Itthisek Nilkhamhang

Model-based adaptive friction compensation for accurate position control

An adaptive friction compensator for position control is proposed using the generalized Maxwell-slip (GMS) friction model, with a new, linearly-parameterized Stribeck function. It employs a polynomial equation that is linear-in-the-parameter to approximate the nonlinear Stribeck effect in the GMS model, and simplifies the design of the adaptive friction compensator. The proposed compensator has a switching structure to accomodate for the hybrid nature of the GMS model, and contains a robustifying term to account for unmodelled dynamics. The stability of the proposed adaptive algorithm is analyzed and its stability conditions are clarified. The validity and effectiveness of the proposed, linearly-parameterized friction compensator is verified by simulations for the positional control of an inertia system under the influence of dynamic friction.

Adaptive friction compensation using the GMS model with polynomial stribeck function

An adaptive friction compensator is proposed using the generalized Maxwell-slip (GMS) friction model, with a new, linearly-parameterized Stribeck function. It employs a polynomial equation that is linear-in-the-parameter to describe the nonlinear Stribeck effect in the GMS model, and simplifies the design of an adaptive friction compensator. The proposed compensator has a switching structure to accommodate for the hybrid nature of the GMS model, and the parameter projection method is used to guarantee a boundedness on parameter estimates and stability of the switching adaptive controller. The validity and effectiveness of the proposed, linearly-parameterized friction compensator is verified by simulations for the velocity control of an inertia system under the influence of dynamic friction

Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network

An inverse controller is proposed for a magnetorheological (MR) damper that consists of a hysteresis model and a voltage controller. The force characteristics of the MR damper caused by excitation signals are represented by a feedforward neural network (FNN) with an elementary hysteresis model (EHM). The voltage controller is constructed using another FNN to calculate a suitable input signal that will allow the MR damper to produce the desired damping force. The performance of the proposed EHM-based FNN controller is experimentally compared to existing control methodologies, such as clipped-optimal control, signum function control, conventional FNN, and recurrent neural network with displacement or velocity inputs. The results show that the proposed controller, which does not require force feedback to implement, provides excellent accuracy, fast response time, and lower energy consumption.

Force estimation using piezoelectric actuator with adaptive control

This paper presents a method to estimate external force which is applied at the tip of a robotic arm. Piezoelectric actuator has been used as linear motor into this system and provide force estimation. The disturbance system has been divided into two parts: external force and design trajectory tracking. An external force is applied to the piezoelectric linear model to estimate current external force, without force sensor which is normally used in telesurgery or micro robotics. The displacement can be controlled by feedback signal from adaptive control to track the design trajectory tracking displacement. If the unknown parameters in this system, this adaptive controller can also approximate unknown parameters and make system stable. Sensorless force estimation is applied with four controllers to compare the results.

Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization

In recent years, intelligent prosthetic knees have been developed that enable amputees to walk as normally as possible when compared to healthy subjects. Although semi-active prosthetic knees utilizing magnetorheological (MR) dampers offer several advantages, they lack the ability to generate active force that is required during some states of a normal gait cycle. This prevents semi-active knees from achieving the same level of performance as active devices. In this work, a new control algorithm for a semi-active prosthetic knee during the swing phase is proposed to reduce this gap. The controller uses neural network predictive control and particle swarm optimization to calculate suitable command signals. Simulation results using a double pendulum model show that the generated knee trajectory of the proposed controller is more similar to the normal gait than previous open-loop controllers at various ambulation speeds. Moreover, the investigation shows that the algorithm can be calculated in real time by an embedded system, allowing for easy implementation on real prosthetic knees.

Adaptive Compensation of a Linearly-Parameterized GMS Friction Model with Parameter Projection

A new, linearly-parameterized dynamic friction model is proposed based upon the generalized Maxwell-slip (GMS) model. This model is capable of describing essential frictional characteristics, such as the Stribeck effect, hysteresis, stick-slip limit cycling, frictional lag, non-drifting properties, and non-local memory. It replaces the traditional, nonlinear Stribeck function with a new function that is linearly-parameterized, and simplifies the design of an adaptive friction compensator. The proposed compensator has a switching structure to accommodate for the hybrid nature of the GMS model, and the parameter projection method is used to guarantee a bound on parameter estimates and stability of the switching adaptive controller. The validity and effectiveness of the proposed, linearly-parameterized friction compensator is verified by simulations for the velocity control of an inertia system under the influence of dynamic friction

Adaptive semi-active vibration isolation considering uncertainties of MR damper and suspension structure

The paper is concerned with a fully adaptive semiactive control scheme which can deal with uncertainties in both models of MR damper and suspension mechanism. The proposed approach consists of two adaptive control algorithms: One is an adaptive inverse control for compensating the nonlinear hysteresis dynamics of the MR damper, which can be realized by identifying a forward model of MR damper and then calculating the input voltage to MR damper to generate a reference damping force. It can also be realized directly by updating an inverse model of MR damper without identification of the forward model, which can directly work as an adaptive inverse controller. The other is an adaptive reference control which gives the desired damping force to match the seat dynamics to a specified reference dynamics even in the presence of uncertainties in the suspension structure. The stability of the total system including the two adaptation algorithms is discussed and its stability condition is explored. Validity of the proposed algorithm is also examined in simulation studies.

Informative frame classification method for real-time analysis of colonoscopy video

The proposed classification method in this paper was introduced to classify the images extracted from colonoscopy videos into two categories: informative frames and non-informative frames. The classification method consists of two steps. In the first step, the Isolated Pixel Ratio (IPR) can distinguish the images into three categories: informative frames , non-informative frames and ambiguous frames. In the second step, the total pixels were used to identify the ambiguous frames into either informative or non-informative frames. After the input image frames have been classified into two categories, the non-informative ones will be discarded before being sent to the surgeon for diagnosis. This will reduce the number of frames to be transmitted over distance between the surgeon side and the patient side in tele-surgery which will enhance quality of transmission and achieve higher speed for video transmission.

Adaptive semi-active control of suspension system with MR damper

Abstract The paper is concerned with a fully adaptive semi active control which can deal with uncertainties in both models of MR damper and suspension mechanism. The proposed approach consists of two adaptive control: One is an adaptive inverse control for compensating the nonlinear hysteresis dynamics of the MR damper, which can be realized by identifying a forward model of MR damper and then calculating the input voltage to MR damper to generate a reference damping force. It can also be realized directly by updating the inverse model of MR damper without identification of forward model, which works as an adaptive inverse controller. The other is an adaptive reference control which gives the desired damping force to match the seat dynamics to a specified reference dynamics even in the presence of uncertainties in the suspension system. Validity of the proposed algorithm is discussed in simulation studies.

An Adaptive Nonlinearity Compensation Scheme for OFDM Communication Systems

An adaptive predistortion linearization is proposed to compensate for the nonlinear distortions induced by high power amplifiers (HPAs) in orthogonal frequency division multiplexing (OFDM) systems. By expressing the uncertain nonlinear input-output dynamics of HPA by a new simplified Volterra series model (VSM), a predistorter (i.e., compensator) is constructed based on identification of inverse system of the HPA. Once predistorters parameters are obtained by RLS algorithm, when OFDM signals pass through the cascade system of the predistorter and the HPA, overall linearization from predistorter input to HPA output can be achieved even if HPA is uncertain. The effectiveness of the proposed predistortion method is validated by numerical simulation for 64-ary quadrature amplitude modulation OFDM (64QAM-OFDM) transmission systems