Transactions of the Institute of Measurement and Control

Engin, Şeref Naci

doi: 10.1177/0142331218815783pmid: N/A

Şahan, Gökhan; Eldem, Vasfi

doi: 10.1177/0142331218780212pmid: N/A

In this study, we give well-posedness conditions for planar conewise linear systems where the vector field is not necessarily continuous. It is further shown that, for a certain class of planar conewise linear systems, well posedness is independent of the conic partition of R2. More specifically, the system is well posed for any conic partition of R2.

Erol, Bilal; Altıner, Berk; Adalı, Erkan; Delibaşı, Akın

doi: 10.1177/0142331218780217pmid: N/A

In this paper, an alternative approach to conventional H∞ control is proposed for adaptive optic (AO) systems. In order to account for the dynamics of the deformable mirror (DM), the Kirchhoff plate equation is considered. Phase wavefront aberrations, which are the effect of atmospheric turbulence, are modelled using the orthogonal set of Zernike polynomials. The first part of this study concerns the derivation of mathematical models for the DM and the atmospheric turbulence. The AO systems used for disturbance rejection concentrate on a specific frequency band, as the disturbance occurs in that region. However, the conventional weighted H∞ controller is not applicable due to its order. The proposed controller handles this problem by using frequency weighted balanced model reduction. The efficiency of the proposed approach is examined on Zernike’s tip and tilt, focus, astigmatism, coma, trefoil, spherical, secondary astigmatism and quadrafoil modes.

Evren Han, Sanem; Unel, Mustafa

doi: 10.1177/0142331218780224pmid: N/A

The robust periodic trajectory tracking problem is tackled by employing acceleration feedback in a hybrid learning-adaptive controller for n-rigid link robotic manipulators subject to parameter uncertainties and unknown periodic dynamics with a known period. Learning and adaptive feedforward terms are designed to compensate for periodic and aperiodic disturbances. The acceleration feedback is incorporated into both learning and adaptive controllers to provide higher stiffness to the system against unknown periodic disturbances and robustness to parameter uncertainties. A cascaded high gain observer is used to obtain reliable position, velocity and acceleration signals from noisy encoder measurements. A closed-loop stability proof is provided where it is shown that all system signals remain bounded and the proposed hybrid controller achieves global asymptotic position tracking. Results obtained from a high fidelity simulation model demonstrate the validity and effectiveness of the developed hybrid controller.

Öksüz, Halil Yiğit; Akar, Mehmet

doi: 10.1177/0142331218785673pmid: N/A

In this paper, two parameter-independent fault-tolerant consensus algorithms are proposed to address the consensus problem in the presence of misbehaving agents. The first algorithm relies on adaptively estimating the number of faulty agents in the network by using a distributed fault-detection scheme. It is shown that this algorithm converges if the network of non-faulty agents is (f+1)-robust, where f is the number of faulty agents in the network. The second algorithm is a non-parametric Mean-Subsequence-Reduced algorithm whose convergence is guaranteed if the network of non-faulty nodes is (f+1)-robust and all non-faulty nodes have the same number of in-neighbours. Neither algorithm requires initial knowledge on the number of faulty agents in the network. The efficacy of the algorithms are illustrated with simulation results.

Arslan, M. Selçuk; Sever, Mert

doi: 10.1177/0142331218795200pmid: N/A

In this study, a nonlinear predictive control method is developed for the active steering control of a sport utility vehicle. The method is tested on a nonlinear mathematical model of an 11-degree-of-freedom vehicle. The system performance is evaluated by considering that the control law must keep the actual yaw rate close to the desired yaw rate and minimizing the vertical load changes at each wheel. The latter is proposed for this work. The vertical load changes play an important role in the dynamics and the stability of the system. The effectiveness of the control method is demonstrated through numerical simulation by using a vehicle model that includes three case studies: rapid lane change at low and high velocities and the fishhook manoeuvre. The results show that the stability of the vehicle is maintained and its rollover propensity is decreased. In addition, the proposed controller is compared with a well-known linear model predictive controller.

Akbatı, Onur; Üzgün, Hatice Didem; Akkaya, Sirin

doi: 10.1177/0142331218813425pmid: N/A

This paper presents the design and implementation of a fuzzy logic controller using Very High Speed Integrated Circuit Hardware Description Language (VHDL) on a field programmable gate array (FPGA). First, a Sugeno-type fuzzy logic controller with five triangular and trapezoidal membership functions for two inputs and with nine singleton membership functions for one output is examined. The proposed structure is tested with second- and third-order system model using FPGA-in-the-loop simulation via a MATLAB/Simulink environment. Then, for different kinds of fuzzy logic controllers, a new look-up table (LUT) and interpolation-based controller implementation is proposed to eliminate the computational complexity of the primarily designed structure. As a case study, a magnetic levitation system is controlled with an adaptive neuro-fuzzy inference system (ANFIS) trained fuzzy logic controller, then it is simulated and implemented using a LUT-based controller. Finally, we provide a comparison of results.

Karadeniz Kartal, Seda; Leblebicioglu, M. Kemal; Ege, Emre

doi: 10.1177/0142331219826524pmid: N/A

In this study, a nonlinear mathematical model for an unmanned underwater survey vehicle (SAGA) is obtained. The structure of the mathematical model of the vehicle comes from a Newton–Euler formulation. The yaw motion is realized by a suitable combination of right and left thrusters. The navigation problem is solved by using the inertial navigation system and vision-based measurements together. These are integrated to more accurately obtain navigation data for the vehicle. In addition, the magnetic compass is used to support the attitude information of the vehicle. A pool experimental set-up is designed to test the navigation system. Performance of the resultant navigation system can be analysed by creating suitable system state, measurement and noise models. The navigational data for the vehicle has been improved using a Kalman filter. The mathematical model of the vehicle includes some unknown parameters such as added mass and damping coefficients. It is not possible to determine all the parameter values as their effects on the state of the system are usually negligible. On the other hand, most of the ‘important’ parameters are obtained based on a system identification study of the vehicle using this estimated navigational data for coupled motion. This study is performed in a MATLAB/Simulink environment.

Hidir, Elvan Kuzucu; Uyanik, Ismail; Morgül, Ömer

doi: 10.1177/0142331218778748pmid: N/A

The analysis, identification and control of periodic systems has gained increasing interest during the last few decades due to the increased use of dynamical systems that exhibit periodic motion. The vast majority of these studies focus on the analysis and control problem for a known state-space formulation of the linear time-periodic (LTP) system. On the other hand, there are also some studies that focus on data-driven identification of LTP systems with unknown state-space formulations. However, most of these methods provide numerical estimates for the harmonic transfer functions (HTFs) of an LTP system that are extremely difficult to work with during controller design. The goal of this paper is to provide a simple controller design methodology for unknown LTP systems by utilizing so-called HTFs estimates. To this end, we first build a mathematical basis of LTP controller design for known LTP systems using the Nyquist diagrams and analytically derived HTFs. We propose a new methodology to design P-, PD- and PID-type controllers for LTP systems using Nyquist diagrams and the eigenlocus of the HTFs. Having established the HTF-based controller design procedure, we extend our methodology to unknown LTP systems by presenting a new sum-of-cosine signal-based data-driven system identification method. We show that the proposed data-driven controller design method allows estimation of the HTFs and it provides simple tools for optimizing certain time-domain performance metrics. We provide numerical examples for both known and unknown LTP system cases to illustrate the performance of the proposed controller design methodology.

Cai, Yuliang; Zhang, Huaguang; He, Qiang; Sun, Shaoxin

doi: 10.1177/0142331218774614pmid: N/A

Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold δ to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.