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<p>Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. The most PFC did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, norm-bounded external disturbances, the system states and control matrices are modified. In addition, the vehicle time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. Compared to adaptive MPC, nonlinear MPC, and cascade LPV control, the proposed robust LPV MPC shows improved tracing accuracy along vehicle lateral dynamics. Finally, the state feedback switched LPV control theory with separate Lyapunov functions under both arbitrary switching and average-dwell-time (ADT) switching conditions are studied and applied to cover the path following control in full speed range. Numerical examples, tracking effectiveness, and convergence analysis are provided to demonstrate and ensure the control effectiveness and strong robustness of the proposed algorithms.</p>

付記する学位プログラム名: デザイン学大学院連携プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21761号 / 工博第4578号 / 新制||工||1713(附属図書館) / 京都大学大学院工学研究科航空宇宙工学専攻 / (主査)教授 藤本 健治, 教授 泉田 啓, 教授 太田 快人 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Path-following is a primary task for most marine, air or space crafts, especially during autonomous operations. Research on autonomous underwater vehicles (AUV) has received large interests in the last few decades with research incentives emerging from the safe, cost-effective and practical solutions provided by their applications such as search and rescue, inspection and monitoring of pipe-lines ans sub-sea structures. This thesis presents a novel guidance system based on the popular line-of-sight (LOS) guidance law for path-following (PF) of underwater vehicles (UVs) subject to environmental disturbances. Mathematical modeling and dynamics of (UVs) is presented first. This is followed by a comprehensive literature review on guidance-based path-following control of marine vehicles, which includes revised definitions of the track-errors and more detailed illustrations of the general PF problem. A number of advances on relative equations of motion are made, which include an improved understanding of the fluid FLOW frame and expression of its motion states, an analytic method of modeling the signs of forces and moments and the proofs of passivity and boundedness of relative UV systems in 3-D. The revision in the relative equations of motion include the concept of state relativity, which is an improved understanding of relativity of motion states expressed in reference frames and is also useful in incorporating environmental disturbances. In addition, the concept of drift rate is introduced along with a revision on the angles of motion in 3-D. A switching mechanism was developed to overcome a drawback of a LOS guidance law, and the linear and nonlinear stability results of the LOS guidance laws have been provided, where distinctions are made between straight and curved PF cases. The guidance system employs the unique formulation and solution of the speed allocation problem of allocating a desired speed vector into x and y components, and the course control that employs the slip angle for desired heading for disturbance rejection. The guidance system and particularly the general course control problem has been extended to 3-D with the new definition of vertical-slip angle. The overall guidance system employing the revised relative system model, course control and speed allocation has performed well during path-following under strong ocean current and/or wave disturbances and measurement noises in both 2-D and 3-D scenarios. In 2-D and 3-D 4 degrees-of-freedom models (DOF), the common sway-underactuated and fully actuated cases are considered, and in 3-D 5-DOF model, sway and heave underactuated and fully actuated cases are considered. Stability results of the LOS guidance laws include the semi-global exponential stability (SGES) of the switching LOS guidance and enclosure-based LOS guidance for straight and curved paths, and SGES of the loolahead-based LOS guidance laws for curved paths. Feedback sliding mode and PID controllers are applied during PF providing a comparison between them, and simulations are carried out in MatLab.

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.