Analysis of Stability, Response and LQR Controller Design of a Small Scale Helicopter Dynamics
Konkuk University（建国大学）
Hardian Reza Dharmayanda, Taesam Kang, Young Jae Lee, Sangkyung Sung
备注：
Uploaded by ICIUS2007 Conference Organizer on behalf of the author(s). 8 pages, 19 figures, 4 tables
发表信息：
Proceedings of the International Conference on Intelligent Unmanned System (ICIUS 2007), Bali, Indonesia, October 24-25, 2007, Paper No. ICIUS2007-C006
This paper presents how to use feedback controller with helicopter dynamics state space model. A simplified analysis is presented for controller design using LQR of small scale helicopters for axial and forward flights. Our approach is simple and gives the basic understanding about how to develop controller for solving the stability of linear helicopter flight dynamics.
The State Key Laboratory of High Performance Complex Manufacturing, Central South University（中南大学高性能复杂制造国家重点实验室）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
This paper addresses the design and application controller for a small-size unmanned aerial vehicle (UAV). In this work, the main objective is to study the modeling and attitude controller design for a small size helicopter. Based on a non-simplified helicopter model, a new robust attitude control law, which is combined with a nonlinear control method and a model-free method, is proposed in this paper. Both wind gust and ground effect phenomena conditions are involved in this experiment and the result on a real helicopter platform demonstrates the effectiveness of the proposed control algorith…
A mini scale helicopter exhibits not only increased sensitivity to control inputs and disturbances, but also higher bandwidth of its dynamics. These properties make model helicopters, as a flying robot, more difficult to control. The dynamics model accuracy will determine the performance of the designed controller. It is attractive in this regards to have a controller that can accommodate the unmodeled dynamics or parameter changes and perform well in such situations. Coefficient Diagram Method (CDM) is chosen as the candidate to synthesize such a controller due to its simplicity and convenien…
Institut Teknologi Bandung（万隆理工学院）；Simundo – a Simulation Technology Company（Simundo仿真技术公司）；Carnegie Mellon Robotics Institute（卡内基梅隆大学机器人研究所）；MIT（麻省理工学院）；UC Berkeley（加州大学伯克利分校）；Georgia Technology Research Institute（佐治亚理工研究院）
A model helicopter is more difficult to control than its full scale counterparts. This is due to its greater sensitivity to control inputs and disturbances as well as higher bandwidth of dynamics. This works is focused on designing practical tracking controller for a small scale helicopter following predefined trajectories. A tracking controller based on optimal control theory is synthesized as part of the development of an autonomous helicopter. Some issues in regards to control constraints are addressed. The weighting between state tracking performance and control power expenditure is analyz…
This paper addresses the design and application controller for a small-size unmanned aerial vehicle (UAV). In this work, the main objective is to study the modeling and attitude controller design for a small size helicopter. Based on a non-simplified helicopter model, a new robust attitude control law, which is combined with a nonlinear control method and a model-free method, is proposed in this paper. Both wind gust and ground effect phenomena conditions are involved in this experiment and the result on a real helicopter platform demonstrates the effectiveness of the proposed control algorith…
Université de Sherbrooke（舍布鲁克大学）；Babol Noshirvani University of Technology（巴博勒诺希尔瓦尼理工大学）
This paper investigates the controller optimization for a helicopter system with three degrees of freedom (3-DOF). To control the system, we combined fuzzy logic with adaptive control theory. The system is extensively nonlinear and highly sensitive to the controller's parameters, making it a real challenge to study these parameters' effect on the controller's performance. Using metaheuristic algorithms for determining these parameters is a promising solution. This paper proposes using a modified particle swarm optimization (MPSO) algorithm to optimize the controller. The algorithm shows a high…
Linear-quadratic regulators (LQR) are a well known and widely used tool in control theory for both linear and nonlinear dynamics. For nonlinear problems, an LQR-based controller is usually only locally viable, thus, raising the problem of estimating the region of attraction (ROA). The need for good ROA estimations becomes especially pressing for underactuated systems, as a failure of controls might lead to unsafe and unrecoverable system states. Known approaches based on optimization or sampling, while working well, might be too slow in time critical applications and are hard to verify formall…
University of Southern California（南加州大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Torque control algorithms which consider robot dynamics and contact constraints are important for creating dynamic behaviors for humanoids. As computational power increases, algorithms tend to also increase in complexity. However, it is not clear how much complexity is really required to create controllers which exhibit good performance. In this paper, we study the capabilities of a simple approach based on contact consistent LQR controllers designed around key poses to control various tasks on a humanoid robot. We present extensive experimental results on a hydraulic, torque controlled humano…
We present a new walking controller based on 3LP, a 3D model of bipedal walking that is composed of three pendulums to simulate falling, swing and torso dynamics. Taking advantage of linear equations and closed-form solutions of 3LP, the proposed controller projects intermediate states of the biped back to the beginning of the phase for which a discrete LQR controller is designed. After the projection, a proper control policy is generated by this LQR controller and used at the intermediate time. The projection controller reacts to disturbances immediately and compared to the discrete LQR contr…
A comprehensive helicopter flight dynamics code is developed based on the UH-60 helicopter and named Texas A\&M University Rotorcraft Analysis Code (TRAC). This is a complete software package, which could perform trim analysis to autonomous flight simulation and the capability to model any helicopter configuration. Different components of the helicopter such as the main rotor, tail rotor, fuselage, vertical tail, and horizontal tail are modeled individually as different modules in the code and integrated to develop a complete UH-60 model. Since the code is developed on a module basis, it can b…
Reinforcement learning is a model-free optimal control method that optimizes a control policy through direct interaction with the environment. For reaching tasks that end in regulation, popular discrete-action methods are not well suited due to chattering in the goal state. We compare three different ways to solve this problem through combining reinforcement learning with classical LQR control. In particular, we introduce a method that integrates LQR control into the action set, allowing generalization and avoiding fixing the computed control in the replay memory if it is based on learned dyna…
Optimal control is often used in robotics for planning a trajectory to achieve some desired behavior, as expressed by the cost function. Most works in optimal control focus on finding a single optimal trajectory, which is then typically tracked by another controller. In this work, we instead consider trajectory distribution as the solution of an optimal control problem, resulting in better tracking performance and a more stable controller. A Gaussian distribution is first obtained from an iterative Linear Quadratic Regulator (iLQR) solver. A short horizon Model Predictive Control (MPC) is then…
This paper aims for a controller that can stabilize a position-controlled humanoid robot when standing still or walking on synthetic grass even when subjected to external disturbances. Two types of controllers are designed and implemented: ankle strategy and stepping strategy. The robot's joints consist of position-controlled servos which can be complicated to model analytically due to nonlinearities and non-measurable parameters, hence the dynamic model of the humanoid robot is acquired using a non-recursive least squares system identification. This model is also used to design a Kalman Filte…
We present a hierarchical whole-body controller leveraging the full rigid body dynamics of the wheeled bipedal robot Ascento. We derive closed-form expressions for the dynamics of its kinematic loops in a way that readily generalizes to more complex systems. The rolling constraint is incorporated using a compact analytic solution based on rotation matrices. The non-minimum phase balancing dynamics are accounted for by including a linear-quadratic regulator as a motion task. Robustness when driving curves is increased by regulating the lean angle as a function of the zero-moment point. The prop…
We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems. We present Risk-Averse Nonlinear Steering RRT* (RANS-RRT*) as an RRT* variant that incorporates nonlinear dynamics by solving a nonlinear program (NLP) and accounts for risk by approximating the state distribution and performing a distributionally robust (DR) collision check to promote safe planning. The generated plan is used as a reference for a low-level tracking controller. We demonstrate three controllers: finite horizon linear quadratic regulator (LQR) with linearized dynamics around the…
University of Pisa（比萨大学）；Research Center “E. Piaggio” of the University of Pisa（比萨大学“E.皮亚乔”研究中心）
In this paper, we aim to improve the robustness of dynamic quadrupedal locomotion through two aspects: 1) fast model predictive foothold planning, and 2) applying LQR to projected inverse dynamic control for robust motion tracking. In our proposed planning and control framework, foothold plans are updated at 400 Hz considering the current robot state and an LQR controller generates optimal feedback gains for motion tracking. The LQR optimal gain matrix with non-zero off-diagonal elements leverages the coupling of dynamics to compensate for system underactuation. Meanwhile, the projected invers…
A Novel Hybrid PID-LQR Controller for Sit-To-Stand Assistance Using a CAD-Integrated Simscape Multibody Lower Limb Exoskeleton
Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）；Khalifa University Center for Autonomous Robotic Systems (KUCARS)（哈利法大学自主机器人系统中心）；Khalifa University（哈利法大学）
To address the operational challenges of in-pipe robots in large pipes of water distribution systems (WDS), in this research, a control algorithm is proposed for our previously designed robot [4]. Our size adaptable robot has an under-actuated modular design that can be used for both leak detection and quality monitoring. First, nonlinear dynamical governing equations of the robot are derived with the definition of two perpendicular planes, and two sets of states are defined for the robot for stabilization and mobilization. For stabilization, we calculated the auxiliary system matrices and des…
Trajectory Tracking Control Design for Autonomous Helicopters with Guaranteed Error Bounds
Institute of Flight Systems, German Aerospace Center (DLR)（飞行系统研究所，德国航天中心）；Institute for Production Technology and Systems, Leuphana University of Lueneburg（生产技术与系统研究所，路比哈大学）
We present a new walking foot-placement controller based on 3LP, a 3D model of bipedal walking that is composed of three pendulums to simulate falling, swing and torso dynamics. Taking advantage of linear equations and closed-form solutions of the 3LP model, our proposed controller projects intermediate states of the biped back to the beginning of the phase for which a discrete LQR controller is designed. After the projection, a proper control policy is generated by this LQR controller and used at the intermediate time. This control paradigm reacts to disturbances immediately and includes rule…
SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping
Artificial Intelligence in Robotics Laboratory (AiR Lab)（人工智能机器人实验室（AiR实验室））；Department of Electrical and Computer Engineering（电气与计算机工程系）；EIVA a/s（EIVA公司）；Automatic Control Group (RAT)（自动控制组（RAT））；Department of Electrical Engineering and Information Technology（电气工程与信息科技系）
Birla Institute of Technology and Science – Pilani（贝拉理工学院皮尔尼分校）
This paper aims to design an optimal stability controller for a point to point trajectory tracking 3 degree of freedom articulated manipulator. The DH convention is used to obtain the forward and inverse kinematics of the manipulator. The manipulator dynamics are formulated using the Lagrange Euler method to obtain a nonlinear system. The complicated nonlinear equations obtained are then linearized in order to implement the optimal LQR. The simulations are performed in MATLAB and Simulink and the optimal controllers performance is tested for various conditions and the results are presented. Th…
A Data-Driven Algorithm for Model-Free Control Synthesis
Department of Electrical and Computer Engineering, Auburn University（电气与计算机工程系，阿伯杜大学）；Physics and Computational Sciences Division, Naval Air Warfare Center Weapons Division（物理与计算科学分部，海军航空武器中心武器分部）
Toyota Research Institute（丰田研究院）；Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）
Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement learning algorithms have been highly effective in synthesizing neural-network controllers for a wide range of tasks. However, despite this empirical success, these controllers still lack theoretical guarantees on their performance, such as Lyapunov stability (i.e., all trajectories of the closed-loop system are guaranteed to converge to a goal state under the control policy). This is in stark contrast to traditional model-based controller design, where principled approaches (like LQR) can synthesize stabl…
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）
We propose a framework for synthesizing a feedback control policy that maximizes the region of attraction (ROA) of a closed-loop nonlinear dynamical system. Our synthesis technique relies on stochastic optimization, which involves computation of an objective function capturing the ROA for a feedback control law. We employ a machine learning technique based on deep neural network to estimate the ROA for a given feedback controller. Overall, our technique is capable of synthesizing a controller co-optimizing traditional control objectives like LQR cost together with ROA. We demonstrate the effic…
Robust Control Synthesis and Verification for Wire-Borne Underactuated Brachiating Robots Using Sum-of-Squares Optimization
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；Georgia Tech Research Institute（佐治亚理工学院研究院）；Woodruff School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院伍德拉夫机械工程学院）；Guggenheim School of Aerospace Engineering, Georgia Institute of Technology（佐治亚理工学院古根海姆航空航天工程学院）
