14 research outputs found

    Disturbance Attenuating Tracking Controller Design of a Quadrotor UAV via a T-S Fuzzy-model-based Disturbance Observer

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    In this thesis, The disturbance attenuating tracking controller design method of a quadrotor unmanned aerial vehicle (UAV) via the Takagi-Sugeno (T-S) fuzzy-model-based disturbance observer is proposed. The quadrotor UAV can perform vertical takeoff and landing (VTOL) propelled by four rotors. In addition, the control system of the quadrotor UAV can be decomposed into the attitude, altitude, and position models. However, since the dynamics of the quadrotor UAV is underactuated in which the number of degrees of freedom is higher than the number of actuators, its controller design is not easy. On the other hand, among the methods modeling the nonlinear system, the T-S fuzzy modeling approach has gained significant attention. The T-S fuzzy model control approach represents a nonlinear system as the IF-THEN rules based on the sector nonlinearity concept. The given nonlinear system is expressed as a convex sum of the linear subsystem and membership function that is the weight of each IF-THEN rule. Employing the T-S fuzzy modeling approach, in this thesis, each control system of the quadrotor UAV is represented by the T-S fuzzy model. Next, the external disturbance affecting the quadrotor UAV is estimated by the disturbance observer. Additionally, I derive the tracking error dynamics between each control system and predefined reference model and disturbance estimation error dynamics between the disturbance observer and exogenous system, respectively. The augmented system is obtained by combining the tracking error dynamics and disturbance estimation error dynamics. Based on this system configuration, this thesis proposes the disturbance attenuating tracking controller design method of the quadrotor UAV via the T-S fuzzy-model-based disturbance observer with uncertain control gain. The stabilization condition of the augmented system is derived in terms of the linear matrix inequality (LMI) by using the fuzzy Lyapunov function. Furthermore, I consider the perturbation of the control gain matrix generated by the aging of the actual controller. Finally, through the simulation example, The effectiveness of the Hโˆž tracking performance and disturbance attenuation performance of the proposed control design method is validated.|๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” Takagi-Sugeno (T-S) ํผ์ง€ ๋ชจ๋ธ ๊ธฐ๋ฐ˜์˜ ์™ธ๋ž€ ๊ด€์ธก๊ธฐ๋ฅผ ํ†ตํ•œ ์ฟผ๋“œ๋กœํ„ฐ์˜ ์™ธ๋ž€ ๊ฐ์‡  ์ถ”์ข… ์ œ์–ด๊ธฐ ์„ค๊ณ„ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฟผ๋“œ๋กœํ„ฐ๋Š” ์ˆ˜์ง ์ด์ฐฉ๋ฅ™์ด ๊ฐ€๋Šฅํ•˜๊ณ  ๋„ค ๊ฐœ์˜ ๋กœํ„ฐ๋กœ ์ถ”์ง„๋ ฅ์„ ์–ป๋Š” ๋ฌด์ธ ํ•ญ๊ณต๊ธฐ์ด๋‹ค. ๋˜ํ•œ, ์ฟผ๋“œ๋กœํ„ฐ๋Š” ์ž์„ธ, ๊ณ ๋„, ์œ„์น˜ ๋ชจ๋ธ๋กœ ์ œ์–ด ์‹œ์Šคํ…œ์„ ์„ธ๋ถ„ํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ž์œ ๋„์— ๋น„ํ•ด ์ œ์–ด ์ž…๋ ฅ์˜ ์ˆ˜๊ฐ€ ์ ์€ underactuated system ์ด๊ธฐ ๋•Œ๋ฌธ์— ์ œ์–ด๊ธฐ ์„ค๊ณ„๊ฐ€ ์–ด๋ ต๋‹ค. ํ•œํŽธ, ๋น„์„ ํ˜• ์‹œ์Šคํ…œ์„ ๋ชจ๋ธ๋งํ•˜๋Š” ๋ฐฉ๋ฒ• ์ค‘์—์„œ T-S ํผ์ง€ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•์ด ์žˆ๋‹ค. T-S ํผ์ง€ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•์€ ์ˆ˜ํ•™์  ๋ชจ๋ธ๋กœ ํ‘œํ˜„๋œ ๋น„์„ ํ˜• ์‹œ์Šคํ…œ์„ IF-THEN ๊ทœ์น™๋“ค๋กœ ํ‘œํ˜„ํ•˜๊ณ , sector nonlinearity ๊ฐœ๋…์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ถ€๋ถ„ ์„ ํ˜• ์‹œ์Šคํ…œ๊ณผ ๊ฐ ๊ทœ์น™์˜ ๊ฐ€์ค‘์น˜์˜ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง„ ์†Œ์†๋„ ํ•จ์ˆ˜๋ฅผ ๋ณผ๋ก ํ•ฉ์œผ๋กœ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐ๊ฐ์˜ ์ฟผ๋“œ๋กœํ„ฐ ์ œ์–ด ์‹œ์Šคํ…œ์„ IF-THEN ๊ทœ์น™์œผ๋กœ ํ‘œํ˜„ํ•˜๊ณ , T-S ํผ์ง€ ๋ชจ๋ธ๋กœ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์™ธ๋ž€ ๊ด€์ธก๊ธฐ๋ฅผ ์„ค๊ณ„ํ•˜์—ฌ ์ฟผ๋“œ๋กœํ„ฐ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์™ธ๋ถ€ ์™ธ๋ž€์„ ์ถ”์ •ํ•œ๋‹ค. ๋˜ํ•œ, ์ฐธ์กฐ ๋ชจ๋ธ์„ ์„ค์ •ํ•˜์—ฌ ์‹œ์Šคํ…œ๊ณผ ์ฐธ์กฐ ๋ชจ๋ธ์˜ ์˜ค์ฐจ ๋™์—ญํ•™๊ณผ ์™ธ๋ž€์„ ์ƒ์„ฑํ•˜๋Š” ์™ธ์ธ์„ฑ ์‹œ์Šคํ…œ๊ณผ ์™ธ๋ž€ ๊ด€์ธก๊ธฐ์˜ ์™ธ๋ž€ ์ถ”์ • ์˜ค์ฐจ ๋™์—ญํ•™์„ ์œ ๋„ํ•˜๋ฉฐ, ์ด๋“ค์„ ํ•˜๋‚˜์˜ ์ฆ๊ฐ€ ์‹œ์Šคํ…œ์œผ๋กœ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” Hโˆž ์ถ”์ข… ์„ฑ๋Šฅ๊ณผ ์™ธ๋ž€ ๊ฐ์‡  ์„ฑ๋Šฅ์„ ๋งŒ์กฑํ•˜๊ณ  ์ ๊ทผ ์•ˆ์ •ํ™”๋ฅผ ๋ณด์žฅํ•˜๋Š” ์•ˆ์ •ํ™” ์กฐ๊ฑด์„ ์œ ๋„ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ํผ์ง€ ๋ฆฌ์•„ํ‘ธ๋…ธํ”„ ํ•จ์ˆ˜ (Lyapunov function)์„ ์‚ฌ์šฉํ•˜๊ณ  ์„ ํ˜• ํ–‰๋ ฌ ๋ถ€๋“ฑ์‹ (linear matrix inequality) ํ˜•ํƒœ๋กœ ์•ˆ์ •ํ™” ์กฐ๊ฑด์„ ์œ ๋„ํ•œ๋‹ค. ๋˜ํ•œ, ์‹ค์ œ ์ œ์–ด๊ธฐ์˜ ๋…ธํ›„ํ™” ๋•Œ๋ฌธ์— ๋ฐœ์ƒํ•˜๋Š” ์ œ์–ด ์ด๋“ ํ–‰๋ ฌ์˜ ์„ญ๋™์„ ๊ณ ๋ คํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์˜ˆ์ œ๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์˜ ์ถ”์ข… ์„ฑ๋Šฅ๊ณผ ์™ธ๋ž€ ์ถ”์ • ์„ฑ๋Šฅ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•œ๋‹ค.1 Introduction 1 2 Preliminaries 5 2.1 Attitude model 7 2.2 Altitude model 10 2.3 Position model 12 2.4 Reference model 15 2.5 Disturbance observer-based controller under the control gain uncertainty 15 2.6 Tracking error dynamics 16 2.7 Exogenous system 18 2.8 Disturbance observer 18 2.9 Augmented system 20 3 Controller design procedure 22 3.1 Design formulation 22 3.2 Stability condition 23 3.3 LMI-based stabilization condition 32 4 Simulation example 38 4.1 System configuration 38 4.2 Controller design 41 4.3 Simulation results 45 5 Conclusions 55Maste
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