Meher Madhu Dharmana

Sonar based terrain estimation & automatic landing of swarm quadrotors

This paper presents a method for emergency landing of a fleet of quadcopters on an unknown terrain using a sonar sensor. Quadcopters are equipped with sonar sensors for estimation of heights and the terrain is analysed. These data from the quadcopters are used for determining the landing area. Landing zone is considered in such a way that each quad is landed using lesser Energy. Multiple objective Swarm Optimisation technique (MOSO) is used for determining the landing surfaces and guiding each quad to its corresponding landing zones. Algorithms for terrain estimation, surface detection and landing are simulated for a swarm size of three.

MPC controller for trajectory tracking control of quadcopter

This paper focuses on trajectory tracking of quadcopter using a Model Predictive control (MPC). The main motive in using MPC is the ability to consider control and state constraints that occur in practical problems. In addition, MPC techniques consider a clear-cut performance criterion to be lessened during the control law computation. The trajectory tracking problem is solved using two approaches: PID controller and Linear MPC. The modeling of Quadcopter is developed using kinematic and dynamic equations. By making suitable assumptions, a simplified model is obtained, which is taken as the reference model for MPC. Simulation results are provided in order to show the effectiveness of both schemes. For comparison purpose only one degree of freedom, i.e., altitude is considered. The same methodology can be extended to other degrees of freedom(DOF). The results show that LMPC was able to achieve good tracking than the PID controller.

Object detection algorithm for segregating similar coloured objects and database formation

Recent advancement in the processing power of onboard computers has encouraged engineers to impart visual feedbacks into various systems like mechatronics and internet of things. Applications ranging from CCTV surveillance to target detection and tracking using UAVs, there is a wide variety of demand on image processing techniques in terms of computational time and quality. In this scenario, developing generalised algorithms which gives a freedom to user in choosing the trade-off between quality and quick response is a challenging task. In this paper a novel boundary detection algorithm for segregating similar coloured objects in an image is presented, which accommodates a degree of freedom in choosing resolution of object detection to the detection time. This method uses colour based segmentation as preprocessing technique to reduce overall computational complexity. It is independent of the shape (convex or non-convex) and size of the object. Algorithm is developed using Open-CV libraries and implemented for separating similar coloured vehicles from an image of different vehicles on road. Implementation results showing different choices of boundary tightness and computation times are showcased.

Autonomous underwater inspection robot under disturbances

Autonomous underwater inspection robots are getting worldwide interest since they used for various applications like surveying seabed mosaics, shipwreck search, pipeline inspection etc. For performing the above applications, the control of position and altitude of underwater inspection robots are extremely significant as the worth of information obtained is very much reliant in the correctness of position control and tracking of the Autonomous underwater inspection robot. So a Time Delay Controller for the position and altitude control of an underwater inspection robot with disturbances is introduced. A TDC performs its best when the data acquisition rate is rapid. But for the control of underwater inspection robot, which uses a Doppler velocity log navigation system (DVL) as sensor, it cant maintain the data acquisition rate fast since DVL gives data at a poor acquisition rate, results in lessening the performance of TDC. So an integral sliding-mode controller is also provided to the typical TDC to avoid this problem and to improve the control precision even if the DVL navigation system is in operation. The controller proposed here makes computations easier and it reduces the steady state error.

Model reference based intelligent control of an active suspension system for vehicles

An active suspension system is a kind of automotive suspension system which is used to enhance ride comfort, stability and safety while the load on the wheel and the suspension movement remain in safety limits. Several researches have been done in the past 15 decades in this field and many control methods were developed ranging from traditional controls to optimal and adaptive controllers. Robust and nonlinear control algorithms for suspension control are also notable now a days. Linear control schemes are robust and easy to implement but parameter uncertainty and nonlinear dynamics of actuator may reduce efficiency of such controllers. PID controllers are widely used control method because of its simplicity, but it lacks robustness in sudden changes in the parameters of a vehicle. Model predictive control is considered as one of the successful control scheme but due to multivariable interactions and time delay this control scheme is not effective in active suspension control. Nonlinear control schemes such as Artificial neural network controllers are more robust and efficient in Active suspension control. This paper come up with a model reference adaptive control scheme based on neural network for an Active suspension system. Modelling error is considered in this proposed control scheme to provide better adaptivity and stability for active suspension system under change in model parameters. A quarter car model with 2-DOF is selected for the analysis, which covers the vertical dynamics of vehicle. LQR is used as a benchmark controller and the performance of proposed controller is determined by carrying out computer simulations using MATLAB and SIMULINK.

Imu sensor based self stabilizing cup for elderly and parkinsonism

Hand tremor is a neurological disorder that affects people with Parkinsons disease and elderly affecting day to day activities. Hand vibration is a rhythmic muscle movement that occurs due to disorder in the motor system of human body. There are no permanent treatment options available. The subjects typically do life style modifications to cope up with the disease. In this work, we propose a wearable auto stabilizing cup holder that helps in performance the routine activities such as drinking water etc. The proposed system uses Inertial measurement unit (IMU) sensor and actuators for stabilizing the cup when under severe hand vibration. Dynamic modelling of the system is performed including slosh dynamics. A PID controller performance under disturbance and Cross-coupling effect has been analyzed. The prototype design is implemented using solid works and the controller simulation is performed using MATLAB Simulink. Initial tests show satisfactory performance of the system.

Novel ANFIS Based Control for Solar Energy Heliostats

Solar central receiver or power tower thermal energy systems demand high precision heliostat tracking. Large numbers of heliostats in the field need to precisely track their own targets. Such a scenario suggests a hierarchical control architecture in which a model based controller could occupy the supervisory level. A novel two-level control architecture consisting of the NREL SPA (Solar Position Algorithm) and ANFIS (Artificial Neural and Fuzzy Inference System) as supervisory control is presented. The ANFIS system offers reduced complexity and allows for the quick simulation of various heliostat geometries without solving the kinematics. The initial system design, simulation and tracking results using Matlab SimMechanics and Simulink are given here.