Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system: This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-of-freedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.

The paper presents the development of modelling and control strategies for a six-degree-of-freedom, unmanned combat aerial vehicle with the inclusion of the centre of gravity position travel during the straight-leg part of an in-flight refuelling manoeuvre. The centre of gravity position travel is found to have a parabolic variation with an increasing mass of aircraft. A nonlinear dynamic inversion-based neurocontroller is designed for the process under investigation. Three radial basis function neural networks are exploited in order to invert the dynamics of the system, one for each control channel. Modal and time-domain analysis results show that the dynamic properties of the aircraft are strongly influenced during aerial refuelling. The effectiveness of the proposed control law is demonstrated through the use of simulation results for an F-16 aircraft. The longitudinal neurocontroller provided interesting results, and performed better than a baseline nonlinear dynamic inversion controller without neural network. On the other hand, the lateral-directional nonlinear dynamic inversion-based neurocontroller did not perform well as the longitudinal controller. It was concluded that the nonlinear dynamic inversion-based neurocontroller could be applied to control an unmanned combat aerial vehicle during aerial refuelling.

This paper presents the development of a proportional-integral-derivative (PID)-based control method for application to active vehicle suspension systems (AVSS). This method uses an inner PID hydraulic actuator force control loop, in combination with an outer PID suspension travel control loop, to control a nonlinear half-car AVSS. Robustness to model uncertainty in the form of variation in suspension damping is tested, comparing performance of the AVSS with a passive vehicle suspension system (PVSS), with similar model parameters. Spectral analysis of suspension system model output data, obtained by performing a road input disturbance frequency sweep, provides frequency response plots for both nonlinear vehicle suspension systems and time domain vehicle responses to a sinusoidal road input disturbance on a smooth road. The results show the greater robustness of the AVSS over the PVSS to parametric uncertainty in the frequency and time domains.

This paper presents the design of a two-loop, force/suspension travel PID control system, for a four degree-of-freedom (DOF), nonlinear, half-car active vehicle suspension system (AVSS). The two-loop system consists of an inner PID hydraulic actuator force control loop and an outer PID suspension travel control loop. Performance of the PID based AVSS is compared to a passive, nonlinear, half-car suspension system with the same model parameters. The simulation results showed the superior performance of the AVSS in the presence of the deterministic road disturbance.

A fuzzy logic controller was designed for a quadrotor unmanned aerial vehicle (UAV) with the aim of stabilizing it in the presence of atmospheric disturbances. Quadrotors are complex flying machines that are inherently unstable having both parametric and structural uncertainties. A mathematical model was designed and implemented into the MATLAB/Simulink environment in order to run simulations. The mathematical model proved to be a sufficient representation of the dynamics and physics of a quadrotor, incorporating all 6 degrees of freedom. A full system identification was performed in order to validate the three attitudes of the quadrotor, namely the roll, pitch and yaw angles. The Takagi-Sugeno (T-S) model used for the validation, together with the Band-Limited White noise signal ensured that all the dynamics of the system were excited. The T-S model yielded good results which gave the approval for the controller design. Simulation results showed that the performance of the controller designed was acceptable and can be improved by using global optimization techniques in the future.