A Generic Method to Generate AS-Curve Profile in Commercial Motion Controller

It is well believed that S-curve motion profiles are able to reduce residual vibration, and are widely applied in the motion control fields. Recently, a new asymmetric S-curve (AS-curve) motion profile, which is able to effectively adjust the acceleration and deceleration periods, is proposed to enhance the performance of S-curve motion profile, and proved to be better than the traditional symmetric S-curve in many cases. However, most commercial motion controllers do not support the AS-curve motion profiles inherently. Special knowledge or expensive advanced controlling systems, such as dSPACE system, are required to generate the AS-curve motion command, which limits the applications of the AS-curve motion profile in many practical applications. In this paper, a generic method based on the Position-Velocity-Time (PVT) mode move supported by most commercial motion controllers is proposed to generate exact AS-curve motion command in real machines. The analytic polynomial functions of AS-curve motion profile are also derived to simplify the further application, and the effectiveness of the proposed method is verified by numerical simulation.

A Survey of Fractional-Order Neural Networks

In this paper, the literature of fractional-order neural networks is categorized and discussed, which includes a general introduction and overview of fractional-order neural networks. Various application areas of fractional-order neural networks have been found or used, and will be surveyed and summarized such as neuroscience, computational science, control and optimization. Recent trends in dynamics of fractional-order neural networks are presented and discussed. The results, especially the stability analysis of fractional-order neural networks, are reviewed and different analysis methods are compared. Furthermore, the challenges and conclusions of fractional-order neural networks are given.

Lower-Order Active Disturbance Rejection Control and Frequency Analysis of High Order System

Linear active disturbance rejection control (LADRC) has been paid much attention in academic and industrial fields. However, the selection of the order for LADRC controller design and the choice of parameter b0 in system correcting are key facts which are still being faced by designer. In this paper, an effective method about how to select the order of LADRC and the parameter b0 is given first. Frequency analysis often used by engineers for designing controller, the normal transfer function form of LADRC is constructed, so the loop gain, close loop transfer function and disturbance transfer function to a general high-order system are presented and can be easily used. The example shows that the proposed method is easy to apply and verified the lower-order LADRC can obtain the better effective than PID and high-order LADRC, and the frequency response analysis of a thermal power plant is elaborated and the simulation result indicates that LADRC has a strong robustness against the large variation of parameters in the plant mode.

Extended State Observer Based Ascent Trajectory Tracking Method

The ascent trajectory tracking problem of a launch vehicle is investigated in this paper. To improve the conventional trajectory linearization method which usually omits the linearization errors, the extended state observer (ESO) is employed in this paper to timely estimate the total disturbance which consists of the external disturbances and the modeling uncertainties resulting from linearization error. It is proven that the proposed trajectory tracking controller can guarantee the desired performance despite both external disturbances and the modeling uncertainties. Moreover, compared with the conventional linearization control method, the proposed controller is shown to have much better performance of uncertainty rejection. Finally, the feasibility and performance of this controller are illuminated via simulation studies.

An Attention-Based Framework for Context Identification in Autonomous Robots

Being able to correctly assess the context it is currently acting in is a very important ability for every autonomous robot performing a task in a real world scenario such as navigating, manipulating an object or interacting with a user. Sensors are the primary interface with the external world and the means through which contextual knowledge is generated. Humans and animals use cognitive processes such as attention to selectively process perceived task-relevant information and to recognize the context they are currently acting in. Biologically inspired computational models of attention have been developed in recent years to be used as interpretation keys of mainly visual sensor data. This paper presents a new framework for situation assessment that expands existing computational models of attention by providing a unified methodology to interpret and combine data from different sources. The method utilizes probabilistic state estimation techniques such as Bayesian recursive estimation, Kalman filter, and hidden Markov models to interpret features extracted from sensor data and formulate hypotheses about different aspects of the task the robot is performing or of the environment it is currently acting in. The concept of Bayesian surprise is also used to mark the information content of each new hypothesis. A weight that takes into account the confidence in the estimate that generated the hypothesis, its information content, and the quality of the data is then calculated. The methodology presented in this paper is general and allows to consistently apply the framework to data from different types of sensors and to then combine their hypotheses. Once formulated, hypotheses can then be used for context-based reasoning and plan adaptation. The framework was implemented on a small two-wheel differential drive robot equipped with a camera, an ultrasonic and two infrared range sensors. Three different sets of results that evaluate the performance of different features of the framework are presented. First, the method has been applied to detect a target object and to distinguish it from similar objects. Second, the hypotheses strength calculation method has been characterized by isolating the effect of belief, surprise, and of the quality of the data. Third, the combination of hypotheses from different modules has been evaluated in the context of environment classification.

Generalized Disturbance Observer Synthesis Using a Two-Stage Heuristic Algorithm

Disturbance observer (DOB) based control has been widely applied in industries due to its easy usage but powerful disturbance rejection ability. However, the existence of innate structure constraint, namely the inverse of the nominal plant, prevents its implementation on more general class of systems, such as non-minimum phase plants, MIMO systems etc.. Furthermore, additional limitations exerted on Q-filter design, i.e., unity steady state gain and low-pass nature, which narrow down its solution space largely and prevent from achieving optimal performance even if it exists. In this paper, we present a novel DOB architecture, named generalized disturbance observer (G-DOB), with the help of nontraditional use of the celebrated Youla parametrization of two degree-of-freedom controller. Rigorous analyses show that the novel G-DOB not only inherits all the merits of the conventional one, but also alleviates the limitations stated before partially. By some appropriate system manipulation, the synthesis of Q-filter has been converted to the design of reduced-order controller. Thus, a heuristic two-stage algorithm has been developed with the help of Kalman-Yakubovich-Popov (KYP) lemma: firstly design a full information controller for the augmented system and then compute a reduced-order controller. Numerical examples are presented to demonstrate the effectiveness of the proposed G-DOB structure and design algorithm.

A Novel Threshold Pressure Sensor Based on Nonlinear Dynamics of MEMS Arches

In this paper, we propose a new tunable pressure sensor based on the nonlinear snap-through instability of an electrically actuated shallow arch microbeam. The general concept of the sensor can be explained as follows: the shallow arch is excited to trigger dynamic snap-through instability yielding a high output amplitude, if the system operating pressure is below a threshold value. This state is interpreted as a digital logic 1. Once the varying pressure exceeds that threshold value, the arch gains its stability. Therefore, the new state would be interpreted as a digital logic 0 value. We show an example of an operation range of the proposed sensor by identifying the relationship between the excitation AC voltage and the critical cut-off pressure.

The Inverted Decoupling Based Fractional Order Two-Input-Two-Output IMC Controller

Many processes in the industry can be modeled as fractional order, research on the fractional order become more and more popular. Usually, controllers such as fractional order PID (FOPID) or fractional active disturbance rejection control (FADRC) are used to control single-input-single-output (SISO) fractional order system. However, when it comes to fractional order two-input-two-output (TITO) processes, few research focus on this. In this paper, a new design method for fractional order control based on multivariable non-internal model control with inverted decoupling is proposed to handle non-integer order two-input-two-output system. The controller proposed in this paper just has two parameters to tune compared with the five parameters of the FOPID controller, and the controller structure can be achieved by internal model control (IMC) method which means it is easy to implement. The parameters tuning method used in this paper is based on frequency domain strategy. Compared with integer order situation, fractional order method is more complex, because the calculation of the frequency domain characteristics is difficult. The controller proposed in this paper is robust to process gain variations, what’s more, it provides ideal performance for both set point-tracking and disturbance rejection. Numerical results are given to show the performance of the proposed controller.

Research of Permanent Magnet Synchronous Motor Sensorless Control Based on Fractional Order PLL

For those surface mounted permanent magnet synchronous motors (PMSM), a fractional order phase lock loop (FO-PLL) sensorless control method is proposed. On the basis of PMSM model in stationary coordinate, a rotor flux linkage observer is constructed. By designing the modified integrator and introducing the high pass filter, output saturation distortion or numeric overflow caused by integrator zero drift are solved. The observing results have no DC component. Moreover, a fractional order PLL is designed to estimate the PMSM rotor angle and speed. Parameters of FO-PLL controller are tuned and optimized via ITAE criterion. System indiscrimination degree is improved effectively. By using Oustaloup recursive filter, high order integer order approximation to fractional order integrator is realized. Last, the effectiveness and engineering application of the proposed method are verified on a MATLAB based PMSM control simulation platform.

Adding Channel Security to a Fingerprint Verification Chain

Authenticating persons using fingerprints is a widely accepted method in the field of access control, border control, prosecution and many others. Today, fingerprint modules with customizable firmware can be bought commercially off the shelf by hobbyists and small companies to be used in their applications and are usually locally separated from a controller implementing the feature extraction and comparison algorithms. As a matter of fact, the communication channel between the sensor and the controller module is susceptible to eavesdropping and man in the middle attacks. Nevertheless, adding communication channel security to such a system has a direct negative impact on the system’s response time, thus directly affecting user acceptance. The aim of this paper is to provide a comprehensive investigation on measures to counter run-time degredation when adding communication channel security on behalf of an existing fingerprint verification chain. We show that a combination of the elliptic curve Diffie-Hellman key exchange together with AES-256 and the use of parallelization using OpenMP on a controller node leads to an acceptable run time making key creation and exchange upon every fingerprint read request a suitable undertaking.