**1. Introduction**

With the increasing threat of global warming and the waning of fossil fuel reserves, renewable energy is playing more and more important roles in both environmental and economic aspect

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

of lives. Solar photovoltaic (PV), as one of the most important renewable energy sources, has been widely used in more than a hundred of countries [1]. Solar panels are commonly deployed in harsh environments with high temperature, high humidity and strong winds [2], and are expected to be functional for over 30 years. Thus the glass panel, where the PV material will be located, has to be specially designed to sustain the harsh environments over its life of service [3]. As a necessary validation step, the strength and quality of the manufactured glass sheet must be tested and qualified, especially before the mass production stage. A rigorous test would require the strength of the solar glass sheet to be verified under a controlled environment with simulated temperature, humidity and induced vibration or forces on the PV glass sheet. Although commercial solutions exist for the various types of material-strength testing, environment simulated glass sheet strength testing is not available. As the booming of the PV industry and the increasing researching into stronger and better PV glass sheet, there has been a greater demand into the research and development of specially designed PV glass sheet test machines. This chapter presented the design of an environment controlled force loading machine specifically for solar panel glass sheet strength testing.

different outputs; (3) it can receive continuous input and produce continuous output; (4) it can accelerate the response speed by using addressing mode and (5) it is a nonlinear approximation and robust to the sequences of training data. Owing to above predominant merits, the nonlinear approximating capacity of the CMAC is superior to that of other traditional neural

A CMAC-Based Systematic Design Approach of an Adaptive Embedded Control Force Loading…

http://dx.doi.org/10.5772/intechopen.71420

257

In summary, various CMAC control algorithms have been formed so far, such as CMAC feedforward control [9–11], CMAC feedback control [12–14], CMAC optimal control [15, 16], CMAC fuzzy control [17–22], CMAC H-infinity control [23] and CMAC adaptive control [24–26]. The previous works can be divided into two categories: one is to improve the control structures of the CMAC, such as the CMAC feedforward control; the other is to improve the learning algorithms with other intelligent techniques, such as the fuzzy CMAC (FCMAC). Although a lot of complicated advanced CMAC control algorithms can well perform the control tasks in the simulations or with the computer-based control systems, the performances raised by some of them are very limited compared with the basic CMAC structure, even several advanced CMAC control algorithms, such as the FCMAC with the wavelet, cannot be implemented in real time with an embedded system due to the compli-

In this chapter, we adopt CMAC feedforward and PD feedback control, because [10, 11]: (1) the CMAC carries out the feedforward part to approximate the inverse model of the plant; (2) the PD controller actualizes the feedback part to train the CMAC and guarantee the stability of the closed-loop system; (3) compared with other neural networks or other complicated CMACs, limited computation cost of the simple and effective CMAC plus PD control removes CPU burden, so that the microcontroller can have enough computational

Since there is not much effort dealing with the systematic design problem of an embeddedcontrol glass strength testing machine, the contribution of this chapter is to present a three-step systematic design approach to address the embedded control issue: Firstly, the mathematical description of the system is studied using both theoretical and experimental method. A mathematical model is derived from the physical models of each component used, and an experiment is retrieved by employing Levy's method to identify parameters of the mathematical model. Secondly, an adaptive CMAC feedforward plus PD feedback controller is designed and simulated based on the identified system model as a preparation for the embedded system implementation. Finally, the proposed algorithm is applied to the embedded system with the same parameters as those of simulations, and experiments are conducted to verify both the identified model and designed controller. To design a machine systematically for practical use, these three steps are closely linked and indispensable. The three-step systematic design

Rest of this chapter is organized as follows. Section 2 analyses the system requirements and formulates a mathematical system model. Section 3 performs the system identification. Section 4 proposes the simulation-based controller design. Embedded system-based experi-

approach could benefit engineers in measurement and control as a guide.

ments are conducted in Section 5 and Section 6 concludes this work.

networks and is more suitable for real-time control in the real world.

cated improvement algorithms.

power available.

The glass sheet testing machine is basically a real-time electro-mechanical control system which is composed of actuators for simulated loading generation, sensors for monitoring and control feedback, a digital controller for the control of the overall system performance and mechanical supporting structures to hold everything together. The key part is the real-time digital controller which serves as the brain of such a system. Although computer-based control system with great computational power is able to host complex control algorithm, testing facilities with space, power and budget constraint cannot afford the size, power and cost of the computer-based design. Moreover, many extended and long hour testing scenarios require great robustness on the control system, where computer with standard operating system cannot meet the requirement. Embedded control systems with much lower power consumption, compact space, less cost and increased robustness is preferred over computer-based systems in many custom designed solutions [4–6]. Although embedded control systems have many advantages, they usually have limited or reduced computational powers that may not be able to host complex control algorithms. This poses challenges into the design of efficient real-time control algorithms. Specific embedded system-oriented control techniques must be carried out to guarantee control performances. For this reason, a simple and effective control law is preferred on the embedded control system and the Cerebellar Model Articulation Controller (CMAC) is a good choice.

The CMAC proposed by Albus [7] is a lookup-table adaptive neural network for estimating complicated nonlinear functions. The basic idea of CMAC is to quantify states from input, find memory addresses of states according to their locations in memory, add the content in the memory address, generate the CMAC output, compare the output with desired output and update the content in memory based on learning algorithms. Compared with other neural networks, advantages of the CMAC [8] are: (1) it is based on local learning and stores information in local memory, hence weights are changed slightly in each step and its learning speed is fast and suitable for real-time control; (2) it owns definite generalization property, so that the close inputs generate the close outputs, and the different inputs produce the different outputs; (3) it can receive continuous input and produce continuous output; (4) it can accelerate the response speed by using addressing mode and (5) it is a nonlinear approximation and robust to the sequences of training data. Owing to above predominant merits, the nonlinear approximating capacity of the CMAC is superior to that of other traditional neural networks and is more suitable for real-time control in the real world.

of lives. Solar photovoltaic (PV), as one of the most important renewable energy sources, has been widely used in more than a hundred of countries [1]. Solar panels are commonly deployed in harsh environments with high temperature, high humidity and strong winds [2], and are expected to be functional for over 30 years. Thus the glass panel, where the PV material will be located, has to be specially designed to sustain the harsh environments over its life of service [3]. As a necessary validation step, the strength and quality of the manufactured glass sheet must be tested and qualified, especially before the mass production stage. A rigorous test would require the strength of the solar glass sheet to be verified under a controlled environment with simulated temperature, humidity and induced vibration or forces on the PV glass sheet. Although commercial solutions exist for the various types of material-strength testing, environment simulated glass sheet strength testing is not available. As the booming of the PV industry and the increasing researching into stronger and better PV glass sheet, there has been a greater demand into the research and development of specially designed PV glass sheet test machines. This chapter presented the design of an environment controlled force

The glass sheet testing machine is basically a real-time electro-mechanical control system which is composed of actuators for simulated loading generation, sensors for monitoring and control feedback, a digital controller for the control of the overall system performance and mechanical supporting structures to hold everything together. The key part is the real-time digital controller which serves as the brain of such a system. Although computer-based control system with great computational power is able to host complex control algorithm, testing facilities with space, power and budget constraint cannot afford the size, power and cost of the computer-based design. Moreover, many extended and long hour testing scenarios require great robustness on the control system, where computer with standard operating system cannot meet the requirement. Embedded control systems with much lower power consumption, compact space, less cost and increased robustness is preferred over computer-based systems in many custom designed solutions [4–6]. Although embedded control systems have many advantages, they usually have limited or reduced computational powers that may not be able to host complex control algorithms. This poses challenges into the design of efficient real-time control algorithms. Specific embedded system-oriented control techniques must be carried out to guarantee control performances. For this reason, a simple and effective control law is preferred on the embedded control system and the Cerebellar Model Articulation Controller

The CMAC proposed by Albus [7] is a lookup-table adaptive neural network for estimating complicated nonlinear functions. The basic idea of CMAC is to quantify states from input, find memory addresses of states according to their locations in memory, add the content in the memory address, generate the CMAC output, compare the output with desired output and update the content in memory based on learning algorithms. Compared with other neural networks, advantages of the CMAC [8] are: (1) it is based on local learning and stores information in local memory, hence weights are changed slightly in each step and its learning speed is fast and suitable for real-time control; (2) it owns definite generalization property, so that the close inputs generate the close outputs, and the different inputs produce the

loading machine specifically for solar panel glass sheet strength testing.

(CMAC) is a good choice.

256 Adaptive Robust Control Systems

In summary, various CMAC control algorithms have been formed so far, such as CMAC feedforward control [9–11], CMAC feedback control [12–14], CMAC optimal control [15, 16], CMAC fuzzy control [17–22], CMAC H-infinity control [23] and CMAC adaptive control [24–26]. The previous works can be divided into two categories: one is to improve the control structures of the CMAC, such as the CMAC feedforward control; the other is to improve the learning algorithms with other intelligent techniques, such as the fuzzy CMAC (FCMAC). Although a lot of complicated advanced CMAC control algorithms can well perform the control tasks in the simulations or with the computer-based control systems, the performances raised by some of them are very limited compared with the basic CMAC structure, even several advanced CMAC control algorithms, such as the FCMAC with the wavelet, cannot be implemented in real time with an embedded system due to the complicated improvement algorithms.

In this chapter, we adopt CMAC feedforward and PD feedback control, because [10, 11]: (1) the CMAC carries out the feedforward part to approximate the inverse model of the plant; (2) the PD controller actualizes the feedback part to train the CMAC and guarantee the stability of the closed-loop system; (3) compared with other neural networks or other complicated CMACs, limited computation cost of the simple and effective CMAC plus PD control removes CPU burden, so that the microcontroller can have enough computational power available.

Since there is not much effort dealing with the systematic design problem of an embeddedcontrol glass strength testing machine, the contribution of this chapter is to present a three-step systematic design approach to address the embedded control issue: Firstly, the mathematical description of the system is studied using both theoretical and experimental method. A mathematical model is derived from the physical models of each component used, and an experiment is retrieved by employing Levy's method to identify parameters of the mathematical model. Secondly, an adaptive CMAC feedforward plus PD feedback controller is designed and simulated based on the identified system model as a preparation for the embedded system implementation. Finally, the proposed algorithm is applied to the embedded system with the same parameters as those of simulations, and experiments are conducted to verify both the identified model and designed controller. To design a machine systematically for practical use, these three steps are closely linked and indispensable. The three-step systematic design approach could benefit engineers in measurement and control as a guide.

Rest of this chapter is organized as follows. Section 2 analyses the system requirements and formulates a mathematical system model. Section 3 performs the system identification. Section 4 proposes the simulation-based controller design. Embedded system-based experiments are conducted in Section 5 and Section 6 concludes this work.
