RANCANG SISTEM PENGENDALIAN SELF BALANCING PLANT MENGGUNAKAN DUAL MOTOR PROPELLER BERBASIS ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Abstrak Pesawat tanpa awak atau UAV (Unmanned Aerial Vehicle) telah berkembang dengan pesat di berbagai bidang. Sistem tak berawak adalah paltform otonom yang dapat dengan mudah diprogram untuk menjalankan misi dengan atau tanpa campur tangan pilot. Salah satu jenis UAV berdasarkan penggeraknya yang digunakan dalam penelitian ini adalah UAV multirotor di mana sistem penggerak terdiri dari dua buah motor beserta propeller yang biasa disebut dengan dual motors atau twin rotors. Sehubungan dengan ketahanan mekanik dan bahan bakar, kemampuan melayang, dan kegunaannya yang dapat digunakan di dalam ruangan maka UAV jenis ini memiliki peranan yang cukup penting dalam penelitian stabilisasi UAV apabila terdapat beban berlebih, untuk menghinadari kecelakaan di udara maupun untuk mencapai tingkat presisi dan akurasi posisi UAV dengan tepat. Pengontrolan stabilisasi dual motor diterapkan dalam kajian penelitian ini dengan mengontrol kecepatan brushless DC motor agar dapat menyetimbangkan posisi UAV itu sendiri. Metode yang diterapkan adalah metode simulasi plant dengan data sekunder sebagai acuan pengaturan parameter komponen menggunakan aplikasi Matlab 2018a. Pengujian plant secara simulasi menghasilkan nilai ANFIS yang cukup baik dengan nilai waktu naik (tr) = 4.145 s, waktu tunak (ts) = 7.5439 s, simpangan maksimum (Mp) = 0.489 %, dan Ess = 0.000741 %. Kata Kunci: ANFIS, dual motor, self balancing

Gain scheduled attitude control of fixed-wing UAV with automatic controller tuning

Fixed-wing unmanned aerial vehicles (UAVs) have become increasingly important in military, civil, and scientific sectors. Because of the existing nonlinearities, effective control this type of UAV remains a challenge. This paper proposes a gain scheduled proportional-integral derivative (PID) control system for fixed-wing UAVs where a family of PID cascade control systems is designed for several operating conditions of airspeed. This is done using an automatic tuning algorithm, where the controllers are automatically selected by deploying an airspeed sensor positioned ahead of the aircraft. Furthermore, the actual gain scheduling is carried out by forming an interpolation between the family members of the linear closed-loop system, which ensures a smooth transition from one operating point to another. Experimental results are conducted in a wind tunnel to show the successful design and implementation of the gain scheduled control system for the fixed-wing UAV and the significant performance improvement over a linear control system without controller adaptation

Nonlinear Controller Design for UAVs with Time-Varying Aerodynamic Uncertainties

Unmanned Aerial Vehicles (UAVs) are here and they are here to stay. Unmanned Aviation has expanded significantly in recent years and research and development in the field of navigation and control have advanced beyond expectations. UAVs are currently being used for defense programs around the world but the range of applications is expected to grow in the near future, with civilian applications such as environmental and aerial monitoring, aerial surveillance and homeland security being some representative examples. Conventional and commercially available small-scale UAVs have limited utilization and applicability to executing specific short-duration missions because of limitations in size, payload, power supply and endurance. This fact has already marked the dawn of a new era of more powerful and versatile UAVs (e.g. morphing aircraft), able to perform a variety of missions. This dissertation presents a novel, comprehensive, step-by-step, nonlinear controller design framework for new generation, non-conventional UAVs with time-varying aerodynamic characteristics during flight. Controller design for such UAVs is a challenging task mainly due to uncertain aerodynamic parameters in the UAV mathematical model. This challenge is tackled by using and implementing μ-analysis and additive uncertainty weighting functions. The technique described herein can be generalized and applied to the class of non-conventional UAVs, seeking to address uncertainty challenges regarding the aircraft\u27s aerodynamic coefficients

Multi-agent Collision Avoidance Using Interval Analysis and Symbolic Modelling with its Application to the Novel Polycopter

Coordination is fundamental component of autonomy when a system is defined by multiple mobile agents. For unmanned aerial systems (UAS), challenges originate from their low-level systems, such as their flight dynamics, which are often complex. The thesis begins by examining these low-level dynamics in an analysis of several well known UAS using a novel symbolic component-based framework. It is shown how this approach is used effectively to define key model and performance properties necessary of UAS trajectory control. This is demonstrated initially under the context of linear quadratic regulation (LQR) and model predictive control (MPC) of a quadcopter. The symbolic framework is later extended in the proposal of a novel UAS platform, referred to as the ``Polycopter" for its morphing nature. This dual-tilt axis system has unique authority over is thrust vector, in addition to an ability to actively augment its stability and aerodynamic characteristics. This presents several opportunities in exploitative control design. With an approach to low-level UAS modelling and control proposed, the focus of the thesis shifts to investigate the challenges associated with local trajectory generation for the purpose of multi-agent collision avoidance. This begins with a novel survey of the state-of-the-art geometric approaches with respect to performance, scalability and tolerance to uncertainty. From this survey, the interval avoidance (IA) method is proposed, to incorporate trajectory uncertainty in the geometric derivation of escape trajectories. The method is shown to be more effective in ensuring safe separation in several of the presented conditions, however performance is shown to deteriorate in denser conflicts. Finally, it is shown how by re-framing the IA problem, three dimensional (3D) collision avoidance is achieved. The novel 3D IA method is shown to out perform the original method in three conflict cases by maintaining separation under the effects of uncertainty and in scenarios with multiple obstacles. The performance, scalability and uncertainty tolerance of each presented method is then examined in a set of scenarios resembling typical coordinated UAS operations in an exhaustive Monte-Carlo analysis