Olof Sörnmo

Increasing the milling accuracy for industrial robots using a piezo-actuated high-dynamic micro manipulator

The strong process forces arising during highspeed machining operations, combined with the limited stiffness of industrial robots, have hampered the usage of industrial robots in high-end milling tasks. However, since such manipulators may offer flexible and cost-effective machining solutions, a three-dimensional piezo-actuated compensation mechanism, which aims to compensate for the positioning errors of the robot, has earlier been developed. A prototype model-based control scheme for position control of the mechanism, utilizing LQG control, has been proposed. The main contribution of this paper is an experimental verification of the benefit of utilizing the online compensation scheme. We show that the milling accuracy achieved with the proposed compensation mechanism is increased up to three times compared to the uncompensated case.

Increasing Time-Efficiency and Accuracy of Robotic Machining Processes Using Model-Based Adaptive Force Control

Machining processes in the industry of today are rarely performed using industrial robots. In the cases where robots are used, machining is often performed using position control with a conservative feed-rate, to avoid excessive process forces. There is a great benefit in controlling the process forces instead, so as to improve the time-efficiency by applying the maximum allowed force, and thus removing the maximum amount of material per time unit. This paper presents a novel adaptive force controller, based on a derived model of the machining process and an identified model of the robot dynamics. The controller is evaluated in both simulation and an experimental setup. Further, industrial robots generally suffer from low stiffness, which can cause the robot to deviate from the desired path because of strong process forces. The present paper solves this by employing a stiffness model to continuously modify the robot trajectory to compensate for the deviations. The adaptive force controller in combination with the stiffness compensation is evaluated in experiments, with satisfying results.

Modeling and control of a piezo-actuated high-dynamic compensation mechanism for industrial robots

This paper presents a method for 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. The machining spindle was attached to the compensation mechanism and the robot held the workpiece. Due to the inherent resonant character of mechanical constructions of this type, and the nonlinear phenomena appearing in piezo actuators, control of the compensation mechanism is a challenging problem. This paper presents models of the construction, experimentally identified using subspace-based identification methods. A subsequent control scheme, based on the identified models, utilizing state feedback for controlling the position of the spindle is outlined. Results from experiments performed on a prototype of the compensation mechanism are also provided.

Robot Joint Modeling and Parameter Identification Using the Clamping Method

The usage of industrial robots for milling tasks is limited by their lack of absolute accuracy in presence of process forces. While there are techniques and products available for increasing the absolute accuracy of free-space motions, the mechanical weaknesses of the robot in combination with the milling forces limits the achievable performance. If the dynamic effects causing the deviations can be compensated for, there would be several benefits of using industrial robots for machining applications. To enable the compensation, the causes of the path deviations have to be adequately modeled, and there must be a method for determining the model parameters in a simple and inexpensive way. To that end, we propose a radically new method for identification of robot joint model parameters, based on clamping of the robot to a rigid environment. The rigidity of the environment then eliminates the need for expensive measurement equipment, and the internal sensors of the robot give sufficient feedback. An experimental validation shows the feasibility of the method.

Adaptive internal model control for mid-ranging of closed-loop systems with internal saturation

This paper considers the problem of performing mid-ranging control of two closed-loop controlled systems that have internal saturations. The problem originates from previous work in machining with industrial robots, where an external compensation mechanism is used to compensate for position errors. Because of the limited workspace and the considerably higher bandwidth of the compensator, a mid-ranging control approach is proposed. An adaptive, modelbased solution is presented, which is verified through simulations and experiments, where a close correspondence of the obtained results is achieved. Comparing the IAE of experiments using the proposed controller to previously established methods, a performance increase of up to 56 % is obtained.

Increasing the Accuracy for a Piezo-Actuated Micro Manipulator for Industrial Robots using Model-Based Nonlinear Control

We consider the problem of modeling and control of the nonlinear dynamics of a micro manipulator, utilized for machining operations in combination with industrial robots. Position control of the micro manipulator is a challenging problem because of the actuation principle, which is based on piezo-actuators with inherent nonlinear behavior. The major nonlinearities in the manipulator are identified and explicitly modeled in this paper. Different model structures are outlined and subsequent identification experiments are performed and evaluated. The obtained models form the basis for a combined feedforward and feedback control scheme for accurate position control. Experimental results obtained with the developed control scheme are presented and discussed. We show that the accuracy of the controller is increased significantly with the proposed scheme, compared to a linear controller.

Iterative Learning Control for Machining with Industrial Robots

We consider an iterative learning control (ILC) approach to machining with industrial robots. The robot and the milling process are modeled using system identification methods with a data-driven approach. Two different model-based ILC algorithms are proposed and subsequently experimentally verified in a milling scenario. The difference between the two approaches is the required sensors for acquiring relevant input data for the algorithms. The results from the experiments indicate that the proposed methods have the potential of significantly decreasing the position errors in robotic machining, up to 85% in the considered milling scenario.

Learning Approach to Cycle-Time-Minimization of Wood Milling Using Adaptive Force Control

A majority of the machining processes in the industry of today is performed using position-controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermore, path planning with respect to time-minimization for milling processes, especially in non-isotropic materials, is not straightforward. This paper presents a model-based adaptive force controller that achieves optimal feed rates, in combination with a learning algorithm to obtain the optimal machining path, in terms of minimizing the milling duration. The proposed solution is evaluated in both simulation and experiments, where an industrial robot is used to perform rough-cut wood-milling. Cycle-time reductions of 14% using force control compared to position control were achieved, and on average an additional 28% cycle-time reduction with the proposed learning algorithm.

Continuous-time gray-box identification of mechanical systems using subspace-based identification methods

We consider the problem of gray-box identification of dynamic models for mechanical systems. In particular, the problem is approached by means of continuous-time system identification using subspace-based methods based on discrete-time input-output data. A method is developed, with the property that the structure of the model resulting from fundamental physical first principles is obtained and the parameter matrices have a clear physical interpretation. The proposed method is subsequently successfully validated in both simulation and using experimental data from a three-axis manipulator. In both cases the identified models exhibit good fit to the input-output data. The results indicate that the proposed method can be useful in the context of model-based control design in, for example, impedance force control for robots and manipulators, but also for modal analysis of mechanical systems.

Force controlled knife-grinding with industrial robot

This paper investigates the application of sharpening knives using a force controlled industrial robot, for an arbitrary knife shape and orientation. The problem is divided into different parts: calibration of the knife by identifying its unknown orientation, identification of the knife blade contour and estimation of its position in the robot frame through force control, and grinding of the knife, following the path defined by the earlier identified shape, while applying the desired contact force to the revolving grinding wheels. The experimental results show that the knives can be sharpened satisfactorily. An industrial application has also been developed and tested, and it has produced a sharpening quality equal or greater to that achieved manually.