Advanced Control Technology Essay

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MSc Systems Engineering and Engineering Management

Advanced Control Technology (EEM4015)

Design Control Systems Using PID Controller and Fuzzy Logic Controller with MATLAB/SIMULINK

Name: Jing Qian Student No: 0807789 Tutor teacher: ErPing Zhou

Contents:
1. Abstract....................................................................................................3
2 Introduction.............................................................................................4
3 Modeling and Simulation Tasks.........................................................................................................8
4. References............................................................................................14

1. Abstract
When controlling the water level of a tank, whose input subjects to a property of uncertainty, the traditional control methods of PID are struggling to achieve the satisfying objective. Instead, the fuzzy control method is widely introduced in practice. This paper discusses a typical tank system of which the water level is controlled based on the fuzzy algorithm, and makes a simulation about the dynamic action of the system under the platform of Matlab. The result proves that the fuzzy method can perfectly realize the controlling objective. The purposes of this assignment are to design, simulate, analyses and implement of a PID controller and a fuzzy logic control system using MATLAB/SIMULINK.
Keywords: Fuzzy control, water tank, Matlab simulation, PID controller

2. Introduction
1 Background
Fuzzy logic
A fuzzy controller or model uses fuzzy rules, which are linguistic if-then statements involving fuzzy sets, fuzzy logic, and fuzzy inference. Fuzzy rules play a key role in representing expert control/modeling knowledge and experience and in linking the input variables of fuzzy controllers/models to output variable (or variables). Two major types of fuzzy rules exist, namely, madman fuzzy rules and Takagi-Surgeon (TS, for short) fuzzy rules.
Fuzzy controller can be:
• Very robust
• Very quick and cheaper to implement
• Can use multiple inputs and outputs sources
• Much simpler than its predecessors (linear algebraic equations)
• Can be easily modified
Samples of using fuzzy logic:

F-117 Flight Control System

Camcorder – Stabilization

PID controller
A PID controller is a simple three-term controller. The letters P, I and D stand for:
P - Proportional
I - Integral
D – Derivative
By tuning the three parameters in the PID controller algorithm, the controller can provide control action designed for specific process requirements.
Due to its simplicity and excellent if not optimal performance in many applications, ID controllers are used in more than 95% of closed-loop industrial processes. It can be tuned by operators without
Extensive background in Controls, unlike many other modern controllers that are much more complex but often provide only marginal improvement. In fact, most PID controllers are tuned on-site.
Samples of using PID controller:

DC motor
2 History of PID controller
In 1939, the Taylor Instrument Companies introduced a completely redesigned version of its fulsome pneumatic controller. In addition to proportional and reset control actions, this new instrument provided an action which the Taylor Instrument Companies called pre-act. In the same year the Foxboro Instrument Company added Hyper-reset to the proportional and reset control actions provided by their Stabling pneumatic controller. The pre-act and Hyper-reset actions each provide a control action proportional to the derivative of the error signal. Reset provides a control action proportional to the integral of the error signal and hence both controllers
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