由於車型自走車路徑追蹤的模型包含馬達動態學，應用具有自走車姿態的線性動態追蹤誤差的第一切換曲面設計虛擬控制(即兩個馬達需要的電流)。接著，以具有虛擬控制的線性動態追蹤誤差的第二切換曲面設計階層式滑動控制(HSMC)，使得直接輸出(即兩個馬達的電流)漸進和強健地追蹤虛擬控制輸入，如此，自走車便可追上想要的路徑(即所規劃的自走車任務)。本論文選定軟硬體共同設計的平台(即DE2i-150)，即以軟體撰寫控制演算法(包括虛擬控制與階層式滑動控制)，以硬體執行控制訊號(即驅動馬達的PWM)和感應器的輸入(即馬達的位置與速度解碼、USB讀取鏡頭影像)。此外，以RGBD視覺系統偵測障礙物與辨識目標，根據所偵測的障礙物即時路徑規劃躲避，根據SURF的目標比對及辨識目標，並估測其姿態以趨近目標，完成所設定的任務。最後，藉由車型自走車追蹤直線及圓形路徑，並同時進行避障與趨近目標等相關實驗，驗證本論文提出之控制系統的有效性及強健性。

Since the derived model for the path tracking of car-like mobile robot (CLMR) contains motor dynamics, virtual control inputs (i.e., two desired motor currents) are designed by the first switching surface, which is set as the linear dynamic tracking error of mobile robot’s pose. Subsequently, the second switching surface set as the linear dynamic tracking error of virtual control input is employed to design a hierarchical sliding mode control (HSMC) such that the direct output (i.e., two motor currents) asymptotically and robustly tracks the virtual control input. Simultaneously, the CLMR asymptotically tracks the planned path. A hardware/software co-design platform is also employed to develop the software for the control algorithm and the hardware for the control signal (e.g., the PWM for driving the motor) and for the sensor inputs (e.g., the decoder for obtaining the position or velocity of motor, the USB interface for capturing the image). The RGBD vision system is employed to detect the obstacle(s) through the depth signal and to recognize target through SURF (Speed-Up Robust Feature) method. Their distances with respect to CLMR are also estimated to execute the obstacle avoidance and target approach. Finally, the experiments of the straight line and circular path tracking with simultaneous obstacle avoidance and target approach of CLMR confirm the effectiveness, efficiency, and robustness of the proposed control system.

[1] C. C. Tsai, H. C. Huang and C. K. Chan, “Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation,” IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4813–4823, Oct. 2011.
[2] W. Chung, H. Kim, Y. Yoo, C. Moon, and J. Park, “The detection and following of human legs through inductive approaches for a mobile robot with a single laser range finder,” IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3156–3166, Aug. 2012.
[3] C. G. Yang, Z. J. Li and J. Li, “Trajectory planning and optimized adaptive control for a class of wheeled inverted pendulum vehicle models,” IEEE Trans. Cybern., vol. 43, no. 1, pp. 24- 36, Feb. 2013.
[4] M. Zhang and H. H. T. Liu, “Game-theoretical persistent tracking of a moving target using a unicycle-type mobile vehicle,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6222-6233, Nov. 2014.
[5] C. F. Juang, Y. H. Chen and Y. H. Jhan, “Wall- following control of a hexapod robot using a data- driven fuzzy controller learned through differential evolution,” IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 611-619, Jan. 2015.
[6] J. Yu, C. Wang and G. Xie, “Coordination of multiple robotic fish with applications to underwater robot competition,” IEEE Trans. Ind. Electron., 2015 (to be appeared).
[7] C. L. Hwang and N. W. Chang, “Fuzzy decentralized sliding-mode control of car-like mobile robots in a distributed sensor-network space,” IEEE Trans. Fuzzy Syst., vol. 16, no. 1, pp. 97-109, Feb. 2008.
[8] T. S. Li, Y. C. Yeh, J. D. Wu , M. Y. Hsiao and C. Y. Chen,“Multifunctional intelligent autonomous parking controllers for carlike mobile robots,” IEEE Trans. Ind. Electron., vol. 57, no. 5, pp. 1687-1700, May 2010.
[9] C. L. Hwang and H. M. Wu, “Trajectory tracking of a mobile robot with frictions and uncertainties using hierarchical sliding-mode under-actuated control,” IET Control Theory & Applications, vol. 7, no. 7, pp. 952- 965, 2013.
[10] R. Loureiro, S. Benmoussa, Y. Touati, R. Merzouki and B. O. Bouamama, “Integration of fault diagnosis and fault-tolerant control for health monitoring of a class of MIMO intelligent autonomous vehicles,” IEEE Trans. Veh. Technol., vol. 63, no. 1, pp. 30-39, Jan. 2014.
[11] J. C. L. Barreto S., A. G. S. Conceic˜ao, C. E. T. D´orea, L. Martinez and E. R. de Pieri, “Design and implementation of model-predictive control with friction compensation on an omnidirectional mobile robot”, IEEE/ASME Trans. Mechatronics, vol. 19, no. 2, pp. 467-476, Apr. 2014.
[12] R. J. Wai and R. Muthusamy, “Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics,” IEEE Trans. Neural Netw. & Learn. Syst., vol. 24, no. 2, pp. 274-287, Feb. 2013.
[13] H. S. Kim and J. B. Song, “Multi-DOF counter balance mechanism for a service robot arm,” IEEE/ASME Trans. Mechatronics, vol. 19, no.6, pp. 1756-1763, Dec. 2014.
[14] C. M. Lin and Y. J. Mon, “Decoupling control by hierarchical fuzzy sliding-mode controller,” IEEE Trans. Contr. Syst. Technol., vol. 13, no. 4, pp. 593-598, Jul. 2005.
[15] W. Wang, X.D. Liu and J.Q. Yi, “Structure design of two types of sliding-mode controllers for a class of under-actuated mechanical systems,” IET Control Theory Appl., vol. 1, no. 1, pp. 163-172, Jan. 2007.
[16] D. Qian, J. Yi and D. Zhao, "Control of a class of under-actuated systems with saturation sing hierarchical sliding mode", IEEE Int. Conf. on Robotics and Automation, Pasadena, pp. 2429-2434, May 2008.
[17] C. L. Hwang, C. C. Chiang and Y. W. Yeh, “Adaptive fuzzy hierarchical sliding-mode control for the trajectory tracking of uncertain under-actuated nonlinear dynamic systems,” IEEE Trans. Fuzzy Syst., vol. 22, no. 2, pp. 286-297, Apr. 2014.
[18] J. Xiao, P. Wang and L. Setyawan, “Hierarchical control of hybrid energy Storage system in DC microgrids,” IEEE Trans. Ind. Electron., 2015 (to be appeared).
[19] Z. Qu, J. Wang and C. E. Plaisted, “A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles,” IEEE Trans. Robotics, vol. 20, no. 6, pp. 978-993, Dec. 2004.
[20] T. Orlowska-Kowalska and M. Kaminski, “FPGA implementation of the multilayer neural network for the speed estimation of the two-mass drive system”, IEEE Trans. Ind. Inform., vol. 7, no. 3, pp. 436-445, Nov. 2011.
[21] J. Rodríguez-Araújo, J. J. Rodríguez-Andina, J. Fariña and M. Y. Chow, “Field-programmable system-on -chip for localization of UGVs in an Indoor iSpace,” IEEE Trans. Ind. Inform., vol. 10, no. 2, pp. 1033- 1043, May 2014.
[22] E. N. Hartley, J. L. Jerez, A. Suardi, J. M. Maciejowski, E. C. Kerrigan and G. A. Constantinides, “Predictive control using an FPGA with application to aircraft control “, IEEE Trans. Contr. Syst. Technol., vol. 22, no. 3, pp. 1006-1017, May 2014.
[23] Z. Hajduk, B. Trybus and J. Sadolewski, “Architecture of FPGA embedded multiprocessor programmable controller,” IEEE Trans. Ind. Electron., vol. 62, no. 5, pp. 6952-6961, May 2015.
[24] E. Ohn-Bar and M. M. Trivedi, “Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations,” IEEE Trans. Intell. Trans. Syst., vol. 15, no. 6, pp. 2368-2377, Dec. 2014.
[25] M. Gupta, L. Behera, V. K. Subramanian, and M. M. Jamshidi, “A robust visual human detection approach with UKF-based motion tracking for a mobile robot,” IEEE Syst. Journal, to be appeared, 2015.
[26] H. Zhang and L. E. Parker, “CoDe4D: Color-depth local spatio-temporal features for human activity recognition from RGB-D videos,” IEEE Trans. Circuits and Systems for Video Technology, to be appeared, 2015.
[27] C. L. Hwang, B. L. Chen, H. H. Huang and H. T. Syu, “Hybrid learning model and MMSVM classification for on-line visual imitation of a human with 3-D motions,” Robotics and Autonomous Systems, to be appeared, 2015.
[28] L. Juan and O. Gwun, “A comparison of SIFT, PCA- SIFT and SURF,” International Journal of Image Processing, vol. 3, no. 4, pp. 143-151, 2009.
[29] J. W. Hsieh, L. C. Chen and D. Y. Chen, “Symmetrical SURF and its applications to vehicle detection and vehicle make and model recognition,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 1, pp. 6-20, Feb. 2014.
[30] http://www.treffer.com.br/produtos/maxon/motores/pdf/126.pdf .
[31] C. L. Hwang, “Neural-network-based variable structure control of electrohydraulic servosystems subject to huge uncertainties without the persistent excitation,” IEEE/ASME Trans. Mechatronics, vol.4, no.1, pp. 50-59, Jan. 1999.
[32] H. K. Khalil, Nonlinear Systems, Prentice-Hall, 2nd Ed., 1996.
[33] H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded Up Robust Features,” Proceedings of European Conference on Computer Vision, 2006, pp. 404-417.