State feedback control
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Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain (ë ) and linear inner loop gain (K). Since the sliding surface gain (ë) and linear inner loop gain (K) are adjusted by linear error-based tuning method. In this research new ë and new K are obtained by the previous ë and K multiple gains updating factor(á). The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.
Position Control of Robot Manipulator: Design a Novel SISO Adaptive Sliding M...
The document describes a novel adaptive sliding mode fuzzy PD fuzzy sliding mode control algorithm for position control of robot manipulators. The algorithm uses a single-input single-output fuzzy system to compensate for model uncertainties and eliminate chattering using a linear boundary layer method. It also online tunes the sliding function parameter using adaptation laws. The stability of the closed-loop system is proved mathematically using Lyapunov stability theory. The algorithm is analyzed and evaluated on a 2 degree of freedom robotic manipulator to achieve improved tracking performance compared to conventional sliding mode control approaches.
Hierarchical robust fuzzy sliding mode control for a class of simo under-actu...
The development of the algorithms for single input multi output (SIMO) under-actuated systems with mismatched uncertainties is important. Hierarchical sliding-mode controller (HSMC) has been successfully employed to control SIMO under-actuated systems with mismatched uncertainties in a hierarchical manner with the use of sliding mode control. However, in such a control scheme, the chattering phenomenon is its main disadvantage. To overcome the above disadvantage, in this paper, a new compound control scheme is proposed for SIMO under-actuated based on HSMC and fuzzy logic control (FLC). By using the HSMC approach, a sliding control law is derived so as to guarantee the stability and robustness under various environments. The FLC as the second controller completely removes the chattering signal caused by the sign function in the sliding control law. The results are verified through theoretical proof and simulation software of MATLAB through two systems Pendubot and series double inverted pendulum.
A Survey of Autonomous Driving CommonPractices and Emerging.docx
A Survey of Autonomous Driving: Common
Practices and Emerging Technologies
Ekim Yurtsever∗, Jacob Lambert∗, Alexander Carballo∗, Kazuya Takeda∗†
Abstract—Automated driving systems (ADSs) promise a safe,
comfortable and efficient driving experience. However, fatalities
involving vehicles equipped with ADSs are on the rise. The full
potential of ADSs cannot be realized unless the robustness of
state-of-the-art improved further. This paper discusses unsolved
problems and surveys the technical aspect of automated driving.
Studies regarding present challenges, high-level system architec-
tures, emerging methodologies and core functions: localization,
mapping, perception, planning, and human machine interface,
were thoroughly reviewed. Furthermore, the state-of-the-art was
implemented on our own platform and various algorithms were
compared in a real-world driving setting. The paper concludes
with an overview of available datasets and tools for ADS
development.
I. INTRODUCTION
ACCORDING to a recent technical report by the NationalHighway Traffic Safety Administration (NHTSA), 94%
of road accidents are caused by human errors [1]. Automated
driving systems (ADSs) are being developed with the promise
of preventing accidents, reducing emissions, transporting the
mobility-impaired and reducing driving related stress [2].
Annual social benefits of ADSs are projected to reach nearly
$800 billion by 2050 through congestion mitigation, road ca-
sualty reduction, decreased energy consumption and increased
productivity caused by the reallocation of driving time [3].
Eureka Project PROMETHEUS [4] was carried out in Eu-
rope between 1987-1995, and it was one of the earliest major
automated driving studies. The project led to the development
of VITA II by Daimler-Benz, which succeeded in automat-
ically driving on highways [5]. DARPA Grand Challenge,
organized by the US Department of Defense in 2004, was
the first major automated driving competition where all of the
attendees failed to finish the 150-mile off-road parkour. The
difficulty of the challenge was due to the rule that no human
intervention at any level was allowed during the finals. Another
similar DARPA Grand Challenge was held in 2005. This time
five teams managed to complete the off-road track without any
human interference [6].
Fully automated driving in urban scenes was seen as the
biggest challenge of the field since the earliest attempts.
During DARPA Urban Challenge [7], held in 2007, many
different research groups around the globe tried their ADSs
in a test environment that was modeled after a typical urban
scene. Six teams managed to complete the event. Even though
∗E. Yurtsever, J. Lambert, A. Carballo and K. Takeda are with Nagoya
University, Furo-cho, Nagoya, 464-8603, JAPAN
† K. Takeda is also with Tier4 Inc. Nagoya, JAPAN.
Corresponding author: Ekim Yurtsever, [email protected]
this competition was the biggest and most significant event
up to that time, the test environment lacke ...
A Survey of Autonomous Driving CommonPractices and Emerging.docx
A Survey of Autonomous Driving: Common
Practices and Emerging Technologies
Ekim Yurtsever∗, Jacob Lambert∗, Alexander Carballo∗, Kazuya Takeda∗†
Abstract—Automated driving systems (ADSs) promise a safe,
comfortable and efficient driving experience. However, fatalities
involving vehicles equipped with ADSs are on the rise. The full
potential of ADSs cannot be realized unless the robustness of
state-of-the-art improved further. This paper discusses unsolved
problems and surveys the technical aspect of automated driving.
Studies regarding present challenges, high-level system architec-
tures, emerging methodologies and core functions: localization,
mapping, perception, planning, and human machine interface,
were thoroughly reviewed. Furthermore, the state-of-the-art was
implemented on our own platform and various algorithms were
compared in a real-world driving setting. The paper concludes
with an overview of available datasets and tools for ADS
development.
I. INTRODUCTION
ACCORDING to a recent technical report by the NationalHighway Traffic Safety Administration (NHTSA), 94%
of road accidents are caused by human errors [1]. Automated
driving systems (ADSs) are being developed with the promise
of preventing accidents, reducing emissions, transporting the
mobility-impaired and reducing driving related stress [2].
Annual social benefits of ADSs are projected to reach nearly
$800 billion by 2050 through congestion mitigation, road ca-
sualty reduction, decreased energy consumption and increased
productivity caused by the reallocation of driving time [3].
Eureka Project PROMETHEUS [4] was carried out in Eu-
rope between 1987-1995, and it was one of the earliest major
automated driving studies. The project led to the development
of VITA II by Daimler-Benz, which succeeded in automat-
ically driving on highways [5]. DARPA Grand Challenge,
organized by the US Department of Defense in 2004, was
the first major automated driving competition where all of the
attendees failed to finish the 150-mile off-road parkour. The
difficulty of the challenge was due to the rule that no human
intervention at any level was allowed during the finals. Another
similar DARPA Grand Challenge was held in 2005. This time
five teams managed to complete the off-road track without any
human interference [6].
Fully automated driving in urban scenes was seen as the
biggest challenge of the field since the earliest attempts.
During DARPA Urban Challenge [7], held in 2007, many
different research groups around the globe tried their ADSs
in a test environment that was modeled after a typical urban
scene. Six teams managed to complete the event. Even though
∗E. Yurtsever, J. Lambert, A. Carballo and K. Takeda are with Nagoya
University, Furo-cho, Nagoya, 464-8603, JAPAN
† K. Takeda is also with Tier4 Inc. Nagoya, JAPAN.
Corresponding author: Ekim Yurtsever, [email protected]
this competition was the biggest and most significant event
up to that time, the test environment lacke.
Design Novel Lookup Table Changed Auto Tuning FSMC: Applied to Robot Manipulator
Refer to this paper, design lookup table changed adaptive fuzzy sliding mode controller with minimum rule base and good response in presence of structure and unstructured uncertainty is presented. However sliding mode controller is one of the robust nonlinear controllers but when this controller is applied to robot manipulator with highly nonlinear and uncertain dynamic function; caused to be challenged in control. Sliding mode controller in presence of uncertainty has two most important drawbacks; chattering and nonlinear equivalent part which proposed method is solved these challenges with look up table change methodology. This method is based on self tuning methodology therefore artificial intelligence (e.g., fuzzy logic method) is played important role to design proposed method. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.00017).
2-DOF Block Pole Placement Control Application To: Have-DASH-IIBITT Missile
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
Similar to State feedback control (20)
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A Survey of Autonomous Driving CommonPractices and Emerging.docx
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Design Novel Lookup Table Changed Auto Tuning FSMC: Applied to Robot Manipulator
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
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6:个人职称评审加20分
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State feedback control
- 1. Ministry of High Education and Scientific Research Sana'a University Faculty of Engineering Mechatronics Department 5th Year MSD II Supervised byProf.Hatem Al-Doais Prepared byBassam Alghram 208-2015
- 2. Objectives: • LTI systems (Linear Time-Invariant). • Linearization. • Stability. • Output feedback. • state feedback. • Pole placement. • Controller and Controllability. • Observer and Observability. • The separation principle.
- 3. LTI Systems
- 4. The car model is:
- 10. 𝜃
- 11. 𝜃
- 12. We can’t control the motion of the robot in Y-direction
- 19. 𝜃 𝜃
- 28. The {K} matrix tells us how much The system react with each individual state of the system. Notice
- 30. =
- 41. If the rank is less than the number of the states then, the system isn’t controllable
- 49. X1 X2 Blue is the real state (X) Red is the estimated state (X hat )
- 58. Further Topics Related to the K matrix Design Related to the L matrix Design
- 59. References: - MagnusEgerstedt,ControlofMobileRobots,GeorgiaInstuteofTechnology. ( Supported by Coursera platform )
Editor's Notes
- Quadcopter example shall be discussed.