http://ieeexplore.ieee.org/iel5/5610941/5624686/05624745.pdf

Power electronics

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

A method based on second order sliding mode control (2-SMC) is proposed to design controllers for a small quadrotor UAV. For the switching sliding manifold design, the selection of the coefficients of the switching sliding manifold is in general a sophisticated issue because the coefficients are nonlinear. In this work, in order to perform the position and attitude tracking control of the quadrotor perfectly, the dynamical model of the quadrotor is divided into two subsystems, i.e., a fully actuated subsystem and an underactuated subsystem. For the former, a sliding manifold is defined by combining the position and velocity tracking errors of one state variable, i.e., the sliding manifold has two coefficients. For the latter, a sliding manifold is constructed via a linear combination of position and velocity tracking errors of two state variables, i.e., the sliding manifold has four coefficients. In order to further obtain the nonlinear coefficients of the sliding manifold, Hurwitz stability analysis is used to the solving process. In addition, the flight controllers are derived by using Lyapunov theory, which guarantees that all system state trajectories reach and stay on the sliding surfaces. Extensive simulation results are given to illustrate the effectiveness of the proposed control method.

Second order sliding mode control for a quadrotor UAV.

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

Adaptive neural network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input-output data pairs of the system under consideration. The size of the input-output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper, we have proposed an ANFIS based system modelling where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data and a data set collected from a thermal power plant of the North Eastern Electric Power Corporation (NEEPCO) Limited. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced and thereby computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model.

A novel approach for ANFIS modelling based on full factorial design

In this paper an adaptive second order terminal sliding mode (SOTSM) controller is proposed for controlling robotic manipulators. Instead of the normal control input, its time derivative is used in the proposed controller. The discontinuous sign function is contained in the derivative control and the actual control obtained after integration is continuous and hence chatterless. An adaptive tuning method is utilized to deal with the system uncertainties whose upper bounds are not required to be known in advance. The performance of the proposed control strategy is evaluated through the control of a two-link rigid robotic manipulator. Simulation results demonstrate the effectiveness of the proposed control method.

Adaptive second order terminal sliding mode controller for robotic manipulators

In this paper an integral backstepping sliding mode controller is proposed for controlling underactuated systems. A feedback control law is designed based on backstepping algorithm and a sliding surface is introduced in the final stage of the algorithm. The backstepping algorithm makes the controller immune to matched and mismatched uncertainties and the sliding mode control provides robustness. The proposed controller ensures asymptotic stability. The effectiveness of the proposed controller is compared against a coupled sliding mode controller for swing-up and stabilization of the Cart–Pendulum System. Simulation results show that the proposed integral backstepping sliding mode controller is able to reject both matched and mismatched uncertainties with a chattering free control law, while utilizing less control effort than the sliding mode controller.

Integral backstepping sliding mode control for underactuated systems: swing-up and stabilization of the Cart-Pendulum System.

In this study, an adaptive second-order sliding mode (SOSM) controller is proposed to control a laboratory helicopter called the twin-rotor multi-input-multi-output system (TRMS). The design objectives of the controller are to stabilise the TRMS in significant cross couplings, reach a desired position and accurately track a specified trajectory. The TRMS model is divided into a horizontal and a vertical subsystem (VS). The cross coupling existing between the two subsystems is considered as the system uncertainty. A simple adaptive tuning law is developed for the SOSM controller to deal with the bounded system uncertainty. The major advantage offered by this adaptive SOSM controller is that advance knowledge about the upper bound of system uncertainty is not a necessary requirement. The adaptive SOSM controller for the VS uses a proportional plus integral sliding surface to counter the offset present in the pitch angle. System robustness and the stability of the controller are proved by using the Lyapunov criterion. Apart from imparting robustness, the proposed adaptive SOSM controller reduces undesired chattering in the control input and thus is suitable for application in practical motion control applications. The proposed control scheme is validated by simulation results and is compared against the existing proportional-integral-derivative controllers to show that the overall performance of the proposed adaptive SOSM controller is better in the aspects of error and control indices.

Adaptive second-order sliding mode controller for a twin rotor multi-input-multi-output system

In this paper, a chattering free adaptive sliding mode controller (SMC) is proposed for stabilizing a class of multi-input multi-output (MIMO) systems affected by both matched and mismatched types of uncertainties. The proposed controller uses a proportional plus integral sliding surface whose gain is adaptively tuned to prevent overestimation. A vertical take-off and landing (VTOL) aircraft system is simulated to demonstrate the effectiveness of the proposed control scheme.

Chitralekha Mahanta

Papers

A novel approach for ANFIS modelling based on full factorial design

Adaptive second order terminal sliding mode controller for robotic manipulators

Integral backstepping sliding mode control for underactuated systems: swing-up and stabilization of the Cart-Pendulum System.

Adaptive second-order sliding mode controller for a twin rotor multi-input-multi-output system

Chattering free adaptive multivariable sliding mode controller for systems with matched and mismatched uncertainty