Non-linear velocity observer for vehicles with tyre–road friction estimation

A non-linear observer is proposed for the estimation of the longitudinal and lateral velocities of automotive vehicles due to highly non-linear friction. To take the unknown time-varying road conditions into account, a real-time tyre–road friction estimation algorithm is provided for adaptation to changes of different road adhesion characteristics. Besides, the coupling effects of the longitudinal and lateral forces are fully exploited to design the non-linear observer based on the longitudinal and lateral acceleration measurements. The observer is computationally efficient and is based on a standard sensor configuration commonly available in modern cars. The uniform global asymptotical stability of the adaptive observer is guaranteed under a certain persistency-of-excitation condition. Moreover, a stronger local stability result can also be obtained, i.e. uniform local exponential stability. The performance of the observer is compared with that of existing approaches under different manoeuvres and road conditions.     

Spinning frequency estimation algorithm of MEMS gyro’s output signal based on FFT coefficient

Microsystem Technologies - To obtain accurate demodulation of the spinning frequency of MEMS carrier-driven gyro output signal, a frequency estimation algorithm combining the real and imaginary...     

Sheet metal forming global control system based on artificial vision system and force–acoustic sensors

Forming processes are manufacturing processes based on deformation of raw material applying pressure in one or several stages until getting the final product. This process depends on many factors, e.g. process parameters, material properties or lubrication, leading to possible defective parts. Correct forming of parts is very important as any defective part may result in big economical losses, e.g. the return of a complete set of parts or the loss of some clients. Thus, in our European Craft Pro2Control project, leading German, French, Italian and Spanish companies, universities and forming industries are defining and implementing a zero-defect forming control system, minimizing costs and maximizing the throughput of parts.     

A comparison of algorithms for origin–destination matrix generation on real road networks and an approximation approach

This paper considers the generation of the origin–destination (OD) matrix, basic data in any vehicle routing or traveling salesman problem. An OD matrix must be generated by calculating the shortest paths between some nodes. Candidate methods for this are repetitive use of one-to-all shortest path algorithms such as Dijkstra’s algorithm, use of all-to-all shortest path algorithms such as the Floyd–Warshall algorithm, and use of specifically designed some-to-some shortest path algorithms. This paper compares the performance of several shortest path algorithms in computing OD matrices on real road networks. Dijkstra’s algorithm with approximate bucket data structure performed the best for most of the networks tested. This paper also proposes a grouping-based algorithm for OD matrix generation. Although it is an approximation approach, it has several good characteristics: it can find the exact shortest distances for most OD pairs; it guarantees that all the calculated shortest path distance values have corresponding paths; the deviation of any distance from the corresponding true shortest distance is small; and its computation time is short.     

Microinstrument contact force sensing based on cable tension using BLSTM–MLP network

Minimally invasive surgical robotic systems established the foundation for precise and refined surgery, and the intelligentization of robotic systems is an important direction for future development. Among the methods of intelligentization, microinstrument external force sensing is an open and challenging research area. Force sensing information is used not only to ensure that surgeons apply the appropriate amount of force but also to prevent unintentional tissue damage. Because a microinstrument is a compact and small-sized construction, indirect force sensing method instead of the integration of sensors into the microinstrument is used, yielding better biocompatibility, sterilizability and monetary cost savings. This paper focuses on microinstrument-tissue contact force sensing, and the microinstrument used is a three degrees of freedom cable-driven manipulator. A contact force estimation strategy based on the differences in cable tension is established with consideration of the kinematics, dynamics and friction of the manipulator. A principle prototype of a surgical microinstrument force measurement system is developed, and then zero-drift, hysteresis and force loading experiments are studied. Based on the experimental data of the force loading experiments, the relationship between cable tension and contact forces is established by using a bidirectional long short-term memory plus multilayer perceptron network. The results show that the L2 cost of the network in the training set converges to 0.006 and that the RMSE of the network in the testing set converges to 0.053, and the network can meet the measurement requirements without overfitting. Therefore, the indirect force estimation method is a viable method of measuring forces of cable-driven microinstrument and can be used to integrate force sensing information into surgical robotic systems to improve the operability of surgical robots.

     

Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network

This study focuses on the accurate tracking control and sensorless estimation of external force disturbances on robot manipulators. The proposed approach is based on an adaptive Wavelet Neural Network (WNN), named Adaptive Force-Environment Estimator (WNN-AFEE). Unlike disturbance observers, WNN_AFEE does not require the inverse of the Jacobian transpose for computing the force, thus, it has no computational problem near singular points. In this scheme, WNN estimates the external force disturbance to attenuate its effects on the control system performance by estimating the environment model. A Lyapunov based design is presented to determine adaptive laws for tuning WNN parameters. Another advantage of the proposed approach is that it can estimate the force even when there are some parametric uncertainties in the robot model, because an additional adaptive law is designed to estimate the robot parameters. In a theorem, the stability of the closed loop system is proved and a general condition is presented for identifying the force and robot parameters. Some suggestions are provided for improving the estimation and control performance. Then, a WNN-AFEE is designed for a planar manipulator as an example, and some simulations are performed for different conditions. WNN_AFEE results are compared attentively with the results of an adaptive force estimator and a disturbance estimator. These comparisons show the efficiency of the proposed controller in dealing with different conditions.     

Rigid body attitude estimation based on the Lagrange–d’Alembert principle

Estimation of rigid body attitude and angular velocity without any knowledge of the attitude dynamics model is treated using the Lagrange–d’Alembert principle from variational mechanics. It is shown that Wahba’s cost function for attitude determination from two or more non-collinear vector measurements can be generalized and represented as a Morse function of the attitude estimation error on the Lie group of rigid body rotations. With body-fixed sensor measurements of direction vectors and angular velocity, a Lagrangian is obtained as the difference between a kinetic energy-like term that is quadratic in the angular velocity estimation error and an artificial potential obtained from Wahba’s function. An additional dissipation term that depends on the angular velocity estimation error is introduced, and the Lagrange–d’Alembert principle is applied to the Lagrangian with this dissipation. A Lyapunov analysis shows that the state estimation scheme so obtained provides stable asymptotic convergence of state estimates to actual states in the absence of measurement noise, with an almost global domain of attraction. These estimation schemes are discretized for computer implementation using discrete variational mechanics. A first order Lie group variational integrator is obtained as a discrete-time implementation. In the presence of bounded measurement noise, numerical simulations show that the estimated states converge to a bounded neighborhood of the actual states.     

Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation

Most of the reported prognostic techniques use a small number of condition indicators and/or use a thresholding strategies in order to predict the remaining useful life (RUL). In this paper, we propose a reliability-based prognostic methodology that uses condition monitoring (CM) data which can deal with any number of condition indicators, without selecting the most significant ones, as many methods propose. Moreover, it does not depend on any thresholding strategies provided by the maintenance experts to separate normal and abnormal values of condition indicators. The proposed prognostic methodology uses both the age and CM data as inputs to estimate the RUL. The key idea behind this methodology is that, it uses Kaplan–Meier as a time-driven estimation technique, and logical analysis of data as an event-driven diagnostic technique to reflect the effect of the operating conditions on the age of the monitored equipment. The performance of the estimated RUL is measured in terms of the difference between the predicted and the actual RUL of the monitored equipment. A comparison between the proposed methodology and one of the common RUL prediction technique; Cox proportional hazard model, is given in this paper. A common dataset in the field of prognostics is employed to evaluate the proposed methodology.     

Channel estimation based on neural network in space time block coded MIMO–OFDM system

In this study, we propose feed-forward multilayered perceptron (MLP) neural network trained with the Levenberg–Marquardt algorithm to estimate channel parameters in MIMO–OFDM systems. Bit error rate (BER) and mean square error (MSE) performances of least square (LS) and least mean square error (LMS) algorithms are also compared to our proposed neural network to evaluate the performances. Neural network channel estimator has got much better performance than LS and LMS algorithms. Furthermore it doesn?t need channel statistics and sending pilot tones, contrary to classical algorithms.     

MSEBAG: a dynamic classifier ensemble generation based on ‘minimum-sufficient ensemble' and bagging

In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the ‘minimum-sufficient ensemble’ and bagging at the ensemble level. It adopts an ‘over-generation and selection’ strategy and aims to achieve a good bias–variance trade-off. In the training phase, MSEBAG first searches for the ‘minimum-sufficient ensemble’, which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the ‘minimum-sufficient ensemble’, a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.     

Adaptive control based on IF–THEN rules for grasping force regulation with unknown contact mechanism

An industrial gripping application with unknown contact mechanism is considered as a class of unknown nonlinear discrete-time systems. The control scheme is developed by an adaptive network called multi-input fuzzy rules emulated network (MiFREN) within discrete-time domain. The network structure is directly constructed regarding to IF–THEN rules related to gripper and contact mechanism properties. All adjustable parameters require only the on-line learning phase to improve the closed loop performance. The time varying learning rate is devised for gradient reach with the proof of stability analysis. Furthermore, the estimated sensitivity of system dynamic is directly considered within the parameter adaptation. The experimental system with an industrial parallel grip model WSG-50 validates the performance of the proposed controller.     

Sample size determination for paired right-censored data based on the difference of Kaplan–Meier estimates

Sample size determination is essential to planning clinical trials. Jung (2008) established a sample size calculation formula for paired right-censored data based on the logrank test, which has been well-studied for comparing independent survival outcomes. An alternative to rank-based methods for independent right-censored data, advocated by Pepe and Fleming (1989), tests for differences between integrated weighted Kaplan–Meier estimates and is more sensitive to the magnitude of difference in survival times between groups. In this paper, we employ the concept of the Pepe–Fleming method to determine an adequate sample size by calculating differences between Kaplan–Meier estimators considering pair-wise correlation. We specify a positive stable frailty model for the joint distribution of paired survival times. We evaluate the performance of the proposed method by simulation studies and investigate the impacts of the accrual times, follow-up times, loss to follow-up rate, and sensitivity of power under misspecification of the model. The results show that ignoring the pair-wise correlation results in overestimating the required sample size. Furthermore, the proposed method is applied to two real-world studies, and the R code for sample size calculation is made available to users.     

Flexible humidity and temperature sensor based on cellulose–polypyrrole nanocomposite

Recently, cellulose has been re-discovered as a smart material that can be used as sensor and actuator materials, which is termed as electro-active paper. In this work we demonstrate the application of cellulose as a flexible humidity and temperature sensor. Nanoscaled polypyrrole (PPy) as a humidity and temperature sensitive layer was introduced onto cellulose surface via in situ polymerization technique without disrupting cellulose structure. Atomic force microscopy, UV–visible spectroscopy, transmission electron microscopy and secondary ion mass spectroscopic analysis revealed the successful deposition of polypyrrole nanolayer onto cellulose surface, which is referred as a cellulose–PPy nanocomposite. Effect of polymerization time on sensing behavior of cellulose–PPy nanocomposite was investigated, experimental results revealed that cellulose–PPy nanocomposite with 16 h polymerization time suitable for suitable for humidity and temperature sensor.     

Gas and humidity sensors based on iron oxide–polypyrrole nanocomposites

Nanocomposites of iron oxide and polypyrrole were prepared by simultaneous gelation and polymerization process. This resulted in the formation of mixed iron oxide phase for lower polypyrrole concentration, stabilizing to a single cubic iron oxide phase at higher polypyrrole concentration. The composites in the pellet form were used for humidity and gas sensing investigations. Their sensitivity to humidity was found to increase with increasing concentration of polypyrrole. Gas sensing was performed for CO₂, N₂ and CH₄ gases at varying pressures. The sensors showed a linear relationship between sensitivity and pressures for all the gases studied. The sensors showed highest sensitivity to CO₂ gas.     

Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms

Neural Computing and Applications - Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for...     

Simulation of ill-conditioned situations in inverse coefficient problem for the Sturm–Liouville operator based on boundary measurements

The problem of determining the unknown coefficient k=k(x) of the Sturm–Liouville operator Au≡−(k(x)u′(x))′+q(x)u(x) from the measured data at the boundary x=0;1 is considered. It is assumed that the function u=u(x) has several singular points in (0,1) of different types. As a result different types of ill-conditioned situations (mild, moderate and severe) in (0,1) arise. We analyze all the ill-conditioned situations and then based on the analysis construct computational method for the solution of the inverse problem.     

STTG-TTE: spatial–temporal gated multi-modality approach for travel time estimation based on temporal convolutional networks

Neural Computing and Applications - Travel time forecasting has become a core component of smart transportation systems, which assists both travelers and traffic organizers with route planning,...     

A RBF partition of unity collocation method based on finite difference for initial–boundary value problems

Meshfree radial basis function (RBF) methods are popular tools used to numerically solve partial differential equations (PDEs). They take advantage of being flexible with respect to geometry, easy to implement in higher dimensions, and can also provide high order convergence. Since one of the main disadvantages of global RBF-based methods is generally the computational cost associated with the solution of large linear systems, in this paper we focus on a localizing RBF partition of unity method (RBF-PUM) based on a finite difference (FD) scheme. Specifically, we propose a new RBF-PUM-FD collocation method, which can successfully be applied to solve time-dependent PDEs. This approach allows to significantly decrease ill-conditioning of traditional RBF-based methods. Moreover, the RBF-PUM-FD scheme results in a sparse matrix system, reducing the computational effort but maintaining at the same time a high level of accuracy. Numerical experiments show performances of our collocation scheme on two benchmark problems, involving unsteady convection–diffusion and pseudo-parabolic equations.