Enhancing Autonomy in VTOL Aircraft Based on Symbolic Computation Algorithms

Research into the autonomy of small Unmanned Aerial Vehicles (UAVs), and especially on Vertical Take Off and Landing (VTOL) systems has intensified significantly in recent years. This paper develops a generic model of a VTOL UAV in symbolic form. The novelty of this work stems from the designed Model Predictive Control (MPC) algorithm based on this symbolic model. The MPC algorithm is compared with a state-of-the-art Linear Quadratic Regulator algorithm in attitude rate acquisition and its more accurate performance and robustness to noise is demonstrated. Results for the controllers designed for each of the aircraft’s angular rates are presented in response to input disturbances

Development of U-model enhansed nonlinear systems

Nonlinear control system design has been widely recognised as a challenging issue where the key objective is to develop a general model prototype with conciseness, flexibility and manipulability, so that the designed control system can best match the required performance or specifications. As a generic systematic approach, U-model concept appeared in Prof. Quanmin Zhu’s Doctoral thesis, and U-model approach was firstly published in the journal paper titled with ‘U-model based pole placement for nonlinear plants’ in 2002.The U-model polynomial prototype precisely describes a wide range of smooth nonlinear polynomial models, defined as a controller output u(t-1) based time-varying polynomial models converted from the original nonlinear model. Within this equivalent U-model expression, the first study of U-model based pole placement controller design for nonlinear plants is a simple mapping exercise from ordinary linear and nonlinear difference equations to time-varying polynomials in terms of the plant input u(t-1). The U-model framework realised the concise and applicable design for nonlinear control system by using such linear polynomial control system design approaches.Since the first publication, the U-model methodology has progressed and evolved over the course of a decade. By using the U-model technique, researchers have proposed many different linear algorithms for the design of control systems for the nonlinear polynomial model including; adaptive control, internal control, sliding mode control, predictive control and neural network control. However, limited research has been concerned with the design and analysis of robust stability and performance of U-model based control systems.This project firstly proposes a suitable method to analyse the robust stability of the developed U-model based pole placement control systems against uncertainty. The parameter variation is bounded, thus the robust stability margin of the closed loop system can be determined by using LMI (Linear Matrix Inequality) based robust stability analysis procedure. U-block model is defined as an input output linear closed loop model with pole assignor converted from the U-model based control system. With the bridge of U-model approach, it connects the linear state space design approach with the nonlinear polynomial model. Therefore, LMI based linear robust controller design approaches are able to design enhanced robust control system within the U-block model structure.With such development, the first stage U-model methodology provides concise and flexible solutions for complex problems, where linear controller design methodologies are directly applied to nonlinear polynomial plant-based control system design. The next milestone work expands the U-model technique into state space control systems to establish the new framework, defined as the U-state space model, providing a generic prototype for the simplification of nonlinear state space design approaches.The U-state space model is first described as a controller output u(t-1) based time-varying state equations, which is equivalent to the original linear/nonlinear state space models after conversion. Then, a basic idea of corresponding U-state feedback control system design method is proposed based on the U-model principle. The linear state space feedback control design approach is employed to nonlinear plants described in state space realisation under U-state space structure. The desired state vectors defined as xd(t), are determined by closed loop performance (such as pole placement) or designer specifications (such as LQR). Then the desired state vectors substitute the desired state vectors into original state space equations (regarded as next time state variable xd(t) = x(t) ). Therefore, the controller output u(t-1) can be obtained from one of the roots of a root-solving iterative algorithm.A quad-rotor rotorcraft dynamic model and inverted pendulum system are introduced to verify the U-state space control system design approach for MIMO/SIMO system. The linear design approach is used to determine the closed loop state equation, then the controller output can be obtained from root solver. Numerical examples and case studies are employed in this study to demonstrate the effectiveness of the proposed methods

Advance control strategies for Maglev suspension systems

The Birmingham Maglev developed over fifteen years ago has successfully demonstrated the inherent advantages of low speed maglev over comparable wheeled systems. It remains the only commercially operational Maglev in the world today. To develop the next generation of Maglev vehicles which will overcome some of the limitations of the Birmingham system, such as chassis length and cost, the following issues are addressed in this thesis. 1) The possibility of interaction between the chassis resonant frequencies and the suspension control system causing poor ride quality and at worst instability, are formally analysed. In the Birmingham vehicle a stiff chassis (fundamental bending mode 40Hz) is used avoiding significant interaction with the suspension controller. Using advanced control strategies the low frequency chassis resonances can be controlled allowing a vehicle structure to be used with a fundamental bending mode of about 12Hz. 2) A modem control strategy is developed which delivers an improved ride quality compared with the present classical control system despite having to operate with a 'soft' chassis. Kalman filters are digitally implemented and conclusions drawn about their performance. The classical control strategy is also successfully demonstrated on a 3 m long 'flexible beam' rig. 3) An associated Maglev suspension problem for the response to ramp inputs such as the transition onto gradients which causes either a large steady state tracking error or a worsening ride quality is addressed by modern control theory using integral feedback techniques and classical theory using third order filters. These controllers are globally optimised by a multi-objective parameter optimisation system which formally considers the conflicts inherent in a suspension system between response to stochastic inputs and deterministic inputs

Longitudinal stability control system design for the UAV Ultra Stick 25e

The purpose of this study is to design a system control in order to obtain the best longitudinal stability of the UAV Ultra Stick 25e. The state-space model has been taken from a previous study of the University of Minnesota which its tittle is System Identification for Small, Low-Cost, Fixed-Wing Unmanned Aircraft. This study will be explained briefly as background but it is not the aim of the project to go in depth in this matter. This project focuses on the comparison of Classical Control, Optimal Control and Robust Control methods in order to find the best solution for the longitudinal stability of the Ultra Stick 25e. To design the controllers and to study the responses of the control systems I have used Matlab's Control System and Robust Control Toolboxes. Only continuous time systems have been treated

Robust trajectory tracking control for unmanned surface vessels under motion constraints and environmental disturbances

To achieve a fully autonomous navigation for unmanned surface vessels (USVs), a robust control capability is essential. The control of USVs in complex maritime environments is rather challenging as numerous system uncertainties and environmental influences affect the control performance. This paper therefore investigates the trajectory tracking control problem for USVs with motion constraints and environmental disturbances. Two different controllers are proposed to achieve the task. The first approach is mainly based on the backstepping technique augmented by a virtual system to compensate for the disturbance and an auxiliary system to bound the input in the saturation limit. The second control scheme is mainly based on the normalisation technique, with which the bound of the input can be limited in the constraints by tuning the control parameters. The stability of the two control schemes is demonstrated by the Lyapunov theory. Finally, simulations are conducted to verify the effectiveness of the proposed controllers. The introduced solutions enable USVs to follow complex trajectories in an adverse environment with varying ocean currents

The application of neural networks in active suspension

This thesis considers the application of neural networks to automotive suspension systems. In particular their ability to learn non-linear feedback control relationships. The speed of processing, once trained, means that neural networks open up new opportunities and allow increased complexity in the control strategies employed. The suitability of neural networks for this task is demonstrated here using multilayer perceptron, (MLP) feed forward neural networks applied to a quarter vehicle simulation model. Initially neural networks are trained from a training data set created using a non-linear optimal control strategy, the complexity of which prohibits its direct use. They are shown to be successful in learning the relationship between the current system states and the optimal control. [Continues.

Model predictive control of resistive wall mode for ITER

Active feedback stabilization of the dominant resistive wall mode (RWM) for an ITER H-mode scenario at high plasma pressure using infinite-horizon model predictive control (MPC) is presented. The MPC approach is closely-related to linear-quadratic-Gaussian (LQG) control, improving the performance in the vicinity of constraints. The control-oriented model for MPC is obtained with model reduction from a high-dimensional model produced by CarMa code. Due to the limited time for on-line optimization, a suitable MPC formulation considering only input (coil voltage) constraints is chosen, and the primal fast gradient method is used for solving the associated quadratic programming problem. The performance is evaluated in simulation in comparison to LQG control. Sensitivity to noise, robustness to changes of unstable RWM dynamics, and size of the domain of attraction of the initial conditions of the unstable modes are examined.Comment: Original manuscript as submitted to Fusion Engineering and Desig

Model-based control of plate vibrations using active constrained layer damping.

In this thesis, the author presents a numerical and experimental study of the application of active constrained layer damping to a clamped-clamped plate. Piezoelectric actuators with modal controllers are used to improve the performance of vibration suppression from the passive constrained layer damping treatment. Surface damping treatments are often effective at suppressing higher frequency vibrations in thin-walled structures such as beams, plates and shells. However, the effective suppression of lower frequency modes usually requires the additional of an active vibration control scheme to augment the passive treatment. Advances in the technologies associated with so-called smart materials are dramatically reducing the cost, weight and complexity of active structural control and make it feasible to consider active schemes in an increasing number of applications. Specifically, a passive constrained layer damping treatment is enhanced with an active scheme employing a piezoceramic (PZT) patch as the actuator. Starting with an established finite element formulation it is shown how model updating and model reduction are required to produce a low-order state-space model which can be used as the basis for active control. The effectiveness of the formulation is then demonstrated in a numerical study. Finally, in the description of the experimental study it is shown how modes in the frequency range from 0 to 600 Hz are effectively suppressed: the two lowest modes (bending and torsional) through active control, the higher modes (around ten in number) by the passive constrained damping layer. The study'S original contribution lies in the experimental demonstration that given a sufficiently accurate model of the plate and passive constrained damping layer, together with a suitable active feedback control algorithm, spillover effects are not significant even when using a single sensor and single actuator. The experimental traces show, in some instances, minor effects due to spillover. However, it can be concluded that the presence of the passive layer introduces sufficient damping into the residual modes to avoid any major problems when using only the minimum amount of active control hardware

Using Constrained Model Predictive Control to Control Two Quadrotors Transporting a Cable-Suspended Payload

This paper applies a constrained MPC controller to control two quadrotors which carry a cable-suspended payload together. The system dynamics is derived from the Euler-Lagrange equation. Given the dynamics complexity, a linear MPC controller is employed for a control task, which is to make the payload to track a desired trajectory while stabilising the two quadrotors. The constraints on quadrotor control signals and payload positions are taken into consideration. The constrained controller could be useful for practical control systems. The simulation results are provided to evaluate the control performance against an LQR controller