Identification and model-based compensation of Striebeck friction

The paper deals with the measurement, identification and compensation of low velocity friction in positioning systems. The introduced algorithms are based on a linearized friction model, which can easily be introduced in tracking control algorithms. The developed friction measurement and compensation methods can be implemented in simple industrial controller architectures, such as microcontrollers. Experimental measurements are provided to show the performances of the proposed control algorithm

CAD enabled trajectory optimization and accurate motion control for repetitive tasks

As machine users generally only define the start and end point of the movement, a large trajectory optimization potential rises for single axis mechanisms performing repetitive tasks. However, a descriptive mathematical model of the mecha- nism needs to be defined in order to apply existing optimization techniques. This is usually done with complex methods like virtual work or Lagrange equations. In this paper, a generic technique is presented to optimize the design of point-to-point trajectories by extracting position dependent properties with CAD motion simulations. The optimization problem is solved by a genetic algorithm. Nevertheless, the potential savings will only be achieved if the machine is capable of accurately following the optimized trajectory. Therefore, a feedforward motion controller is derived from the generic model allowing to use the controller for various settings and position profiles. Moreover, the theoretical savings are compared with experimental data from a physical set-up. The results quantitatively show that the savings potential is effectively achieved thanks to advanced torque feedforward with a reduction of the maximum torque by 12.6% compared with a standard 1/3-profil

A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM

Dynamic parameters identification of a haptic interface for a helicopter flight simulator

Abstract. The haptic interface force feedback is one of the key factors for a reliable flight simulation. This paper addresses the design and control implementation of a simple joystick-like haptic interface to be used for a helicopter flight simulator. The expression of the haptic interface force is obtained by dynamic analysis of the haptic interface operation. This paper proposes a new strategy aiming at avoiding the use of an expansive and complex force/torque sensor. Accordingly, specific dynamic model is implemented by including Stribeck friction to describe the friction moment. Experimental data are processed as based on a genetic algorithm for identifying the dynamic parameters in the Stribeck friction model. This allows to obtain the friction moment parameters of the haptic interface, as well as the torque distribution due to gravity and the rotational inertia parameters of the haptic interface for the calculation of the haptic interface force. Experimental tests are carried out and results are used to validate the proposed dynamic model and dynamic parameter identification method and demonstrate the effectiveness of the proposed force feedback while using a cheap photoelectric sensor instead of an expansive force/torque sensor

Fault Detection and Identification Method Based on Genetic Algorithms to Monitor Degradation of Electrohydraulic Servomechanisms

Electro Hydraulic Actuators (EHAs) keep their role as the leading solution for the control of current generation primary flight control systems: the main reason can be found in their high power to weight ratio, much better than other comparable technologies. To enhance efficiency and reliability of modern EHAs, it is possible to leverage the diagnostics and prognostics disciplines; these two tools allow reducing life cycle costs without losing reliability, and provide the bases for health management of integrated systems, in compliance with regulations. This paper is focused on the development of a fault detection algorithm able to identify the early signs of EHA faults, through the recognition of their precursors and related degradation patterns. Our methodology provides the advantage of anticipating incoming failures, triggering proper alerts for the maintenance team to schedule adequate corrective actions, such as the replacement of the degraded component. A new EHA model-based fault detection and identification (FDI) method is proposed; it is based on deterministic and heuristic solvers able to converge to the actual state of wear of the tested actuator. Three different progressive failure modes were chosen as test cases for the proposed FDI strategy: clogging of the first stage of the flapper-nozzle valve, spool-sleeve friction increase, and jack-cylinder friction increase. A dedicated simulation model was created for the purpose. The results highlighted that the method is adequate in robustness, since EHA malfunctions were identified with a low occurrence of false alarms or missed failures

Optimal control design for robust fuzzy friction compensation in a robot joint

This paper presents a methodology for the compensation of nonlinear friction in a robot joint structure based on a fuzzy local modeling technique. To enhance the tracking performance of the robot joint, a dynamic model is derived from the local physical properties of friction. The model is the basis of a precompensator taking into account the dynamics of the overall corrected system by means of a minor loop. The proposed structure does not claim to faithfully reproduce complex phenomena driven by friction. However, the linearity of the local models simplifies the design and implementation of the observer, and its estimation capabilities are improved by the nonlinear integral gain. The controller can then be robustly synthesized using linear matrix inequalities to cancel the effects of inexact friction compensation. Experimental tests conducted on a robot joint with a high level of friction demonstrate the effectiveness of the proposed fuzzy observer-based control strategy for tracking system trajectories when operating in zero-velocity regions and during motion reversals

Optimization algorithms for prognostics of electrohydraulic on-board servomechanisms

This paper studies the response of an electrohydraulic actuator (EHA) subjected to three different progressive failures (demagnetization of the torque motor, increment of the jack static friction and presence of backlash); in particular, it is focused on the identification of failure precursors able to give an early identification of progressive failures affecting the system, in order to provide tools that can be used to predict its remaining useful life. This kind of analysis belongs to a new discipline, called Prognostics and Health Management (PHM), that focuses on predicting the time at which a system or a component will no longer perform its intended function, estimating its Remaining Useful Life (RUL) and, then, providing an effective diagnostic tool that allows them to exploit a component until it is safe, saving money. In order to conceive an effective prognostic algorithm authors studied the failures effects on the system behaviors, identifying some details in the monitored time-history signals that exclusively got evidence of a particular failure, avoiding confounding each other and allowing pointing out the fault level of the system. For this purpose, the authors developed a new EHA Monitor Model able to reproduce the dynamic response of the actual system in terms of position, speed and equivalent current, even with the presence of incipient faults. Starting from this Monitor Model, the authors propose a new model-based fault detection and identification (FDI) method, based on Genetic Algorithms (GAs) optimization approach and parallelized calculations, investigating its ability to timely identify symptoms alerting that a component is degrading

PARAMETER IDENTIFICATION OF A ROBOT ARM USING GENETIC ALGORITHMS

An identification method for inverse dynamics of a robot arm based on genetic algorithms (GA) is considered. It is shown that GAs are able to find robot parameters effectively even if the robot has low resolution position encoders. It is possible because the method only requires position feedback and there is no need to find out the speed and acceleration of the links that usually can only be done through finite differences calculations that cause dramatic errors during identification. The effectiveness of the algorithm is demonstrated on the example of parameter identification of the real robot PUMA 560 (for second and third links)