Characteristics of Control System

Although different systems are designed to perform different functions, all of them have to meet some common requirements. The major characteristics of a typical control system, which is often used as measures of performance to evaluate a system under consideration, are the following:

Stability

The stability of a system relates to its response to inputs or disturbances. “Systems stability can be defined in terms of its response to external impulse inputs”. A system which remains in a constant state unless affected by an external action and which returns to a constant state when the external action is removed can be considered to be stable. A system is stable if every bounded input produces a bounded output. A system is stable if its impulse response approaches zero as time approaches infinity. The objectives of stability analysis are the determination of the following:

• The steady state performance

• The transient response

Accuracy

The accuracy indicates deviation of the actual output from its desired value and it is a relative measure of system performance. Generally, the accuracy of a control system is improved by using control models such as integral or integral plus proportional.

Speed of response

The speed of response is a measure of how quickly an output attains a steady-state value after the input is applied. A practical system must have a finite response time.

Sensitivity

The sensitivity of a system is a system measure of how sensitive the output is to the changes in the value of physical components as well as environmental conditions. The sensitivity function has an important role to play in judging the performance of the controller because it also describes (from Fig. 3.14) the effect of the disturbance d(s)on the control output Y(s). For the controller to achieve good disturbance rejection, it is obvious that E(s) be made as small as possible by an approximate design for the controllerGc(s).

From the Fig. 3.14,

E(s) =R(s)−Y(s) (3.44)

E(s) =R(s)−[G_p(s).U(s) +d(s)] (3.45) But,

U(s) =Gc(s).E(s) (3.46)

Putting the values ofU(s)in the Eq. 3.45 we get,

E(s) =R(s)−Gp(s).Gc(s).E(s)−d(s) (3.47)

Now, rearranging these terms,

Hence,

E(s)

R(s)−d(s) =

1

[1+G_p(s).G_c(s)] (3.49)

then sensitivity functionE(S)can be written as follow:,

E(S) = E(s)

R(s)−d(s) =

1

[1+G_p(s).G_c(s)] (3.50)

3.9

Summary

In this chapter kinematic analysis of mobile robot has been carried out. In the next chapter Fuzzy logic controller has been analysed for navigation of mobile robot.

F

UZZY

LOGIC AND

CONTROL

STRUCTURE

In recent years, robots have to play key role in many kinds of industries to construct op- erational production systems and also to promote automation and unmanned fabrication. To concern these problems researchers suggested distinctive feature of robot having mobile platform, is that they can change its places according to the jobs and complete the jobs ac- cording to task demands by the total fabrication control system. Accordingly, this chapter presents the enlargement and tentative assessment of a logical method, based on fuzzy logic to localize mobile robots in a brainy space using sensor network.

The modeling of intelligent fuzzy logic based navigator involves an obstacle avoidance behavior and target seeking behavior [7]. The input fuzzy set creates the map demonstrat- ing the mobile robot state in space and resolute by sensor readings to the output fuzzy sets [135] representing the mobile robot in action space. These sensor systems are the col- lection of autonomous network sensing devices that has integrate sensing, considering the problem capability, loading and erasing the maps, and communication capabilities. The measurements consist of both information about the sensor node location relative to the sensor network, e.g., distance and statistics on the sensor node motion, such as drive esti- mation gotten from odometers. Fuzzy logic offers controlling tools [80] to illustrate and handle the different aspects of ambiguity in measurements.

The core of this suggested approach, divided into two independent algorithms i.e.: ,→ The sensor integration network: It retrieves data from environment by the help of

sensor network and produces a compact depth image, i.e. disparity map, of the scene. ,→ The fuzzy decision making algorithm: It examine the data for previous section and

adopt the finest direction for navigation of robot and avoid obstacles, based on a simple fuzzy inference system (FIS).

This chapter describes the navigation control technique of mobile robot in anonymous environs with obstacles avoidance and prepares sensor-based control architecture for mobile robot navigation in anonymous environs using fuzzy based technology.

4.1

Introduction

In recent years, the use of fuzzy logic or fuzzy set theory being extensively used to develop suitable mobile robot control algorithm due to its easier technical skills over mathematical form as well as for development of control architecture for complex system like, the unit of washing machine to speedboats, a simple mobile robot to space mobile robot and air condition units to hand-held autofocus cameras. Here, fuzzy logic is exemplified with respect to navigation system of a mobile robot.

The control engineers have usually trusted on mathematical models for their design. However, if system is more complex; the mathematical model is less effective. This kind of philosophy create the researcher attention towards the motivation of fuzzy logic, which formulized by Zadeh the founder of fuzzy set theory [84], as the principle of incompatibil- ity. Zadeh stated that “As the complexity of the system increase, our ability to make precise and yet significant statements about its behaviour diminishes until a threshold is reached beyond which precision and significance (or relevance) became almost mutually exclusive characteristics” [136].