Control Strategies for Machining with Industrial Robots

This thesis presents methods for improving machining with industrial robots using control, with focus on increasing positioning accuracy and controlling feed rate. The strong process forces arising during high-speed machining operations, combined with the limited stiffness of industrial robots, have hampered the usage of industrial robots in high-end machining tasks. However, since such manipulators may offer flexible and cost-effective machining solutions compared to conventional machine tools, it is of interest to increase the achievable accuracy using industrial robots. In this thesis, several different methods to increase the machining accuracy are presented. Modeling and control of a piezo-actuated high-dynamic compensation mechanism for usage together with an industrial robot during a machining operation, such as milling in aluminium, is considered. Position control results from experiments are provided, as well as an experimental verification of the benefit of utilizing the online compensation scheme. It is shown that the milling surface accuracy achieved with the proposed compensation mechanism is increased by up to three times compared to the uncompensated case. Because of the limited workspace and the higher bandwidth of the compensator compared to the robot, a mid-ranging approach for control of the relative position between the robot and the compensator is proposed. An adaptive, model-based solution is presented, which is verified through simulations as well as experiments, where a close correspondence with the simulations was achieved. Comparing the IAE from experiments using the proposed controller to previously established methods, a performance increase of up to 56 % is obtained. Additionally, two different approaches to increasing the accuracy of the machining task are also presented in this thesis. The first method is based on identifying a stiffness model of the robot, and using online force measurements in order to modify the position of the robot to compensate for position deflections. The second approach uses online measurements from an optical tracking system to suppress position deviations. In milling experiments performed in aluminium, the absolute accuracy was increased by up to a factor of approximately 6 and 9, for the two approaches, respectively. Robotic machining is often performed using position feedback with a conservative feed rate, to avoid excessive process forces. By controlling the applied force, realized by adjusting the feed rate of the workpiece, precise control over the material removal can be exercised. This will in turn lead to maximization of the time-efficiency of the machining task, since the maximum amount of material can be removed per time unit. This thesis presents an adaptive force controller, based on a derived model of the machining process and an identified model of the Cartesian dynamics of the robot. The controller is evaluated in both simulation and an experimental setup

Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

Navigation Control of an Automated Guided Underwater Robot using Neural Network Technique

In recent years, under water robots play an important role in various under water operations. There is an increase in research in this area because of the application of autonomous underwater robots in several issues like exploring under water environment and resource, doing scientific and military tasks under water. We need good maneuvering capabilities and a well precision for moving in a specified track in these applications. However, control of these under water bots become very difficult due to the highly non-linear and dynamic characteristics of the underwater world. The logical answer to this problem is the application of non-linear controllers. As neural networks (NNs) are characterized by flexibility and an aptitude for dealing with non-linear problems, they are envisaged to be beneficial when used on underwater robots. In this research our artificial intelligence system is based on neural network model for navigation of an Automated Underwater robot in unpredictable and imprecise environment. Thus the back propagation algorithm has been used for the steering analysis of the underwater robot when it is encountered by a left, right and front as well as top obstacle. After training the neural network the neural network pattern was used in the controller of the underwater robot. The simulation of underwater robot under various obstacle conditions are shown using MATLAB

Mathematical modelling and control of a robotic manipulator : a thesis presented in partial fulfilment of the requirements of the degree of Masters of Technology at Massey University

Control system engineering strives to alter a systems performance to suit the objectives of the user. This requires pre-requisite knowledge of the system behaviour. This is often in the form of mathematical models of the system. These models can then be used to simulate the system and obtain a sound understanding of the systems operation, these can then be used in controller design. Every real world physical system has its own unique characteristics. These must be modelled to develop a simulation of the system. The uniqueness of a real world system necessitates the use of experimental practices and procedures to obtain information about the system. This information is then used to form models representing the system. A simulation of the system can then be based on these models. In this project a robotic system comprising of a link structure, a pneumatic driving system and a valve regulating system, is investigated. Mathematical models describing each component of the robotic system are investigated. Mathematical models describing the dynamic interactions of the link structure are developed and implemented in a fashion to facilitate control of the robot mechanism. The equations are in an explicit form which do not require the use of a numerical method for development of state space equations used in controller development. The pneumatic muscle used as the desired actuator for the robot structure is analysed. Analytical models obtained from the available literature are examined and new models are developed to describe the characteristics of the pneumatic muscle. A proto-type valve specially developed for supplying air to the pneumatic muscle is investigated. Experiments are conducted on this valve to characterise the valves behaviour. A model of the valves behaviour is then developed. A selection of controllers are then applied to the valve pneumatic muscle system. The investigation of alternative actuation systems proposes a new rotary pneumatic muscle design. Analytical models for the rotary pneumatic muscle are developed, a prototype is constructed as part of a feasibility study

Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration

In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows the dynamics model to be fitted with a simple least squares procedure, and the features are identified from a high-level specification of the robot's morphology, consisting of the number and connectivity structure of its links. Model predictive control is then used to choose the actions under an optimistic model of the dynamics, which produces an efficient and goal-directed exploration strategy. We present real time experimental results on standard benchmark problems involving the pendulum, cartpole, and double pendulum systems. Experiments indicate that our method is able to learn a range of benchmark tasks substantially faster than the previous best methods. To evaluate our approach on a realistic robotic control task, we also demonstrate real time control of a simulated 7 degree of freedom arm.Comment: 8 page

Neural Adaptive Control of a Robot Joint Using Secondary Encoders

Using industrial robots for machining applications in flexible manufacturing processes lacks a high accuracy. The main reason for the deviation is the flexibility of the gearbox. Secondary Encoders (SE) as an additional, high precision angle sensor offer a huge potential of detecting gearbox deviations. This paper aims to use SE to reduce gearbox compliances with a feed forward, adaptive neural control. The control network is trained with a second network for system identification. The presented algorithm is capable of online application and optimizes the robot accuracy in a nonlinear simulation

A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems

This paper presents a block oriented nonlinear dynamic model suitable for online identi cation.The model has the well known Hammerstein architecture where as a novelty the nonlinear static part is represented by a B-spline neural network (BSNN), and the linear static one is formalized by an auto regressive exogenous model (ARX). The model is suitable as a feed-forward control module in combination with a classical feedback controller to regulate velocity and position of pneumatic and hydraulic actuation systems which present non stationary nonlinear dynamics. The adaptation of both the linear and nonlinear parts is taking place simultaneously on a patterby- patter basis by applying a combination of error-driven learning rules and the recursive least squares method. This allows to decrease the amount of computation needed to identify the model's parameters and therefore makes the technique suitable for real time applications. The model was tested with a silver box benchmark and results show that the parameters are converging to a stable value after 1500 samples, equivalent to 7.5s of running time. The comparison with a pure ARX and BSNN model indicates a substantial improvement in terms of the RMS error, while the comparison with alternative non linear dynamic models like the NNOE and NNARX, having the same number of parameters but greater computational complexity, shows comparable performances