In 1970¡¦s, by applying the field-oriented analysis method, the decoupling of motor speed (motor torque) and rotor flux is obtained. However, the performance is rather sensitive to the variation of motor parameters, especially the motor time constant, which varies with the temperature and saturation of magnetizing inductance. In this thesis, the model following based £g design for induction motor speed control is studied. Roughly speaking, the model following component provides a reference model with desired closed-loop performance and the £g component provides a systematic synthesis procedure so that, under practical uncertainty and load disturbance, the goal of asymptotic model following is achieved.

Simulation, whether in-flight or ground-based, is an invaluable tool for testing and evaluating aircraft. Classically, a simulation model is specific to a single particular airframe, only able to model those flying characteristics. Vast information can be gained from a simulation that is able to model a wide range of aircraft, through a comparison of the performance of these aircraft. Such a variable stability simulation model was created based on 46 stability parameters, including natural frequencies, damping ratios, time constants, and gains. The simulation was obtained using transfer functions representing the aircraft state responses to control inputs. These transfer functions were converted into state space systems used to create the linear equations for the model. The model was first developed as a desktop simulation and then converted for use with the Virginia Tech's 2F122A flight simulator. This conversion required a simple dynamic inversion of the body axis force and moment terms. To reduce the error in these terms, a model following scheme was incorporated. A series of canned inputs and real-time pilot-in-the-loop tests were flown to evaluate the variable stability model. Results in this paper have demonstrated the successful creation of a variable stability simulation model. / Master of Science

The purpose of this dissertation is to study the robust control for the smart flexible linkage mechanism. The control of flexible linkage induced inertia force under high-speed rotation is taken into consideration with the system parameter uncertainties such as modeling error, truncation error, and both of control spillover and observation spillover due to the residual modes of structural control problem. Based on the principles of LQ, optimal model following (OMF) and frequency shaping, this study proposes some sufficient conditions of stability criteria for the design of robust controller, respectively. These techniques guarantee that the controlled plant, under both bounded parameter perturbations and model truncation, could remain stable. Meanwhile, searching for the optimal locating positions of sensor and actuator by applying Taguchi method and genetic algorithm (GA) combined technique is further studied. The system is modeled through employing finite element method (FEM), and the limited lower frequency part modes are separated into controlled modes and residual modes. In time domain, at first we design a Luenberger-observer-based robust controller for the finite-dimensional mode plant keeping stability in a specified region. And then, a robust controller with the OMF is designed for the controlled system to achieve the performance as those of the specified optimum model. From the view of frequency domain, the robust controller could also be deigned according to the frequency shaping principle to suppress the exciting effect of higher frequency residual modes, and similarly avoid that the system might be destabilized. Finally, the combination of Taguchi method and GA technique to search the optimal locations for placing actuator and sensor to coincide with the stability and performance requirement is also done. From the computer simulation, the middle point movement of the linkage is obviously well controlled; hence, the designed robust controllers can certainly suppress the affection of parameter uncertainties and the spillover stimulation of residual modes, and can produce satisfactory results.

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.

The motivation of this thesis is to propose a robust control technique for a laser beam system with target estimation. The laser beam is meant to track and fall on a particular portion of the target until the operation is accomplished. There are many applications of such a system. For example, laser range finder uses laser beam to determine the distance of the target from the source. Recently, unmanned aerial drones have been developed that run on laser power. Drone batteries can be recharged with power sup-ply from laser source on the ground. Laser is also used in high energy laser weapon for defence applications. However, laser beams travelling long distances deviate from the desired location on the target due to continually changing atmospheric parameters (jitter effect) such as pressure, temperature, humidity and wind speed. This deviation error is controlled precisely using a lightweight fast steering mirror (FSM) for fine correction. Furthermore, for a moving target, minimizing the deviation of the beam is not sufficient. Hence, in coarse correction, the target has to be tracked by determining its position and assigning the corresponding azimuth and elevation angles to the laser sources. Once these firing angles are settled within an accuracy of +3 mrad, the effort for minimizing the beam deviation (fine correction) takes place to improve the accu-racy to +10 rad. The beam deviation due to jitter effect is measured by a narrow field of view (NFOV) camera at a high frame rate (1000 frames per second), which takes one frame to com-pute this error information. As a result, controller receives error information witha delay from NFOV. This data cannot be modelled for prediction and hence, a few promising data driven techniques have been implemented for one step ahead prediction of the beam deviation. The predictions are performed over a set of sliding window data online after rejecting the outliers through least square approximated straight line. In time domain, methods like auto-regressive least square, polynomial extrapolation (zeroth, first and second order), Chebyshev polynomial extrapolation, spline curve extrapolation are implemented. Further, a convex combination of zeroth order hold and spline extrapolation is implemented. In frequency domain, Fourier series-Fourier transform and L-point Discrete Fourier Transform stretching are implemented where the frequency component of the signal are analysed properly and propagated for one step ahead prediction. After one step ahead prediction, three nominal controllers (PID, DI and DLQR) are designed such that the output of FSM tracks the predicted beam deviation and the performances of these controllers are compared. Since the FSM is excited by high frequency signals, its performance degrades, which leads to parameter degradation in the mathematical model. Hence, three adaptive controllers have been implemented, namely, model reference adaptive control (MRAC), model reference adaptive sliding mode control (MRASMC) and model following neuro-adaptive control (MFNAC). The parameters of the FSM model are degraded up to 20% and the model is augmented with cross coupling terms because the same mirror is used for horizontal and vertical beam deviation. With this condition, the tracking performance and control rate energy consumption of the implemented adaptive controllers are analysed to choose the best among them. For a moving target, in coarse correction, two tracking radars are placed to measure the position of the target. However, this information is assumed to be noisy, for which an extended Kalman filter is implemented. Once the position of the target is known, the desired firing angles of the laser sources are determined. Given the laser source steering mathematical model, a controller is designed such that it tracks the desired firing angle. Once the residual error of the coarse correction settles inside 3 mrad, fine correction takes part to reduce the residual error to 10 rad. The residual error magnitude of the proposed mechanization was analysed for a moving target by perturbing the FSM model by 20% and zeroth order hold predictor with different combinations of angle tolerance and frame tolerance.