulation

(CPWM), a particular Pulse Stream technique AMINAH input signal (Xj) can either

be analog or CPWM in the former case, they are converted using on chip analog to

CPWM converters.

Each AMINAH synapse uses two synapse weights (called respectively

excitatory and inhibitory weights) to compute the synaptic contribution. A copy of

active weights is stored internally into AMINAH using two capacitive memories per

synapse. The synapse contribution depends also on the chosen neural paradigm thiscan be either Multi Layer Perception (MLP),Radial Basis Function(RBF), or

Weighted Radial Basis Function (WBRF) of order 1, and therefore the system can

also implement Fuzzy System.

Synaptic contributions are charge quantities. For MLP operation each synapse

generate charge quantities proportional to the product of the stored weight and width

of the input CPWM signal; this product is quit linear and allow a small power

dissipation while for RBF/WRBF operation each synapse generate charge quantities

proportional to the distance between stored weight and width of the input CPWM

signal.

For the MLP paradigm, the synaptic contributions are presented as a

differential current (I+MLP, I-MLP), while for the RBF/WRBF paradigms, they are


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presented as a single ended current (I+MLP) while I-MLP is not used. Each row j of

synapse sums up all the currents on the two common summation lines I+MLP and I-MLP

(excitatory and inhibitory respectively).

The neuron (one for each row ) generate a current that is the algebraic sum of

them using programmable gain current mirrors multiplying it by a chosen factor

R1


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(2,1,1/2,,1/32) and can be turned on/off writing a configuration register

in each neuron.

NEURO-FUZZY CONTROLLER

DANIELA has also special Power Drivers used to drive the actuators in the

plant. Plant control is realized with a neuro-fuzzy algorithm computed by the neuro-

fuzzy processor. Algorithm characteristics both for neural and fuzzy are defined by

a matrix of synaptic weights and other neural parameters that are stored into the

neuro-fuzzy processor. In order to define different control strategies optionally, one

per each state of the FSA) there are different matrices of synaptic weights and neural

parameters stored in the weight memory, where every matrix is stored in a


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differentmemory bank.Only one bank can be active per time and its data are

transferred to the neuro-fuzzy processor internal memory by the Configuration and

Refresh Block every time the FSA changes states.

This memory organization allows to obtain different neural controllers by

changing the active memory bank. Weight memory is also used to store tables of

FSA states and outputs; these parameters are used by the microcontroller to

implement the FSA controller.

The microcontroller performs the following tasks:

1. on-line training: by sampling neuro-fuzzy inputs and outputs (also hidden) and

modifying matrices of synaptic weights stored into weight memory. As the

microcontroller is less powerful than the neural chip, learning is a relatively slow

process, but this is not a problem in most learning controllers.

2. FSA controller: by tracking the discrete states of the plant and selecting a suitable

memory bank in order to change the control strategy. The computational

requirements to compute the FSA are less stringent, as in most plants state

transitions are much less frequent than neural computation.

3. board self test: performing tests and diagnostic at power up.

4. interface with a host computer; which can either write data into the memory or

read several debugging information from the microcontrollers (such as neural

states,learning performance, actual FSM state,etc.).


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The purpose of the Conguration and Refresh Block is to transfer a matrix of

synaptic weights and the other neural parameters from the active memory bank to

the neural processor every time the FSA changes state.

Required Hexapod Phases

1. Modeling individual legs and the composite hexapod system.

2. Identification of kinematics and the dynamics of the Robert(individual legs

and complete Robert ).

3. Design and implementation of a neuro-fuzzy controller for individual legs.

4. Design of the motion co-ordination system(gait control and obstacles

avoidance).

5. Investigating the ability of the neural controller to autonomously learn to

move on uneven grounds and to deal with obstacles .

Control Hierarchy

Motion coordination control. Its acts like a central which gives the legs the

right high level control signals (such as Robert speed, Robert height from

ground ,radians of trajectory ) to let the hexapode execute properly gait. Obstacle

avoidance is achieved by giving proper trajectory information to each leg control.


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Only large obstacles are avoided, as small ones ( namely roughness of the ground,

small stones, holes etc ) are handled by each leg control.

Leg controls. They are like local controls that defines the trajectory and the

sequence of movements of each leg. They also recover and modify leg trajectories

when small local obstacles are encountered.Joint position controls, They receive the

angular positions and translate them into control signals for positioning motors.

Leg Control

Locomotion control is distributed evenly among the six legs. Independently from the

different gait, each leg cycles over six main different states.

Power phase: the leg on the ground where it supports and propels the body, moving

backward to the posterior extreme positions.

Lift phase: the leg rises from the posterior and loses its support function. Life phase

ends when the leg is high enough to swing forward.

Return phase: leg swings forward to the anterior extreme position. As soon as it

reaches this point it is ready for next phase.

Contact phase: leg lowers down to the ground. During this phase it start again to

support the body weight.


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Upstep phase: the leg rises when it hits an obstacle (obstacle avoidance). When the

height is enough,the leg returns in the Return state.

Backstep phase: the leg recover the right position after an obstacle avoidance or ifit is in time out.

The use of Neuro-Fuzzy approach (Fuzzy-State Automata) allows to smooth the

leg movements during walking (changes of speed and of directions without slipping

of the feet in ground).

Motion Coordination Control

The local leg controllers run simultaneously; however they are not

independent of one other. To let the robot move and deal with obstacles, an inter-leg

coordination is needed. For instance, when a leg is trying to step over an obstacle, it

can ask the supporting legs to momentarily stop, to raise the body, or to move the

robot backwards, when the obstacle it too large to step over. To achieve this inter-


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leg coordination, each Leg Control communicates with the Motion Coordination

Control.

Fuzzy State Automata

The Motion Coordination and the Leg Controls have been developed using a

number of custom Fuzzy State Automatas (FFSA), and in particular one for each leg

plus one for the Motion Coordination Control.

An FFSA looks like a finite state automaton which tracks the discrete states of

the system (for instance, power, lift, return, and contact phases). Each state is

associate with one of a set of different controllers(Cj), each one tailored to the

specific system state.

The main difference of an FFSA with respect to the above described method is

that transitions are not triggered by crisp events, but by fuzzy variables (all labels on

state transitions in figure are fuzzy events), and state transitions are fuzzy as well. It

immediately results that, at any time, the whole system is not necessarily in one

well-defined state, but it may well be in more states, each one with its own state

membership value.

State transitions are therefore smoother and slower, even if the controllers

described in each state are not designed to smooth them. As a consequence, all theindividual controllers may become as simple as a traditional PID, each one with a

different target for instance, when the leg is carrying the body weight, the PID

should keep body height and speed constant, while in the backward step it should

move as quick as possible towards a target point). Smoothing of transitions between

partial local trajectories is then taken care by the FFSA, which is much simpler to

design and tune than a controller with trajectory smoothing, as smoothing is intrinsic

in the FFSA working principles.Unfortunately FFSA have a drawback, that is, as the

system is often in more than one state,it has to process more than one controller at

any time. This increase of computing time often counterbalances the higher speed

that can be achieved through the use of simpler controllers. Globally, an FFSA has

approximately the same computing complexity of an FSA controlling Neuro-fuzzy


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controllers, but it is easier to design (especially for complex systems) and produces

smoother trajectories.

The actual FFSA is more complex, as it includes provisions for:

i) modication of hexapod attitude in presence of complex obstacles (e.g. to

walk inside low tunnels),

ii) modication of leg trajectory when walking sideways,

iii) control of alterna-tive paces for dynamic gaits (e.g. gallopping,or

climbing staircases);

iv) other special cases a leg might have to face .

Leg Identication and Neuro-Fuzzy Controller

Kinematic and dynamic identication of the leg model has been performed

with dierent neural paradigms such as MLP, FUZZY, RBF. Each leg motor was

driven by an independent net. Training has been done with several linear trajectories

(60 cm movement amplitude) at different heights (10, 20 and 30 cm) of the robot

body from the ground. The test has been performed on a completely different

parabolic trajectory with a slightly wider range to test the generalization ability of

the net. So far only simulations have been done, as the real hexapod is available

only since few days. Results for all the different paradigms are comparable,as shown

in table . As can be seen, an MLP network with only 3 hidden neurons is sufficient

to generate a trajectory with good precision (max. RMS error is = 4 mm for 80 cm

movement range).

A similar accuracy can be obtained with a polynomial (POLY) or with a

RBF networks. Dynamic identification of the leg [1, 2] has been performed using

the scheme shown in figure. For each coordinate a neural net has been trained to

evaluate the acceleration of the leg caused by the application of the u(t) stimulus that

represents the input current to the motor drivers. The leg position is represented by

s(t). The scheme used to train the neural controller for a single leg is shown in

figure. It is a case of inverse control [2], where the neural model of the leg (i) is


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required to back propagate the training errors to the controller (c). The system works

at 1 kHz sample frequency and this is equivalent, for the MLP (6), to 72000 CPS

(connections per second) for a single leg controller. This rate can be achieved only

with high computational power DSP or with a dedicated hardware implementation

such as DANIELA.

CONCLUSION

This paper has described an autonomously walking hexapod controlled by means of

Fuzzy State Automata, which have been developed starting from a mixture of Finite

State Automata and Neuro-Fuzzy controllers.Several paradigms have been

integrated, such as Neural Network, Fuzzy Systems, linear controllers, DSPs and

finite and fuzzy state Automata. The paper propose that approach is very promising

and this can be adopted to a wide variety of different applications such as, among

others, robotics, servo-controls and machine with moving parts with backlash and

low stiffness. The application can be improved to nonlinear controllers capable to

handle complex sequences and coordination functions.


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1.hybrid neuro fuzzy

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