基于RBF神经网络补偿的一种绳牵引并联机器人支撑系统的力/位混合控制

为了保证用于风洞试验的绳牵引并联机器人支撑系统(Wire-Driven Parallel Robot Support System WDPRSS)的末端执行精度,设计了一种采用Hamilton-Jacobi Inequality(HJI)定理并基于RBF神经网络补偿的力/位混合控制。通过对WDPRSS的动力学建模分析,选择以位姿作为变量建立WDPRSS的整体动力学方程。将所设计的力/位混合控制代入到整体动力学方程中得误差闭环系统,对闭环系统进行稳定性分析,结果证明WDPRSS是趋于渐进稳定特性的。对八绳牵引的并联机器人支撑系统进行MATLAB/SIMULINK仿真实验,仿真结果表明所设计的力/位混合控制是正确有效的,满足控制精度要求,并将所设计的力/位混合控制与PD控制进行对比分析。最后,通过样机实验验证了所提控制方案的有效性。仿真实验和样机实验为在样机上进行技术实现提供了理论依据和实验基础

Hierarchical control of complex manufacturing processes

The need for changing the control objective during the process has been reported in many systems in manufacturing, robotics, etc. However, not many works have been devoted to systematically investigating the proper strategies for these types of problems. In this dissertation, two approaches to such problems have been suggested for fast varying systems. The first approach, addresses problems where some of the objectives are statically related to the states of the systems. Hierarchical Optimal Control was proposed to simplify the nonlinearity caused by adding the statically related objectives into control problem. The proposed method was implemented for contour-position control of motion systems as well as force-position control of end milling processes. It was shown for a motion control system, when contour tracking is important, the controller can reduce the contour error even when the axial control signals are saturating. Also, for end milling processes it was shown that during machining sharp edges where, excessive cutting forces can cause tool breakage, by using the proposed controller, force can be bounded without sacrificing the position tracking performance. The second approach that was proposed (Hierarchical Model Predictive Control), addressed the problems where all the objectives are dynamically related. In this method neural network approximation methods were used to convert a nonlinear optimization problem into an explicit form which is feasible for real time implementation. This method was implemented for force-velocity control of ram based freeform extrusion fabrication of ceramics. Excellent extrusion results were achieved with the proposed method showing excellent performance for different changes in control objective during the process --Abstract, page iv

GCCF : a generalized contact control framework

The field of robotics has come a long way since the first reprogrammable robot was able to automate simple tasks on an assembly line. However, many industrial robots are stuck doing similar simple tasks in the field, especially in the nuclear industry. Roboticists can expand the task space of industrial robots by making advanced robot technology reliable, easily integrated, and packaged in a manner that does not require an expert in the field to use. One particular field of robotics that could be used to help this task space expansion is compliant control which is used to execute robotic procedures involving contact with environmental objects. It is especially useful when the position or orientation of the environmental objects is not precise. Examples of industrial procedures that a robot could do with compliant control include material reduction, surface finishing, packaging, assembly, material handling, and many more. This thesis explores the state of the art in compliant control and proposes a Generalized Contact Control Framework (GCCF) that packages compliant control laws in a manner that is easy to use for a non-expert. GCCF splits the control of a robot end effector into separate control of each linear and rotational dimension. The user sets the law that controls each dimension independently to one of three intuitive laws. By specifying laws and stiffness independently for each dimension of end effector control, the user can complete a large variety of contact tasks. We illustrate GCCF’s broad capabilities in two flexible demonstrations. The first demonstration provides a graphical user interface to GCCF with which a user can set and reconfigure the control of the end effector while interacting with the robot. This allows the user to subjectively experience the reconfigurablilty as well as the physical behavior prompted by the control. In the second demonstration, we use GCCF to execute multiple contact tasks with the goal of putting a peg in a hole. These demonstrations prove the feasibility and usefulness of GCCF, using the API and ROS compatible package for the controller.Mechanical Engineerin

Robotic Trajectory Tracking: Position- and Force-Control

This thesis employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control. In a first phase, the focus is put on the following of a freeform surface in a discontinuous manner. Next to resulting switching constraints, disturbances and uncertainties, the case of unknown robot models is addressed. In a second phase, once contact has been established between surface and end effector and the freeform path is followed, a desired force is applied. In order to react to changing circumstances, the manipulator needs to show the features of an intelligent agent, i.e. it needs to learn and adapt its behaviour based on a combination of a constant interaction with its environment and preprogramed goals or preferences. The robotic manipulator mimics the human behaviour based on bio-inspired algorithms. In this way it is taken advantage of the know-how and experience of human operators as their knowledge is translated in robot skills. A selection of promising concepts is explored, developed and combined to extend the application areas of robotic manipulators from monotonous, basic tasks in stiff environments to complex constrained processes. Conventional concepts (Sliding Mode Control, PID) are combined with bio-inspired learning (BELBIC, reinforcement based learning) for robust and adaptive control. Independence of robot parameters is guaranteed through approximated robot functions using a Neural Network with online update laws and model-free algorithms. The performance of the concepts is evaluated through simulations and experiments. In complex freeform trajectory tracking applications, excellent absolute mean position errors (<0.3 rad) are achieved. Position and torque control are combined in a parallel concept with minimized absolute mean torque errors (<0.1 Nm)