PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

Modeling and Robust Attitude Controller Design for a Small Size Helicopter

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 algorithm and robustness of its resultant controller.Comment: 6 page

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Automatic Landing of a Rotary-Wing UAV in Rough Seas

Rotary-wing unmanned aerial vehicles (RUAVs) have created extensive interest in the past few decades due to their unique manoeuverability and because of their suitability in a variety of flight missions ranging from traffic inspection to surveillance and reconnaissance. The ability of a RUAV to operate from a ship in the presence of adverse winds and deck motion could greatly extend its applications in both military and civilian roles. This requires the design of a flight control system to achieve safe and reliable automatic landings. Although ground-based landings in various scenarios have been investigated and some satisfactory flight test results are obtained, automatic shipboard recovery is still a dangerous and challenging task. Also, the highly coupled and inherently unstable flight dynamics of the helicopter exacerbate the difficulty in designing a flight control system which would enable the RUAV to attenuate the gust effect. This thesis makes both theoretical and technical contributions to the shipboard recovery problem of the RUAV operating in rough seas. The first main contribution involves a novel automatic landing scheme which reduces time, cost and experimental resources in the design and testing of the RUAV/ship landing system. The novelty of the proposed landing system enables the RUAV to track slow-varying mean deck height instead of instantaneous deck motion to reduce vertical oscillations. This is achieved by estimating the mean deck height through extracting dominant modes from the estimated deck displacement using the recursive Prony Analysis procedure. The second main contribution is the design of a flight control system with gust-attenuation and rapid position tracking capabilities. A feedback-feedforward controller has been devised for height stabilization in a windy environment based on the construction of an effective gust estimator. Flight tests have been conducted to verify its performance, and they demonstrate improved gust-attenuation capability in the RUAV. The proposed feedback-feedforward controller can dynamically and synchronously compensate for the gust effect. In addition, a nonlinear H1 controller has been designed for horizontal position tracking which shows rapid position tracking performance and gust-attenuation capability when gusts occur. This thesis also contains a description of technical contributions necessary for a real-time evaluation of the landing system. A high-infedlity simulation framework has been developed with the goal of minimizing the number of iterations required for theoretical analysis, simulation verification and flight validation. The real-time performance of the landing system is assessed in simulations using the C-code, which can be easily transferred to the autopilot for flight tests. All the subsystems are parameterized and can be extended to different RUAV platforms. The integration of helicopter flight dynamics, flapping dynamics, ship motion, gust effect, the flight control system and servo dynamics justifies the reliability of the simulation results. Also, practical constraints are imposed on the simulation to check the robustness of the flight control system. The feasibility of the landing procedure is confimed for the Vario helicopter using real-time ship motion data

Low Speed Flap-bounding in Ornithopters and its Inspiration on the Energy Efficient Flight of Quadrotors

Flap-bounding, a form of intermittent flight, is often exhibited by small birds over their entire range of flight speeds. The purpose of flap-bounding is unclear during low to medium speed (2 - 8 m/s) flight from a mechanical-power perspective: aerodynamic models suggest continuous flapping would require less power output and lower cost of transport. This thesis works towards the understanding of the advantages of flap-bounding and tries to employ the underlining principle to design quadrotor maneuver to improve power efficiency. To explore the functional significance of flap-bounding at low speeds, I measured body trajectory and kinematics of wings and tail of zebra finch (Taeniopygia guttata, N=2) during flights in a laboratory between two perches. The flights consist of three phases: initial, descending and ascending. Zebra finch first accelerated using continuous flapping, then descended, featuring intermittent bounds. The flight was completed by ascending using nearly-continuous flapping. When exiting bounds in descending phase, they achieved higher than pre-bound forward velocity by swinging body forward similar to pendulum motion with conserved mechanical energy. Takeoffs of black-capped chickadees (Poecile atricapillus, N=3) in the wild was recorded and I found similar kinematics. Our modeling of power output indicates finch achieves higher velocity (13%) with lower cost of transport (9%) when descending, compared with continuous flapping in previously-studied pigeons. To apply the findings to the design of quadrotor motion, a mimicking maneuver was developed that consisted of five phases: projectile drop, drop transition, pendulum swing, rise transition and projectile rise. The quadrotor outputs small amount (4 N) of thrust during projectile drop phase and ramps up the thrust while increasing body pitch angle during the drop transition phase until the thrust enables the quadrotor to advance in pendulum-like motion in the pendulum swing phase. As the quadrotor reaches the symmetric point with respect to the vertical axis of the pendulum motion, it engages in reducing the thrust and pitch angle during the rise transition phase until the thrust is lowered to the same level as the beginning of the maneuver and the body angle of attack minimized (0.2 deg) in the projectile rise phase. The trajectory of the maneuver was optimized to yield minimum cost of transport. The quadrotor moves forward by tracking the cycle of the optimized trajectory repeatedly. Due to the aggressive nature of the maneuver, we developed new algorithms using onboard sensors to determine the estimated position and attitude. By employing nonlinear controller, we showed that cost of transport of the flap-bounding inspired maneuver is lower (28%) than conventional constant forward flight, which makes it the preferable strategy in high speed flight (≥15 m/s)

Development of advanced autonomous learning algorithms for nonlinear system identification and control

Identification of nonlinear dynamical systems, data stream analysis, etc. is usually handled by autonomous learning algorithms like evolving fuzzy and evolving neuro-fuzzy systems (ENFSs). They are characterized by the single-pass learning mode and open structure-property. Such features enable their effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of ENFSs lies in its design principle, which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of the type-2 fuzzy system. From this literature gap, a novel ENFS, namely Parsimonious Learning Machine (PALM) is proposed in this thesis. To reduce the number of network parameters significantly, PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering, where it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of them characterize a fully dynamic rule-based system. Thus, it is capable of automatically generating, merging, and tuning the hyperplane-based fuzzy rule in a single-pass manner. Moreover, an extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of both PALM and rPALM have been evaluated through numerical study with data streams and to identify nonlinear unmanned aerial vehicle system. The proposed models showcase significant improvements in terms of computational complexity and the number of required parameters against several renowned ENFSs while attaining comparable and often better predictive accuracy. The ENFSs have also been utilized to develop three autonomous intelligent controllers (AICons) in this thesis. They are namely Generic (G) controller, Parsimonious controller (PAC), and Reduced Parsimonious Controller (RedPAC). All these controllers start operating from scratch with an empty set of fuzzy rules, and no offline training is required. To cope with the dynamic behavior of the plant, these controllers can add, merge or prune the rules on demand. Among three AICons, the G-controller is built by utilizing an advanced incremental learning machine, namely Generic Evolving Neuro-Fuzzy Inference System. The integration of generalized adaptive resonance theory provides a compact structure of the G-controller. Consequently, the faster evolution of structure is witnessed, which lowers its computational cost. Another AICon namely, PAC is rooted with PALM's architecture. Since PALM has a dependency on user-defined thresholds to adapt the structure, these thresholds are replaced with the concept of bias- variance trade-off in PAC. In RedPAC, the network parameters have further reduced in contrast with PALM-based PAC, where the number of consequent parameters has reduced to one parameter per rule. These AICons work with very minor expert domain knowledge and developed by incorporating the sliding mode control technique. In G-controller and RedPAC, the control law and adaptation laws for the consequent parameters are derived from the SMC algorithm to establish a stable closed-loop system, where the stability of these controllers are guaranteed by using the Lyapunov function and the uniform asymptotic convergence of tracking error to zero is witnessed through the implication of an auxiliary robustifying control term. While using PAC, the boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Their efficacy is evaluated by observing various trajectory tracking performance of unmanned aerial vehicles. The accuracy of these controllers is comparable or better than the benchmark controllers where the proposed controllers incur significantly fewer parameters to attain similar or better tracking performance