This report presents optimal control methods integrated with hierarchical control framework to realize real-time collision-free optimal trajectories for motion control in kinematic chain manipulator (KCM) robot systems under dynamic environments. Recently, they have been increasingly used in applications where manipulators are required to interact with random objects and humans. As a result, more complex trajectory planning schemes are required. The main objective of this research is to develop new motion control strategies that can enable such robots to operate efficiently and optimally in such unknown and dynamic environments. Two direct optimal control methods: The direct collocation method and discrete mechanics for optimal control methods are investigated for solving the related constrained optimal control problem and the results are compared. Using the receding horizon control structure, open-loop sub-optimal trajectories are generated as real-time input to the controller as opposed to the predefined trajectory over the entire time duration. This, in essence, captures the dynamic nature of the obstacles. The closed-loop position controller is then engaged to span the robot end-effector along this desired optimal path by computing appropriate torque commands for the joint actuators. Employing a two-degree of freedom technique, collision-free trajectories and robot environment information are transmitted in real-time by the aid of a bidirectional connectionless datagram transfer. A hierarchical network control platform is designed to condition triggering of precedent activities between a dedicated machine computing the optimal trajectory and the real-time computer running a low-level controller. Experimental results on a 2-link planar robot are presented to validate the main ideas. Real-time implementation of collision-free workspace trajectory control is achieved for cases where obstacles are arbitrarily changing in the robot workspace.

In recent years, more and more light aircraft enter our daily life, from Agricultural applications, emergency rescue, flight experiment and training to Barriers to entry, light aircraft always have their own advantages. Thus, they have become more and more popular. However, in the process of GDP research about Flight Control System design for the Flying Crane, the author read a lot of literature about Flight Control System design, then noticed that the research in Flight Control System have apparently neglected to Low-cost vehicles. So it is necessary to do some study about Flight Control System for this kind of airplane. The study will more concern the control law design for ultra-light aircraft, the author hopes that with an ‘intelligence’ Flight Control System design, this kind of aircraft could sometimes perform flying tasks according to a prearranged flight path and without a pilot. As the Piper J-3 cub is very popular and the airframe data can be obtained more easily, it was selected as an objective aircraft for the control law design. Finally, a ¼ scale Piper J-3 cub model is selected and the aerodynamics coefficients are calculated by DATCOM and AVL. Based on the forces and moments acting on the aircraft, the trim equilibrium was calculated for getting proper dynamics coefficients for the selected flight conditions. With the aircraft aerodynamics coefficients, the aircraft dynamics characteristics and flying qualities are also analyzed. The model studied in this thesis cannot answer level one flying qualities in the longitudinal axis, which is required by MIL-F- 8785C. The stability augment system is designed to improve the flying qualities of the longitudinal axis. The work for autopilot design in this thesis includes five parts. First, the whole flight profile is designed to automatically control aircraft from takeoff to landing. Second, takeoff performance and guidance law is studied. Then, landing performance and trajectory is also investigated. After that, the control law design is decoupled into longitudinal axis and later-directional axis. Finally, simulation is executed to check the performance for the auto-controller.

This report presents optimal control methods integrated with hierarchical control framework to realize real-time collision-free optimal trajectories for motion control in kinematic chain manipulator (KCM) robot systems under dynamic environments. Recently, they have been increasingly used in applications where manipulators are required to interact with random objects and humans. As a result, more complex trajectory planning schemes are required. The main objective of this research is to develop new motion control strategies that can enable such robots to operate efficiently and optimally in such unknown and dynamic environments. Two direct optimal control methods: The direct collocation method and discrete mechanics for optimal control methods are investigated for solving the related constrained optimal control problem and the results are compared. Using the receding horizon control structure, open-loop sub-optimal trajectories are generated as real-time input to the controller as opposed to the predefined trajectory over the entire time duration. This, in essence, captures the dynamic nature of the obstacles. The closed-loop position controller is then engaged to span the robot end-effector along this desired optimal path by computing appropriate torque commands for the joint actuators. Employing a two-degree of freedom technique, collision-free trajectories and robot environment information are transmitted in real-time by the aid of a bidirectional connectionless datagram transfer. A hierarchical network control platform is designed to condition triggering of precedent activities between a dedicated machine computing the optimal trajectory and the real-time computer running a low-level controller. Experimental results on a 2-link planar robot are presented to validate the main ideas. Real-time implementation of collision-free workspace trajectory control is achieved for cases where obstacles are arbitrarily changing in the robot workspace.

[pt] Quadricópteros vêm sendo o objeto de estudo de inúmeras pesquisas ao redor do mundo. Diversas técnicas de controle já foram desenvolvidas, cada uma com seus prós e contras, objetivando o aprimoramento do desempenho destes veículos aéreos na consecução de tarefas específicas. Este trabalho foca na comparação de características de desempenho de técnicas de controle aplicadas a quadricópteros. Este trabalho mostra o projeto de controles modernos aplicados à quadricópteros apresentando tais técnicas, iniciando com a realimentação de estados com polos dominantes, passando pelo controle Linear Quadratic Regulator (LQR). Por sua vez, visando otimizar o desempenho das técnicas de controle aqui estudadas, foram aplicadas técnicas de inteligência computacional para resolver um problema de otimização do LQR e para auxiliar no controle de forças dos rotores. Apresenta-se o projeto de um controle PID, que será usado como referência para as demais técnicas analisadas. O controle por realimentação de estados citado anteriormente obteve bons resultados. O tempo de assentamento foi o menor para o eixo Z, overshoots e o erro em regime permanente, os menores para os eixos X e Y. O controlador Fuzzy conseguiu fazer seu papel auxiliando a movimentação do quadricóptero. O GA otimizou o tempo de assentamento do LQR. Esse controle conseguiu alcançar os menores tempos de assentamento para os eixos X e Y, sendo mais rápido que a configuração original do LQR, escolhida por heurísticas. Com esse trabalho foi possível notar que os controladores modernos, realimentação de estado e LQR, tem um desempenho melhor que o controle PID de referência. / [en] Quadcopters are researched all over the world. A lot of techniques had been developed and many others a blistering, each one with their pros and cons. The focus of this work is the comparison between the performance of some techniques most used in quadcopters, qualifying these techniques. This work shows the designing process of a quadcopter controllers, starting with state feedback with dominant poles and going to the Linear Quadratic Regulator controller. Focusing on optimizing the performance of those control strategies, computational techniques were used to solve an LQR optimization problem and to help choose the best inputs for the rotors. This work presents a PID controller that will be used as a reference for comparison. The state feedback controller with the Fuzzy position control performed very well, being the fastest one to settle on the Z axis, having the least overshoots and the lowest steadystate errors for the X and Y axes. The Fuzzy controller did what was supposed to do, smoothing and enabling a precise movement for the quadcopter. The GA also did what was supposed to do and improved the settling time for the LQR controller. It showed that it was a nice way to tune the Q and R matrixes, allowing the controller to be the fastest one to settle in the X and Y axes. As a result of this work, the modern control techniques, state feedback and LQR, performance better than the classic PID controller, used as reference.