**Author details**

lot of manpower and time, which means that the optimal PID parameters are difficult to be obtained by people's tuning, and the inappropriate parameters cannot guarantee the control performances to meet the control requirements. In addition, the PID control law is a kind of the linear control law which owns few robustness to the disturbances. It means that even though the PID parameters are tuned optimally and perfectly in the simulations, the tuned parameters may perform poor in the real world due to disturbances and uncertainties.

**Method Maximum absolute error Variance** CMAC 3.79 1.69 Inverse model 6.13 3.85

**Table 2.** Comparisons between the CMAC and the inverse model.

Thus, combining the adaptive CMAC and the traditional PID to construct an intelligent neural network PID controller, can automatically identify the controlled plant and adaptively adapt the control parameters of the CMAC, which can solve the difficult problem of tuning parameters of the traditional PID controller. As shown in Sections 4 and 5, the PID parameters of the proposed CMAC algorithm in the experiments are same as those in the simulations, which verifies control performances of the proposed CMAC algorithm are independent of tuning PID parameters. Experimental results in the real world in Section 5 also demonstrate the robustness of the proposed algorithm owing to the CMAC neural network, while the tra-

In this chapter, a three-step systematic design approach is proposed to design an adaptive control system for practical use. We firstly study the system model identification problem of the embedded control material-strength testing system, including mathematical modeling of all the open-loop physical components and parameters identification of the mathematical model. Both theoretical analyses and experimental comparisons validate the identified transfer function of the system model is applicable for controller design and simulation. Next, benefited from limited computation cost and compensation ability to the modeling error, a simple and effective CMAC plus PD controller is simulated based on the identified system model, and then applied to the embedded control system for real-time force tracking. Both numerical simulations and actual experiments illustrate the proposed algorithm satisfactorily performs the tracking control task under real-time constraints of the embedded system.

On the other hand, different strength features of different types of the material plates will affect the control performances. Since the yield strength generated by the solar panel glass has

If the tested material plate is not a solar panel glass and the yield strength generated does not

in Eq. (7) is a constant.

in Eq. (7) will not be a

an almost linear relationship with the deformation of the glass sheet, *KF*

have a linear relationship with the deformation of the material plate, *KF*

ditional PID controller does not have this capacity.

**6. Conclusions**

270 Adaptive Robust Control Systems

Jian Chen1,2\*, Peng Li<sup>3</sup> , Gangbing Song<sup>3</sup> , Shubo Wang1,2, Zichao Zhang1,2, Guangqi Wang1,2, Yu Tan1,2 and Yongjun Zheng1,2

\*Address all correspondence to: jchen@cau.edu.cn

1 College of Engineering, China Agricultural University, Beijing, China

2 Key Laboratory of Soil-Machine-Plant System Technology, Ministry of Agriculture, Beijing, China

3 Department of Mechanical Engineering, University of Houston, TX, USA
