In this thesis a class of robust non-linear controllers for a gantry crane system are discussed. The gantry crane has three degrees of freedom, all of which are interrelated. These are the horizontal traverse of the cart, the vertical motion of the goods (i.e. rope length) and the swing angle made by the goods during the movement of the cart. The objective is to control all three degrees of freedom. This means achieving setpoint control for the cart and the rope length and cancellation of the swing oscillations. A mathematical model of the gantry crane system is developed using Lagrangian dynamics. In this thesis it is shown that a model of the gantry crane system can be represented as two sub models which are coupled by a term which includes the rope length as a parameter. The first system will consist of the cart and swing dynamics and the other system is the hoist dynamics. The mathematical model of these two systems will be derived independent of the other system. The model that is comprised of the two sub models is verified as an accurate model of a gantry crane system and it will be used to simulate the performance of the controllers using Matlab. For completeness a fully coupled mathematical model of the gantry crane system is also developed. A detailed design of a gain scheduled sliding mode controller is presented. This will guarantee the controller's robustness in the presence of uncertainties and bounded matched disturbances. This controller is developed to achieve cart setpoint and swing control while achieving rope length setpoint control. A non gain scheduled sliding mode controller is also developed to determine if the more complex gain scheduled sliding mode controller gives any significant improvement in performance. In the implementation of both sliding mode controllers, all system states must be available. In the real-time gantry crane system used in this thesis, the cart velocity and the swing angle velocity are not directly available from the system. They will be estimated using an alpha-beta state estimator. To overcome this limitation and provide a more practical solution an optimal output feedback model following controller is designed. It is demonstrated that by expressing the system and the model for which the system is to follow in a non-minimal state space representation, LQR techniques can be used to design the controller. This produces a dynamic controller that has a proper transfer function, and negates the need for the availability of all system states. This thesis presents an alternative method of solving the LQR problem by using a generic eigenvalue solution to solve the Riccati equation and thus determine the optimal feedback gains. In this thesis it is shown that by using a combination of sliding mode and H??? control techniques, a non-linear controller is achieved which is robust in the presence of a wide variety of uncertainties and disturbances. A supervisory controller is also described in this thesis. The supervisory control is made up of a feedforward and a feedback component. It is shown that the feedforward component is the crane operator's action, and the feedback component is a sliding mode controller which compensates as the system's output deviates from the desired trajectory because of the operator's inappropriate actions or external disturbances such as wind gusts and noise. All controllers are simulated using Matlab and implemented in real-time on a scale model of the gantry crane system using the program RTShell. The real-time results are compared against simulated results to determine the controller's performance in a real-time environment.

In this thesis a class of robust non-linear controllers for a gantry crane system are discussed. The gantry crane has three degrees of freedom, all of which are interrelated. These are the horizontal traverse of the cart, the vertical motion of the goods (i.e. rope length) and the swing angle made by the goods during the movement of the cart. The objective is to control all three degrees of freedom. This means achieving setpoint control for the cart and the rope length and cancellation of the swing oscillations. A mathematical model of the gantry crane system is developed using Lagrangian dynamics. In this thesis it is shown that a model of the gantry crane system can be represented as two sub models which are coupled by a term which includes the rope length as a parameter. The first system will consist of the cart and swing dynamics and the other system is the hoist dynamics. The mathematical model of these two systems will be derived independent of the other system. The model that is comprised of the two sub models is verified as an accurate model of a gantry crane system and it will be used to simulate the performance of the controllers using Matlab. For completeness a fully coupled mathematical model of the gantry crane system is also developed. A detailed design of a gain scheduled sliding mode controller is presented. This will guarantee the controller's robustness in the presence of uncertainties and bounded matched disturbances. This controller is developed to achieve cart setpoint and swing control while achieving rope length setpoint control. A non gain scheduled sliding mode controller is also developed to determine if the more complex gain scheduled sliding mode controller gives any significant improvement in performance. In the implementation of both sliding mode controllers, all system states must be available. In the real-time gantry crane system used in this thesis, the cart velocity and the swing angle velocity are not directly available from the system. They will be estimated using an alpha-beta state estimator. To overcome this limitation and provide a more practical solution an optimal output feedback model following controller is designed. It is demonstrated that by expressing the system and the model for which the system is to follow in a non-minimal state space representation, LQR techniques can be used to design the controller. This produces a dynamic controller that has a proper transfer function, and negates the need for the availability of all system states. This thesis presents an alternative method of solving the LQR problem by using a generic eigenvalue solution to solve the Riccati equation and thus determine the optimal feedback gains. In this thesis it is shown that by using a combination of sliding mode and H??? control techniques, a non-linear controller is achieved which is robust in the presence of a wide variety of uncertainties and disturbances. A supervisory controller is also described in this thesis. The supervisory control is made up of a feedforward and a feedback component. It is shown that the feedforward component is the crane operator's action, and the feedback component is a sliding mode controller which compensates as the system's output deviates from the desired trajectory because of the operator's inappropriate actions or external disturbances such as wind gusts and noise. All controllers are simulated using Matlab and implemented in real-time on a scale model of the gantry crane system using the program RTShell. The real-time results are compared against simulated results to determine the controller's performance in a real-time environment.

Helicopters are often used to transport supplies and equipment. When a heavy load is carried via suspension cables below a helicopter, the load oscillates in response to helicopter motion and disturbance forces, such as wind. This oscillation is dangerous and adversely affects control of the helicopter, especially when carrying large or heavy loads. By adding input shaping to the helicopter's flight controller, the suspended load oscillation caused by helicopter motion is greatly reduced. A significant benefit of this approach is that it does not require measurement of the load position. This thesis contains derivations and analysis of simple planar helicopter-load dynamic models, and these models are verified using experimental data from model-scale, radio-controlled helicopters. The effectiveness of input shaping at eliminating suspended load oscillation is then demonstrated on this experimental hardware. In addition, the design of an attitude command, near-hover flight controller that combines input shaping and a common flight control architecture is illustrated using dynamic models of a Sikorsky S-61 helicopter, and simulation results are shown for example lateral and longitudinal repositioning movements. Results show that applying input shaping to simulated pilot commands greatly improves performance when carrying a suspended load.

This thesis evaluates different controllers for traction control on an electric forklift truck and has been done in cooperation with Toyota Material Handling Manufacturing Sweden. The need for a traction control system has increased with the introduction of lithium-ion batteries replacing the older lead-acid batteries, reducing the battery weight and therefore the downward force on the driving wheel increasing the risk for slip. The forklift truck was modelled using Simulink and validated by experiment. Different possible control strategies were investigated and three were chosen for implementation in simulation. These were controllers based on Model Following Control, Maximum Transmissible Torque Estimation and Sliding Mode Control. Model Following Control makes use of a nominal model to compare actual wheel speed values with nominal wheel speed values to determine if slip is occurring, Maximum Transmissible Torque Estimation makes use of a closed-loop disturbance observer to compute the maximum transmissible torque possible without inducing slip and using it as a limitation on the input signal, and Sliding Mode Control uses different functions to \say{slide} along a sliding surface to stay around a specific slip value. All three controller types were developed both as speed controlled and torque controlled. All of the controllers could reduce slip heavily in simulation. The Maximum Transmissible Torque Estimation controller reduced slip the most and kept oscillations at a minimum, but was not as responsive as the others to driver commands. The conclusion was that the controller of choice would depend on the working environment of the forklift truck. In a low friction environment where slip is expected to occur often, the Maximum Transmissible Torque Estimation controller is advisable, while the other two would be a better choice for environment with low slip occurrence. The use of torque control, while often better with regards to decreasing slip, could not be advised due to a perceived increase in implementation cost.