PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal
24 Sep 2015-pp 128-133
TL;DR: Camera gimbal control is designed which controls the on board camera position used in UAV for various applications such as target tracking, Surveillance, Aerial photography, autonomous navigation and so on.
Abstract: In this paper, camera gimbal control is designed which controls the on board camera position used in UAV for various applications such as target tracking, Surveillance, Aerial photography, autonomous navigation and so on. Traditional tracking systems are heavy and large to mount on small airframes. Gimbal with camera replaces traditional tracking systems and used to capture aerial photography without video noise and vibrations. So, the gimbal trajectory planning and its motion control are necessary. The controlling of camera gimbal is designed using different controlling techniques which respond quickly without excitation of damping flexibility. In order to develop the control, kinematics is derived using different robotics techniques. In this paper PID controller is designed to control camera position using gimbal mechanism. PID control is the popular controller used in industries for its effectiveness, simplicity of design and its feasibility. PID consists of three tuning parameters which can be tuned using different techniques. Manual tuning is not preferred since it is time consuming, tedious and leads to poor performance. Here, traditional tuning methods and evolutionary algorithms/bio-inspired algorithms are used to tune PID parameters. PSO is the evolutionary algorithm used because of its stable convergence, dynamic and static performance, good computational efficiency due to which system performance with minimum errors can be achieved. In this paper, performance of system with conventional PID and PSO tuned PID are compared and optimum solution is implemented
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TL;DR: A thorough review of state-of-the-art and classical strategies for PID controller parameters tuning using metaheuristic algorithms can be found in this article , where the primary objectives of PID control parameters are to achieve minimal overshoot in steady state response and lesser settling time.
62 citations
18 May 2020
TL;DR: In this paper, a review of potential control algorithms of the current researches in the field of the quadrotor flight controller is presented, and a comparison has been made to provide an overview of the advantages and disadvantages of the mentioned methods.
Abstract: The development of unmanned aerial vehicles (UAVs) has become a revolution in the fields of data collection, surveying, monitoring, and tracking objects in the field. Many control and navigation algorithms are experimented and deployed for UAVs, especially quadrotors. Recent numerous approaches are geared towards reducing the influence of external disturbances to enhance the performance of UAVs. Nevertheless, designing cutting-edge controllers following the requirements of the applications is still a huge challenge. Based on the operating characteristics and movement principle of a quadrotor, this work reviews potential control algorithms of the current researches in the field of the quadrotor flight controller. Besides, a comparison has been made to provide an overview of the advantages and disadvantages of the mentioned methods. At last, the challenges and future directions of the quadrotor flight controller are suggested. Received on 09 April 2020; accepted on 15 May 2020; published on 18 May 2020
22 citations
Cites methods from "PSO tuned PID controller for contro..."
...PSO methods help decrease the error between the desired value and real value to achieve stable convergence [21], only 18....
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TL;DR: A UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning.
Abstract: The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.
15 citations
Cites background from "PSO tuned PID controller for contro..."
...Speed Coordinate Systems The origin is taken at the center of gravity of the aircraft, and the axis O X is in the same direction as the flight speed V; the axis O Z is located on the vertical axis O X of the plane of symmetry of the aircraft, pointing to the belly; O Y is perpendicular to the plane X O Z , pointing to the right [24]....
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01 Jan 2019
TL;DR: This chapter focuses on four intelligent control techniques, which are fuzzy logic control, neural networks, metaheuristics control tuning, and multi-agent systems.
Abstract: Control engineering is the engineering discipline that refers to the use of automatic control. This discipline has been intensively enlarging over the past decades due to technological advances and technology affordability. Nowadays, almost all engineering activities exploit automatic control. Therefore, this chapter aims to provide the core knowledge concerning some of the most important features in control design and its methods. It covers basic information to introduce the readers to the other chapters of this volume. Fundamental system properties and specifications for control design such as robustness and stability are explained. In addition to a broad overview of modern control techniques with explicative examples and reference publications, the chapter focuses on four intelligent control techniques, which are fuzzy logic control, neural networks, metaheuristics control tuning, and multi-agent systems. Perspective and new trends of research are also exposed for each presented control technique as well as for control systems in general.
9 citations
01 Nov 2017
TL;DR: The work is centered on the development of a Fuzzy-PID controller for Aerial vehicle gimbal which gave a better performance by drastically reducing the overshoot to 7.77%, with more stability as seen in the Bode stability plot which led to a stable convergence of the system.
Abstract: The work is centered on the development of a Fuzzy-PID controller for Aerial vehicle gimbal. Improper control of the gimbal leads to instability of the servo-motor, thus leading to the detection and capture of poor quality photos and videos in aerial surveillance. In presenting a solution to this problem, independent joint control technique was employed to obtain the dynamic model of the gimbal. In order to ensure high performance in the gimbal positioning, the error generated from the difference between the actual output and reference input is compensated using two methods: PID and a hybrid compensation of Fuzzy-PID. The PID controller achieved good performance but recorded high overshoot of 18.60% which can affect the initial movement of the gimbal. To overcome this high overshoot, a Fuzzy Logic Controller was incorporated into the design to achieve a Fuzzy-PID controller. The result obtained gave a better performance by drastically reducing the overshoot to 7.77%, with more stability as seen in the Bode stability plot which led to a stable convergence of the system.
6 citations
Cites background or methods from "PSO tuned PID controller for contro..."
...[11] employed particle swarm optimization (PSO) tuning of a PID controller to control the camera position; [15] employed robust control technique....
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...In [11], the gimbal kinematics is related to the actuator dynamics needed to control the sensor position....
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References
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26 Sep 2005
TL;DR: The architecture of a pan/tilt/roll camera control system implemented on the Georgia Tech’s UAV research helicopter, the GTMax, is outlined, which allows flexible and accurate controlling and results in a relatively light and inexpensive system.
Abstract: *† This paper presents the architecture of a pan/tilt/roll camera control system implemented on the Georgia Tech’s UAV research helicopter, the GTMax. The controller has currently three operating modes available: it can keep the camera at a fixed angle with respect to the helicopter, make the camera point in the direction of the helicopter velocity vector, or track a specific location. The camera is mounted in a large, but relatively light gimbal. Each axis is driven by a modified servo, and optical encoders measure the gimbal orientation. A PID controller with anti-windup and derivative filtering was designed in Simulink based on simple models of the servos and later implemented on the real system. A discussion of results obtained from Hardware-In-The-Loop tests and flight tests is given at the end of the paper. I. Introduction AVs may be used for a variety of civilian and military purposes, of which rescue operations in dangerous areas and surveillance may be mentioned as obvious examples. A lot of these tasks require live video recording and researchers at Georgia Tech are currently working on imaging algorithms making it possible to track moving targets, achieve visual feedback in flight, and to automate landings on moving platforms. Accurate pointing of the onboard camera is vital in order to achieve these tasks, and this paper outlines the architecture of the camera control system implemented on the Georgia Tech UAV lab’s research helicopter, the GTMax. (For related work on camera control systems mounted on UAVs, consider for example Ref. 1 and Ref. 2.) The camera is placed in a gimbal delivered by Neural Robotics and mounted at the front end of the GTMax. Three modified servos from Hitec serve as motors, rotating the camera about each axis and taking velocity commands as inputs. The system is designed so that there are no limitations in the rotation angle about the pan axis, while roll is limited to angles between -100 deg and 100 deg and tilt to -90 deg and 90 deg. Three encoders with indexing delivered by US Digital are used to read each angle. The velocity commands are given at a rate of 50 Hz, as are the angle readings from the encoders. Altogether, these hardware components allow flexible and accurate controlling and results in a relatively light and inexpensive system. The high level controller can operate in three different modes. Its outputs are desired pan, tilt and roll angles for the gimbal in all modes, but the angles are calculated differently in the three cases. The controller may order the camera to point at a specific location, and the computed angles are in that case based on the given location and GTMax position and attitude as estimated by the integrated navigation system 3 . Ground station personnel may enter a target position into the system manually and send it to GTMax, but the ground station may also receive position information about a stationary or moving target (like another aircraft) and forward it to GTMax without any human intervention. The second controller mode makes the camera point in the direction of the helicopter velocity vector, while the third and simplest mode keeps the camera at a fixed angle relative to the helicopter. The low level part of the control is implemented as a simple PID controller; it outputs velocity commands to each motor and angle measurements are fed back from the encoders. The load experienced by each motor differs considerably. In particular, the load on the pan axis motor is larger than that on the other two and also varies in time due to gimbal motion. A simple model partly based on measurement data was therefore made of each motor and implemented in Simulink. Several simulations were then performed to study system behavior for various controller
32 citations
Book•
01 Jan 2002
TL;DR: This thesis is a part of the SIREOS project at Swedish Defence Research Agency which aims at developing a sensor system consisting of infrared and video sensors and an integrated navigation system.
Abstract: This thesis is a part of the SIREOS project at Swedish Defence Research Agency which aims at developing a sensor system consisting of infrared and video sensors and an integrated navigation system. ...
24 citations
02 Dec 2008
TL;DR: In this paper, a tuning method for controlling a gimbals device is proposed, where the position of the camera is first controlled so that the camera may always move to the horizontal position in the ground.
Abstract: In this paper, PD parameters tuning method for controlling a Gimbals device is proposed. The controlled object the gimbals device is the hanging device, and it is used to keep the camera to be the horizontal position. For example this technique has been paid to attention in the aerial photography by the RC helicopter.However, it is thought as problem that the camera vibrates and the image shakes in the aerial photography by the RC helicopter. Therefore, the position of the camera is first controlled so that the camera may always move to the horizontal position in the ground. As the control technique, the system parameters are estimated by the least squares method. Next, the PD parameters are computed by the generalized minimum variance control law, and control the gimbals device by the gimbals control using the PD control technique. In this paper, the design scheme of the control system is discussed, and the behavior of the control system is examined in the experimental equipment.
13 citations
"PSO tuned PID controller for contro..." refers methods in this paper
...Whereas in automated systems tracking software can be used along with different control algorithms to control camera position without human in loop [3]....
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Journal Article•
TL;DR: Soft computing methods like Genetic algorithm and Particle swarm optimization are used for the position control of the DC servo motor and it is found that the soft computing techniques gives better results compared to the conventional PID tuning method.
Abstract: In this paper, position control of servo motor using PID controller with soft computing optimization techniques is discussed. PID controllers widely used in the industry. Different methods are available for tuning the PID controller. In this paper conventional tuning method Z-N method and soft computing methods like Genetic algorithm (GA) and Particle swarm optimization (PSO) are used for the position control of the DC servo motor. The results obtained from soft computing methods (GA, PSO) are compared with conventional tuning method (Z-N) found that the soft computing techniques gives better results compared to the conventional PID tuning method.
12 citations
"PSO tuned PID controller for contro..." refers background in this paper
...It solves problems based on the movement and intelligence of swarms [7]....
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Journal Article•
TL;DR: The paper presents to design PID controller parameters for an unstable Automatic Voltage Regulator system using Particle swarm optimization (PSO), to minimize the integral errors and reduce transient response by minimizing overshoot, settling time and rise time of step response.
Abstract: The paper Present to design PID controller parameters for an unstable Automatic Voltage Regulator system using Particle swarm optimization (PSO). The design goal is to minimize the integral errors and reduce transient response by minimizing overshoot, settling time and rise time of step response. First an objective function is defined, and then by minimizing the objective functions using real-coded PSO, the optimal controller parameters can be assigned. The avr system taken for case study is inherently unstable, highly nonlinear and after tuning of PID using PSO, results stable system.
8 citations