A First Course in FUZZY and NEURAL CONTROL

4.4. FUZZY CONTROLLER DESIGN 151 4.4.2 Example: controlling dynamics of a servomotor We now investigate the design of a fuzzy co ...

152 CHAPTER 4. FUZZY CONTROL (c) Motor voltage Figure 4.9. Prototype fuzzy membershipfunctions for DC servomotor control This kn ...

4.4. FUZZY CONTROLLER DESIGN 153 If position error is NE and velocity is NA, then motor voltage is Negative. If position error ...

154 CHAPTER 4. FUZZY CONTROL Figure 4.12. Simulink diagram for DC servomotor control using the transfer function model of the DC ...

4.4. FUZZY CONTROLLER DESIGN 155 position, and then begin to decrease the voltage as the error approaches zero. There is some tr ...

156 CHAPTER 4. FUZZY CONTROL Figure 4.16. Further tuning of membership function The simulation results in Figure 4.17 clearly sh ...

4.5. EXERCISES AND PROJECTS 157 five for each of the input variables and the output variable, then we have a total of 25 fuzzy r ...

158 CHAPTER 4. FUZZY CONTROL (d) Compare the results from the PID controller and the fuzzy controller. The open-loop transfer f ...

4.5. EXERCISES AND PROJECTS 159 radius of the ball, andgthe gravitational constant, the Lagrange equa- tion of motion can be wri ...

160 CHAPTER 4. FUZZY CONTROL 4.18. Magnetic levitation The mathematical model of this system can be obtained as follows. For the ...

4.5. EXERCISES AND PROJECTS 161 (b) Develop a fuzzy controller that can magnetically levitate the steel ball for various positio ...

162 CHAPTER 4. FUZZY CONTROL The following basic linear relationships are valid for this system, namely, q =Rh=rate offlow throu ...

4.5. EXERCISES AND PROJECTS 163 where dydt(t) is the rocket velocity at timet(the plant output),y(t)is the altitude of the rocke ...

164 CHAPTER 4. FUZZY CONTROL -1 -2 2 1 -2 (^12) 0 1 2 3 4 z For the antecedent membership functions, take triangular functions o ...

Chapter 5 NEURAL NETWORKS FOR CONTROL In this chapter, we will introduce computational devices known as neural net- works that a ...

166 CHAPTER 5. NEURAL NETWORKS FOR CONTROL of inputs and learn solely from training samples. As mathematical models for biologic ...

5.1. WHAT IS A NEURAL NETWORK? 167 so that the activation of that neuron is f √n X i=1 wixi−b ! = ( 1 if Pn i=1wixi≥b 0 if Pn i= ...

168 CHAPTER 5. NEURAL NETWORKS FOR CONTROL Figure 5.3. Perceptron the middle layer, called thehidden layer,haspnodes, and the ou ...

5.2. IMPLEMENTING NEURAL NETWORKS 169 Figure 5.4. Two-layer neural network mented by a perceptron, a neural network without hidd ...

170 CHAPTER 5. NEURAL NETWORKS FOR CONTROL First, to see whether or not this problem is solvable, we look at the domain of the f ...