Issues of Designing a Model Adaptive Controller Without a State Observer

It can be challenging to develop a controller using conventional techniques for a plant with a linear or nonlinear dynamical system or model uncertainty. Model adaptive control is a new alternative to classical control techniques and a simple way to update controller parameters. Because model reference adaptive control is unable to anticipate the state in real time if the state observer is not designed with, we will review some of the most major disadvantages of the most commonly used design techniques without state observer in this work

The Effectiveness of Pole Placement Method in Control System Design for an Autonomous Helicopter Model in Hovering Flight

This paper presents the results of attitude, velocity, heave and yaw controller design for an autonomous model scaled helicopter using identified model of vehicle dynamic from parameterized state-space model with quasi-steady attitude dynamic approximation (6 Degree of Freedom model). Multivariable state-space control methodology such as pole placement was used to design the linear state-space feedback for the stabilization of helicopter because of its simple controller architecture. The design specification for controller design was selected according to Military Handling Qualities Specification ADS-33C. Results indicate that acceptable controller can be designed using pole placement method with quasi-steady attitude approximation and it has been shown that the controller design was compliance with design criteria of hover requirement in ADS-33C

Obstacle detection technique using multi sensor integration for small unmanned aerial vehicle

Achieving a robust obstacle detection system for small UAV is very challenging. Due to size and weight constraints, very limited detection sensors can be equipped in the system. Prior works focused on a single sensing device which is either camera or range sensors based. However, these sensors have their own advantages and disadvantages in detecting the appearance of the obstacles. In this paper, combination of both sensors based is proposed for a small UAV obstacle detection system. A small Lidar sensor is used as the initial detector and queue for image capturing by the camera. Next, SURF algorithm is applied to find the obstacle sizes estimation by searching the connecting feature points in the image frame. Finally, safe avoidance path for UAV is determined through the exterior feature points from the estimated width of the obstacle. The proposed method was evaluated by conducting experiments in real time with indoor environment. In the experiment conducted, we successfully detect and determine a safe avoidance path for the UAV on 6 different sizes and textures of the obstacles including textureless obstacle

Sudden Obstacle Appearance Detection by Analyzing Flow Field Vector for Small-Sized UAV

Achieving a reliable obstacle detection and avoidance system that can provide an effective safe avoidance path for small unmanned aerial vehicle (UAV) is very challenging due to its physical size and weight constraints. Prior works tend to employ the vision based-sensor as the main detection sensor but resulting to high dependency on texture appearance while not having a distance sensing capabilities. The previous system only focused on the detection of the static frontal obstacle without observing the environment which may have moving obstacles. On the other hand, most of the wide spectrum range sensors are heavy and expensive hence not suitable for small UAV. In this work, integration of different based sensors was proposed for a small UAV in detecting unpredictable obstacle appearance situation. The detection of the obstacle is accomplished by analysing the flow field vectors in the image frames sequence. The proposed system was evaluated by conducting the experiments in a real environment which consisted of different configuration of the obstacles. The results from the experiment show that the success rate for detecting unpredictable obstacle appearance is high which is 70% and above. Even though some of the introduced obstacles are considered to have poor texture appearances on their surface, the proposed obstacle detection system was still able to detect the correct appearance movement of the obstacles by detecting the edges

Object detection technique for small unmanned aerial vehicle

Obstacle detection and avoidance is desirable for UAVs especially lightweight micro aerial vehicles and is challenging problem since it has payload constraints, therefore only limited sensor can be attached the vehicle. Usually the sensors incorporated in the system is either type vision based (monocular or stereo camera) or Laser based. However, each of the sensor has its own advantage and disadvantage, thus we built the obstacle detection and avoidance system based multi sensor (monocular sensor and LIDAR) integration. On top of that, we also combine SURF algorithm with Harris corner detector to determine the approximate size of the obstacles. In the initial experiment conducted, we successfully detect and determine the size of the obstacles with 3 different obstacles. The differences of length between real obstacles and our algorithm are considered acceptable which is about -0.4 to 3.6

A Comparative Study on Longitudinal Dynamics Stability Between Two Aircraft Models

The present work presents a comparative study on the longitudinal dynamic’s stability behavior for two aircraft models, namely the Learjet 24 and the Cessna 182. The longitudinal flight dynamics behaviors are evaluated by introducing a disturbance to the elevator. This device uses a single doublet impulse as well as multiple doublet impulses. The governing equation of longitudinal flight motion, which was derived based on a small perturbation theory and a linearized process by dropping the second order and above to the disturbance quantities, allowed one to formulate the governing equation of flight motion in the form of an equation known as the longitudinal equation of flight motion. This equation describes the flight behavior of an aircraft and can be expressed in the disturbance quantity as translational velocity in the x-direction u, angle of attack , and pitch angle . The implementation in the case of the Cessna 182 and the Learjet 24, where the Cessna 182 uses a single doublet impulse or a multiple doublet impulse, demonstrates that the aircraft response in these three variable states is better than that of the Learjet 24

Wind as a sustainable alternative energy source in Malaysia - a review

Wind energy is being considered all over the world due to the clean characteristics that it possesses and prevalent virtually everywhere in the world. With the current existing technology, a wind turbine could only harness a small portion of energy from the available wind. The amount of harnessed energy could be significantly decreased, if proper wind energy assessments (i.e. micrositing, geographical condition, wind regime characteristics, etc.) were not performed carefully and effectively. As previous studies have shown that, most of the projects which seem to be unfeasible were due to environmental problems such as appropriate site selection, and not technological problems such as wind turbine design. This study presents and discusses the main factors to be considered when undertaking wind energy project in any potential site, so that significant amount of energy could be harvested

Twin Delayed Deep Deterministic Policy Gradient-Based Target Tracking for Unmanned Aerial Vehicle with Achievement Rewarding and Multistage Training

Target tracking using an unmanned aerial vehicle (UAV) is a challenging robotic problem. It requires handling a high level of nonlinearity and dynamics. Model-free control effectively handles the uncertain nature of the problem, and reinforcement learning (RL)-based approaches are a good candidate for solving this problem. In this article, the Twin Delayed Deep Deterministic Policy Gradient Algorithm (TD3), as recent and composite architecture of RL, was explored as a tracking agent for the UAV-based target tracking problem. Several improvements on the original TD3 were also performed. First, the proportional-differential controller was used to boost the exploration of the TD3 in training. Second, a novel reward formulation for the UAV-based target tracking enabled a careful combination of the various dynamic variables in the reward functions. This was accomplished by incorporating two exponential functions to limit the effect of velocity and acceleration to prevent the deformation in the policy function approximation. In addition, the concept of multistage training based on the dynamic variables was proposed as an opposing concept to one-stage combinatory training. Third, an enhancement of the rewarding function by including piecewise decomposition was used to enable more stable learning behaviour of the policy and move out from the linear reward to the achievement formula. The training was conducted based on fixed target tracking followed by moving target tracking. The flight testing was conducted based on three types of target trajectories: fixed, square, and blinking. The multistage training achieved the best performance with both exponential and achievement rewarding for the fixed trained agent with the fixed and square moving target and for the combined agent with both exponential and achievement rewarding for a fixed trained agent in the case of a blinking target. With respect to the traditional proportional differential controller, the maximum error reduction rate is 86%. The developed achievement rewarding and the multistage training opens the door to various applications of RL in target tracking

Arbitrary Lagrangian-Eulerian form of flowfield dependent variation method for fluid-structure interaction application

In this study, Flowfield Dependent Variation (FDV) method is coupled with Arbitrary Lagrangian-Eulerian (ALE) method in order to solve fluid-structure interaction problems. FDV method is a mixed explicit-implicit numerical scheme where its implicitness is determined by several parameters that are dependent on the physical properties of the local flow. The scheme which is called as ALE-FDV method is discretized using finite volume method to give flexibility in dealing with complicated geometries. The formulation itself yields a sparse matrix, which can be solved using any iterative algorithm. Several numerical tests have been conducted and the results are in good agreement with exact and available numerical solutions in the literature

Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

In this paper, we present the performance analysis of a fully tuned neural network trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time identification of a quadcopter. Radial basis function network (RBF) based on system identification can be utilised as an alternative technique for quadcopter modelling. To prevent the neurons and network parameters selection dilemma during trial and error approach, RBF with EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN’s performance is compared with the minimal resource allocating network (MRAN) training for 1000 input-output pair untrained attitude data. The findings show that the EMRAN method generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy