Flight control design for small-scale helicopter using disturbance observer based backstepping

Flight control design for small-scale helicopter using disturbance observer based backsteppin

Optimal control of a helicopter unmanned aerial vehicle (UAV)

This thesis addresses optimal control of a helicopter unmanned aerial vehicle (UAV). Helicopter UAVs may be widely used for both military and civilian operations. Because these helicopters are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This thesis presents an optimal controller design via both state and output feedback for trajectory tracking of a helicopter UAV using a neural network (NN). The state and output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers while the output feedback approach uses an observer in addition to these controllers. The online approximator-based dynamic controller learns the Hamilton-Jacobi-Bellman (HJB) equation in continuous time and calculates the corresponding optimal control input to minimize the HJB equation forward-in-time. Optimal tracking is accomplished with a single NN utilized for cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking. A description of the hardware for confirming the theoretical approach, and a discussion of material pertaining to the algorithms used and methods employed specific to the hardware implementation is also included. Additional attention is devoted to challenges in implementation as well as to opportunities for further research in this field. This thesis is presented in the form of two papers --Abstract, page iv

Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness

Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e., high-speed and high-acceleration) maneuvers have attracted significant attention in the past few years. This paper focuses on accurate tracking of aggressive quadcopter trajectories. We propose a novel control law for tracking of position and yaw angle and their derivatives of up to fourth order, specifically, velocity, acceleration, jerk, and snap along with yaw rate and yaw acceleration. Jerk and snap are tracked using feedforward inputs for angular rate and angular acceleration based on the differential flatness of the quadcopter dynamics. Snap tracking requires direct control of body torque, which we achieve using closed-loop motor speed control based on measurements from optical encoders attached to the motors. The controller utilizes incremental nonlinear dynamic inversion (INDI) for robust tracking of linear and angular accelerations despite external disturbances, such as aerodynamic drag forces. Hence, prior modeling of aerodynamic effects is not required. We rigorously analyze the proposed control law through response analysis, and we demonstrate it in experiments. The controller enables a quadcopter UAV to track complex 3D trajectories, reaching speeds up to 12.9 m/s and accelerations up to 2.1g, while keeping the root-mean-square tracking error down to 6.6 cm, in a flight volume that is roughly 18 m by 7 m and 3 m tall. We also demonstrate the robustness of the controller by attaching a drag plate to the UAV in flight tests and by pulling on the UAV with a rope during hover.Comment: To be published in IEEE Transactions on Control Systems Technology. Revision: new set of experiments at increased speed (up to 12.9 m/s), updated controller design using quaternion representation, new video available at https://youtu.be/K15lNBAKDC

Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

DESIGN OF ADAPTIVE BACKSTEPPING WITH GRAVITATIONAL SEARCH ALGORITHM FOR NONLINEAR SYSTEM

Adaptive backstepping controller is designed for tracking purpose of nonlinear system with unknown parameter is injected to it. Gravitational search algorithm (GSA) is integrated with the designed controller in order to automatically tune its control parameters and adaptation gain since the tracking performance of the controller relies on these parameters. Performance evaluation is observed based on the tracking output and the tracking error between reference input and the system’s output. The effectiveness of the adaptive backstepping controller is verified by looking at the lowest amount value of Sum of Squared Error (SSE) attained from the simulation process. The results show that the system’s output follow the reference input given with remarkably small tracking error