Mohd Ariffanan Mohd Basri

Probabilistic Neural Network for Brain Tumor Classification

In this paper, Probabilistic Neural Network with image and data processing techniques was employed to implement an automated brain tumor classification. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques for instant, neural networks, and fuzzy logic shown great potential in this field. Hence, in this paper the Probabilistic Neural Network was applied for the purposes. Decision making was performed in two stages: feature extraction using the principal component analysis and the Probabilistic Neural Network (PNN). The performance of the PNN classifier was evaluated in terms of training performance and classification accuracies. Probabilistic Neural Network gives fast and accurate classification and is a promising tool for classification of the tumors.

Enhanced Backstepping Controller Design with Application to Autonomous Quadrotor Unmanned Aerial Vehicle

Quadrotor unmanned aerial vehicle (UAV) is an underactuated multi-input and multi-output (MIMO) system which has nonlinear dynamic behavior such as high coupling degree and unknown nonlinearities. It is a great challenge to design a quadrotor control system due to these features. In this paper, the contribution is focused on the backstepping-based robust control design of the quadrotor UAV. Firstly, the dynamic model of the aerial vehicle is mathematically formulated. Then, a robust controller is designed for the stabilization and tracking control of the vehicle. The developed robust control system comprises a backstepping and a proportional-derivative (PD) controller. Backstepping is a recursive design methodology that uses Lyapunov theorem which can guarantee the stability of the nominal model system, while PD control is used to attenuate the effects caused by system uncertainties. For the problem of determining the backstepping control parameters, particle swarm optimization (PSO) algorithm has been employed. In addition, the genetic algorithm (GA) technique is also adopted for the purpose of performance comparison with PSO scheme. Finally, the designed controller is experimentally evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development.

Intelligent adaptive backstepping control for MIMO uncertain non-linear quadrotor helicopter systems

Designing a controller for multi-input–multi-output (MIMO) uncertain non-linear systems is one of the most important challenging works. In this paper, the contribution is focused on the design and analysis of an intelligent adaptive backstepping control for a MIMO quadrotor helicopter perturbed by unknown parameter uncertainties and external disturbances. The design approach is based on the backstepping technique and uses a radial basis function neural network (RBFNN) as a perturbation approximator. First, a backstepping controller optimized by the particle swarm optimization is developed for a nominal helicopter dynamic model. Then, the unknown perturbations are approximated based on the universal approximation property of the RBFNN. The parameters of the RBFNN are adjusted through online learning. To improve the control design performance further, a fuzzy compensator is introduced to eliminate the approximation error produced by the neural approximator. Asymptotical stability of the closed-loop control system is analytically proven via the Lyapunov theorem. The main advantage of the proposed methodology is that no prior knowledge of parameter uncertainties and disturbances is required. Simulations of hovering and trajectory tracking missions of a quadrotor helicopter are conducted. The results demonstrate the effectiveness and feasibility of the proposed approach.

A hybrid optimal backstepping and adaptive fuzzy control for autonomous quadrotor helicopter with time-varying disturbance

In this paper, a hybrid control system called as backstepping adaptive fuzzy control system, which integrates nominal and compensation controller is developed for autonomous quadrotor helicopter with inherent time-varying disturbance. In this hybrid control system, the nominal controller based on backstepping technique is the main controller, and the compensation controller containing a fuzzy control approach is used to eliminate the effect of uncertainties caused by external disturbance. In addition, in order to relax the requirement of prior knowledge on the bound of external disturbance, an online adaptation law is derived. Asymptotical stability of the closed-loop control system is analytically proven via the Lyapunov theorem. For the problem of determining the backstepping control parameters, particle swarm optimization algorithm has been employed. Finally, the designed controller is experimentally evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development.

Nonlinear control of an autonomous quadrotor unmanned aerial vehicle using backstepping controller optimized by particle swarm optimization

Quadrotor unmanned aerial vehicle (UAV) is an unstable nonlinear control system. Therefore, the development of a high performance controller for such a multi-input and multi-output (MIMO) system is important. The backstepping controller (BC) has been successfully applied to control a variety of nonlinear systems. Conventionally, control parameters of a BC are usually chosen arbitrarily. The problems in this method are the adjustment is time demanding and a designer can never tell exactly what are the optimal control parameters should be selected. In this paper, the contribution is focused on an optimal control design for stabilization and trajectory tracking of a quadrotor UAV. Firstly, a dynamic model of the aerial vehicle is mathematically formulated. Then, an optimal backstepping controller (OBC) is proposed. The particle swarm optimization (PSO) algorithm is used to compute control parameters of the OBC. Finally, simulation results of a highly nonlinear quadrotor system are presented to demonstrate the effectiveness of the proposed control method. From the simulation results it is observed that the OBC tuned by PSO provides a high control performance of an autonomous quadrotor UAV.

Fuzzy supervisory backstepping controller for stabilization of quadrotor unmanned aerial vehicle

In this paper, a fuzzy supervisory backstepping controller (FSBC) is designed for the altitude and attitude stabilization of quadrotor unmanned aerial vehicle (UAV). The designed controller consists of a backstepping controller which can automatically select its parameters, on-line by a fuzzy supervisory mechanism. The stability criterion for the stabilization of the quadrotor is proven by the Lyapunov theorem. Numerical simulations using the dynamic model of a four degree of freedom (DOF) quadrotor UAV show the effectiveness of the approach. Besides, the simulation results indicate that the proposed design techniques can stabilize the quadrotor UAV with better performance than established linear design techniques.

Backstepping controller with intelligent parameters selection for stabilization of quadrotor helicopter

backstepping controller (IBC) is designed for the quadrotor altitude and attitude stabilization in the existence of external disturbances and measurement noise. The designed controller consists of a backstepping controller which can automatically select its parameters on-line by a fuzzy supervisory mechanism. The stability criterion for the stabilization of the quadrotor is proven by the Lyapunov theorem. Several numerical simulations using the dynamic model of a four degree of freedom (DOF) quadrotor helicopter show the effectiveness of the approach. Besides, the simulation results indicate that the proposed design techniques can stabilize the quadrotor helicopter with better performance than established linear design techniques.

GSA-based optimal backstepping controller with a fuzzy compensator for robust control of an autonomous quadrotor UAV

Purpose – The purpose of this paper is to propose a new approach for robust control of an autonomous quadrotor unmanned aerial vehicle (UAV) in automatic take-off, hovering and landing mission and also to improve the stabilizing performance of the quadrotor with inherent time-varying disturbance. Design/methodology/approach – First, the dynamic model of the aerial vehicle is mathematically formulated. Then, a combination of a nonlinear backstepping scheme with the intelligent fuzzy system as a new key idea to generate a robust controller is designed for the stabilization and altitude tracking of the vehicle. For the problem of determining the backstepping control parameters, a new heuristic algorithm, namely, Gravitational Search Algorithm has been used. Findings – The control law design utilizes the backstepping control methodology that uses Lyapunov function which can guarantee the stability of the nominal model system, whereas the intelligent system is used as a compensator to attenuate the effects cau...

Adaptive PID actuator fault tolerant control of single-link flexible manipulator

Actuator faults may cause performance degradation of a system and may sometimes even lead to instability. This paper deals with the fault tolerant control problem of a single-link flexible manipulator under a loss of actuator effectiveness. The proposed control scheme uses an adaptive proportional–integral–derivative (APID) controller, which may automatically online tune the three control gains, kp, ki, and kd. The adaptation laws of the APID controller are derived in the sense of the Lyapunov function, so that the stability of the closed-loop system may be guaranteed. The main advantage of the proposed methodology is that no prior offline learning or manual retuning of the PID controller is required to accommodate the actuator fault. In addition, the proposed APID controller does not require any knowledge of the fault magnitude in advance. The effectiveness and feasibility of the proposed approach is tested for the hub angular position and tracking control of a single-link flexible manipulator under both faulty and fault-free conditions. The results demonstrate that the approach is valid, leading to an accurate fault reconstruction, a better transient and good tracking performance, and significantly improved upon previous approaches in terms of errors with respect to the corresponding traditional fixed-gain PID controller.

Optimal Formation Control of Multiple Quadrotors Based on Particle Swarm Optimization

This paper presents the optimal formation control for a group of quadrotors based on particle swarm optimization (PSO) algorithm. This is motivated by the conventional approaches that still involve a certain degree of trial and error approach which may not give the optimal performance. The parameter optimization using PSO utilizes the linear quadrotor model obtained from feedback linearization technique. Simulations are conducted on the parameter optimization, followed by implementation of the optimal parameters for formation control of multiple quadrotors. The results show the effectiveness of the proposed technique.