M. H. Shaheed

Mathematical dynamic modelling of a twin-rotor multiple input-multiple output system

Abstract This investigation presents the mathematical modelling of an experimental aerodynamic test rig, a twin-rotor multiple input-multiple output (MIMO) system (TRMS). The system is modelled in terms of vertical one-degree-of-freedom (1DOF), horizontal 1DOF, and two-degree-of-freedom (2DOF) dynamics using Newtonian as well as Lagrangian methods. The modelling is carried out in two phases. In the first phase the interface circuit, d.c. motors, and propulsive forces due to these motors, common to both Newtonian and Lagrangian approaches, are modelled. Thereafter, the dynamic equations for the remaining parts are formulated taking all the effective forces into account. The responses of both the Newtonian and the Lagrangian models are compared with that of the real TRMS to validate the accuracy of the models. The performances of the two models are also compared.

Dynamic characterisation of a flexible manipulator system

This paper presents theoretical and experimental investigations into the dynamic modelling and characterisation of a flexible manipulator system. A constrained planar single-link flexible manipulator is considered. A dynamic model of the system is developed based on finite element methods. The flexural and rigid dynamics of the system as well as inertia effects and structural damping are accounted in the model. Performance of the algorithm in describing the dynamic behaviour of the system is assessed in comparison to an experimental test-rig. Experimental results are presented for validation of the developed finite element model in the time and frequency domains.

Nonlinear modelling of a twin rotor MIMO system using radial basis function networks

Modelling of innovative aircraft such as UAVs, X-wing, tilt body and delta-wing is not easy. This paper presents a nonlinear system identification method for modelling air vehicles of complex configuration. This approach is demonstrated through a laboratory helicopter. Extensive time and frequency-domain model-validation tests are employed to instil confidence in the estimated model. The estimated model has a good predictive capability and can be utilized for nonlinear flight simulation studies. The approach presented is suitable for modelling new generation air vehicles.

Dynamic modelling of a single-link flexible manipulator: parametric and non-parametric approaches

This paper presents an investigation into the development of parametric and non-parametric approaches for dynamic modelling of a flexible manipulator system. The least mean squares, recursive least squares and genetic algorithms are used to obtain linear parametric models of the system. Moreover, non-parametric models of the system are developed using a non-linear AutoRegressive process with eXogeneous input model structure with multi-layered perceptron and radial basis function neural networks. The system is in each case modelled from the input torque to hub-angle, hub-velocity and end-point acceleration outputs. The models are validated using several validation tests. Finally, a comparative assessment of the approaches used is presented and discussed in terms of accuracy, efficiency and estimation of the vibration modes of the system.

Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks

This paper investigates the development and experimental implementation of an adaptive dynamic nonlinear model inversion control law for a Twin Rotor MIMO System (TRMS) using artificial neural networks. The TRMS is a highly nonlinear aerodynamic test rig with complex cross-coupled dynamics and therefore represents the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. The proposed on-line learning algorithm updates the weights and biases of the neural network using the error between the set-point and the real output. The real-time response of the method shows a satisfactory tracking performance in the presence of inversion errors caused by model uncertainty. The approach is therefore deemed to be suitable to apply real-time to other nonlinear systems with necessary modifications.

Performance analysis of 4 types of conjugate gradient algorithms in the nonlinear dynamic modelling of a TRMS using feedforward neural networks

Nowadays aircrafts are expected to perform varied and complex tasks which have presented unprecedented control challenges to the aero dynamicists and control engineers. This implies that linear characterization of aircrafts is not well enough to describe the systems characteristics for control purposes and nonlinear modelling techniques are required. Neural network based nonlinear characterization look promising in this regard. This paper investigates into the development of nonlinear modelling paradigms for modern air vehicles with application to a twin rotor multi-input-multi-output system (TRMS). The system is modelled using a nonlinear autoregressive process with external input (NARX) paradigm with a feedforward neural network. Four different types of conjugate gradient algorithms (CGAs) are used in this investigation for supervised learning of the network and their performances are compared in terms of input-output mapping and speed of convergence.

Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization

An innovative gene selection approach using shuffle method prior to cancer classification is proposed.A novel optimization algorithm, COA-GA, is developed by integrating cuckoo optimization algorithm (COA) and GA to enhance classification performance.Performance of the COA-GA is analyzed and compared with GA, PSO and COA.It is further confirmed that traditional clustering does not have any impact on gene selection and classification performance.Optimization based clustering is shown to enhance the accuracy of gene selection and classification. This research presents an innovative method for cancer identification and type classification using microarray data. The method is based on gene selection with shuffling in association with optimization based unconventional data clustering. A new hybrid optimization algorithm, COA-GA, is developed by synergizing recently invented Cuckoo Optimization Algorithm (COA) with a more traditional genetic algorithm (GA) for data clustering to select the most dominant genes using shuffling. For gene classification, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) artificial neural networks are used. Literature suggests that data clustering using traditional approaches such as K-means, C-means and Hierarchical do not have any impact on classification accuracy. This is also confirmed in this investigation. However, results show that optimization based clustering with shuffling increase the classification accuracy significantly. The proposed algorithm (COA-GA) not only outperforms COA, GA and Particle Swarm optimization (PSO) in achieving?better classification performance but also reaches a better global minimum with only few iterations. Higher accuracy is observed to have achieved with SVM classifier compared to MLP in all datasets used.

Modelling and Open-Loop Control of a Single- Link Flexible Manipulator with Genetic Algorithms

An open-loop control strategy for vibration suppression of a flexible manipulator system using genetic algorithms is presented in this paper. This consists of developing suitable forcing functions so that the dominant vibration modes of the system are not excited and hence the system vibration is reduced. The method requires that the vibration modes of the system be determined very precisely. Genetic algorithms (GAs) are used for this purpose. Low-pass and band-stop (elliptic type) filtered bang-bang torque inputs are accordingly developed on the basis of the identified vibration modes. The filtered torque inputs thus developed are applied to the system in an open-loop configuration and their performances in suppressing structural vibrations of the system are assessed in comparison to a bang-bang torque input. A comparative study of the low-pass and band-stop filtered torque inputs in suppressing the system vibrations are also presented and discussed.

Hybrid Fuzzy-PID-based Control of a Twin Rotor MIMO System

This paper presents the development of a hybrid fuzzy-PID-based control approach for an experimental aerodynamic test rig-a twin rotor multi-input-multi-output system (TRMS). The control objective is to make the beam of the TRMS move quickly and accurately to the desired positions, i.e., the pitch and the yaw angles. Developing controller for this type of system is challenging due to the coupling effects between two axes and also due to its highly nonlinear characteristics. In this investigation accurate dynamic models of the system for both vertical and horizontal movements are developed first in order to get very similar responses to that of the real plant. These models are then used as test-beds to develop a set of hybrid-fuzzy PID controllers. The performance of the controllers in tracking movements in both vertical and horizontal planes are found to be very satisfactory in terms of accuracy, speed and the variations of reference signals. A comparative performance study of this hybrid fuzzy-PID control approach with respect to a single PID approach is also presented in this study

Adaptive closed-loop control of a single-link flexible manipulator

An investigation into the development of a closed-loop vibration control strategy for flexible manipulator systems is presented in this paper. Development of the controller is carried out in two phases. A collocated position controller on the basis of a proportional-derivative feedback control technique is developed first and then a command-filter vibration controller is developed based on the dominant vibration modes of the system and placed inside the position control loop. While the purpose of the position controller is to place the end-point of the manipulator at a position of demand, the objective of the vibration controller is to reduce motion-induced vibration of the manipulator arising from structural flexibility of the system during fast maneuvers. Low-pass and band-stop elliptic filters are used in designing the vibration controller to filter out input energy at dominant vibration modes of the manipulator so that it is not excited at its natural frequencies. The performances of the controllers are assessed within a simulation environment of a single-link flexible manipulator. It is demonstrated that while the performance of the position controller in controlling the rigid body motion of the manipulator is as expected, significant reduction in the level of structural vibration of the system is achieved with the help of the vibration controller.