DESIGN AND OPTIMIZATION OF BACKSTEPPING CONTROLLER FOR AN UNDERACTUATED AUTONOMOUS QUADROTOR UNMANNED AERIAL VEHICLE

The development of a high performance controller for a quadrotor unmanned aerial vehicle (UAV) is a challenging issue since a quadrotor is an underactuated and a highly unstable nonlinear system. In this paper, the contribution is focused on the design and optimization of a controller for an autonomous quadrotor UAV. Firstly, the dynamic model of the aerial vehicle is mathematically formulated. Then, an optimal backstepping controller (OBC) is proposed. Conventionally, control parameters of a backstepping controller (BC) are often chosen arbitrarily. To this end, it is necessary to invoke a well-established optimization algorithm in order to find the best parameters. Here, the particle swarm optimization (PSO) is utilized as a new key idea to determine the optimal values of the BC parameters. In the algorithm, the control parameters are computed by minimizing the fitness function defined by using the integral absolute error (IAE) performance index. Since the control law is derived based on the Lyapunov theorem, the asymptotical stability of the system can be guaranteed. Finally, the efficiency of the proposed OBC is illustrated by implementing several simulation experiments

Dynamics Modeling and Control of a Quadrotor with Swing Load

Nowadays, aerial robots or Unmanned Aerial Vehicles (UAV) have many applications in civilian and military fields. For example, of these applications is aerial monitoring, picking loads and moving them by different grippers. In this research, a quadrotor with a cable-suspended load with eight degrees of freedom is considered. The purpose is to control the position and attitude of the quadrotor on a desired trajectory in order to move the considered load with constant length of cable. So, the purpose of this research is proposing and designing an antiswing control algorithm for the suspended load. To this end, control and stabilization of the quadrotor are necessary for designing the antiswing controller. Furthermore, this paper is divided into two parts. In the first part, dynamics model is developed using Newton-Euler formulation, and obtained equations are verified in comparison with Lagrange approach. Consequently, a nonlinear control strategy based on dynamic model is used in order to control the position and attitude of the quadrotor. The performance of this proposed controller is evaluated by nonlinear simulations and, finally, the results demonstrate the effectiveness of the control strategy for the quadrotor with suspended load in various maneuvers

Robust Control Methods for Nonlinear Systems with Uncertain Dynamics and Unknown Control Direction

Robust nonlinear control design strategies using sliding mode control (SMC) and integral SMC (ISMC) are developed, which are capable of achieving reliable and accurate tracking control for systems containing dynamic uncertainty, unmodeled disturbances, and actuator anomalies that result in an unknown and time-varying control direction. In order to ease readability of this dissertation, detailed explanations of the relevant mathematical tools is provided, including stability denitions, Lyapunov-based stability analysis methods, SMC and ISMC fundamentals, and other basic nonlinear control tools. The contributions of the dissertation are three novel control algorithms for three different classes of nonlinear systems: single-input multipleoutput (SIMO) systems, systems with model uncertainty and bounded disturbances, and systems with unknown control direction. Control design for SIMO systems is challenging due to the fact that such systems have fewer actuators than degrees of freedom to control (i.e., they are underactuated systems). While traditional nonlinear control methods can be utilized to design controllers for certain classes of cascaded underactuated systems, more advanced methods are required to develop controllers for parallel systems, which are not in a cascade structure. A novel control technique is proposed in this dissertation, which is shown to achieve asymptotic tracking for dual parallel systems, where a single scalar control input directly affects two subsystems. The result is achieved through an innovative sequential control design algorithm, whereby one of the subsystems is indirectly stabilized via the desired state trajectory that is commanded to the other subsystem. The SIMO system under consideration does not contain uncertainty or disturbances. In dealing with systems containing uncertainty in the dynamic model, a particularly challenging situation occurs when uncertainty exists in the input-multiplicative gain matrix. Moreover, special consideration is required in control design for systems that also include unknown bounded disturbances. To cope with these challenges, a robust continuous controller is developed using an ISMC technique, which achieves asymptotic trajectory tracking for systems with unknown bounded disturbances, while simultaneously compensating for parametric uncertainty in the input gain matrix. The ISMC design is rigorously proven to achieve asymptotic trajectory tracking for a quadrotor system and a synthetic jet actuator (SJA)-based aircraft system. In the ISMC designs, it is assumed that the signs in the uncertain input-multiplicative gain matrix (i.e., the actuator control directions) are known. A much more challenging scenario is encountered in designing controllers for classes of systems, where the uncertainty in the input gain matrix is extreme enough to result in an a priori-unknown control direction. Such a scenario can result when dealing with highly inaccurate dynamic models, unmodeled parameter variations, actuator anomalies, unknown external or internal disturbances, and/or other adversarial operating conditions. To address this challenge, a SMCbased self-recongurable control algorithm is presented, which automatically adjusts for unknown control direction via periodic switching between sliding manifolds that ultimately forces the state to a converging manifold. Rigorous mathematical analyses are presented to prove the theoretical results, and simulation results are provided to demonstrate the effectiveness of the three proposed control algorithms