Journal of Modeling and Simulation
Volume:50 Issue: 2, Summer-Autumn 2018

In dealing with model predictive controllers (MPC), controller tuning is a key design step. Various tuning methods are proposed in the literature which can be categorized as heuristic, numerical and analytical methods. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a proposed analytical MPC tuning approach for plants can be approximated by first order plus dead time models. The performance of such methods deteriorates in dealing with unknown or time-varying parameter plants. To overcome this problem, adaptive MPC tuning strategies are practical alternatives. The adaptive MPC tuning approach proposed in this paper is based on on-line identification and analytical tuning formulas. Simulation results are used to show the effectiveness of the proposed methodology. Also a comparison of the proposed adaptive tuning method with a well-known online tuning method is presented briefly which shows superiority of the proposed adaptive tuning method.

Many of the physical phenomena, like friction, backlash, drag, and etc., which appear in mechanical systems are inherently nonlinear and have destructive effects on the control systems behavior. Generally, they are modeled by hard nonlinearities. In this paper, two different methods are proposed to cope with the effects of hard nonlinearities which exist in friction various models. Simple inverted pendulum on a cart (SIPC) is considered as a test bed system, too. In the first technique, a nonlinear optimal controller based on approximate solution of Hamilton-Jacobi-Bellman (HJB) partial differential equation (PDE) is designed for the system and finally an adaptive anti disturbance technique is proposed to eliminate the friction destructive effects. In the second one, three continuous functions are used to approximate hard nonlinearities when they are augmented to system model. These techniques are compared with each other using simulations and their effectiveness are shown.

The conventional liquids have some limitations regarding the thermal properties. The nanoparticles addition is one of the techniques which can modify them. In this research, heat transfer coefficient (h) and pressure loss (Δp) of various nanofluids containing Al2O3, SiO2 and MgO nanoparticles dispersed in water in an annular tube with constant wall temperature were numerically considered. According to the literature, five different nanofluid volume concentrations (1%, 2%, 3%, 4% and 5%) were chosen and used. Two models involving the mixture and VOF were applied and their data were compared. The average convective heat transfer coefficient and pressure loss enhanced with volume fraction and Reynolds number (Re) increment (3000

Growing concern in the management of energy due to the increasing energy costs, has forced managers to optimize the amount of energy required to provide products and services. This research integrates an energy-based resource investment project-scheduling problem (RIP) under a multi-skilled structure of the resources. The proposed energy based multi skilled resource investment problem (EB-MSRIP) consists of a single project with a set of tasks that require several skills to be competed. Each skill could be applied in several levels of efficiency, each including significant energy and implementation costs. Similar to RIPs, in the EB-MSRIP the required levels of skills are considered as decision variables and a bi-objective formulation is proposed for the problem. The first objective of the model minimizes total cost regarding to energy consumption cost and implementation cost of required multi skilled resources, and the second one minimizes the project’s makespan. The epsilon constraint method has been used to validate the developed formulation on several small-size instances. For larger problem instances, as epsilon constraint method fails to obtain a solution, the multi objective ant colony optimization (MOACO) algorithm has been implemented to tackle the problems. The key control parameters of proposed MOACO are tuned within Taguchi method. Computational results in terms of several comparison metrics including MID, DM, NPS and SNS determine notable advantages of proposed MOACO.

Synthetic aperture radar (SAR) images due to the usage of coherent imaging systems are affected by speckle. So lots of despeckling filters have been introduced up to now to suppress the speckle. Hence, objective and subjective evaluation of the denoised SAR images becomes a necessity. Thereby lots of objective evaluating estimators are introduced to evaluate the performance of despeckling filters. However, evaluating the SAR images, two main problems exist: 1) contradiction of objective and subjective evaluations and 2) absence of the ground-truth (noiseless) SAR image of the illuminated scene. So lots of efforts had been done to introduce precise referenceless estimators for SAR images which will be compatible with subjective evaluation and the results obtained by other estimators. In this paper we propose a new edge detector and also a new referenceless estimator called “Extended Ratio Edge Detector” and “ ” respectively. These algorithms are the extended version of “Ratio Edge Detector” and “ ” estimator. Experiments on images obtained from RADARSAT-1 dataset showed that the proposed edge detector and the estimator outperform their previous versions of algorithms as the proposed parameter subjectively reports up to 0.2 better results for images filtered with FANS filter in comparison with other used methods. This is also validated by and parameters, so it is reliable tool for objective evaluation of despeckled SAR images.

This paper investigates the problem of controlling a team of Quadrotors that cooperatively transport a common payload. The main contribution of this study is to propose a cooperative control algorithm based on a decentralized algorithm. This strategy is comprised of two main steps: the first one is calculating the basic control vectors for each Quadrotor using Moore–Penrose theory aiming at cooperative transport of an object and second one is combining these vectors with individual control vectors, which are obtained from a closed-loop non-linear robust controller. In this regard, a nonlinear robust controller is designed based on Second Order Sliding Mode (SOSMC) approach using Extended Kalman-Bucy Filter (EKBF) to estimate the unmeasured states which is capable of facing external disturbances. The distinctive features of this approach include robustness against model uncertainties along with high flexibility in designing the control parameters to have an optimal solution for the nonlinear dynamic of the system. Design of the controller is based on Lyapunov laws which can provide the stability of the end-effector during the tracking of the desired trajectory. Finally, simulation results are given to illustrate the effectiveness of the proposed method for the cooperative Quadrotors to transport a common payload in various maneuvers.

Many researchers have controlled and analyzed biped robots that walk in the sagittal plane. Nevertheless, walking robots require the capability to walk merely laterally, when they are faced with the obstacles such as a wall. In walking robot field, both nonlinearity of the dynamic equations and also having a tracking system cause an effective control has to be utilized to address these problems. Therefore, this paper presents a nonlinear fuzzy tracking control for the walking robots that step purely in the lateral plane on slope. When fuzzy control is utilized to track the desired trajectories of the joints, there has to be a trade-off between tracking errors and control efforts. Consequently, a particle swarm optimization algorithm is used to obtain the Pareto front of these non-commensurable objective functions to determinate the fuzzy control parameters. In this paper, normalized summation of angles errors and normalized summation of control efforts are considered as the objective functions. These objective functions have to be minimized simultaneously. A vector which contains the control parameters is considered as the vector of selective parameters with positive constant values. The obtained Pareto front by the proposed multi-objective algorithm is contrasted with three prominent algorithms, modified NSGAII, Sigma method and MATLAB Toolbox MOGA. The result dramatizes the superiority of innovative particle swarm optimization over the algorithms.

In tire industry, it is very crucial to evaluate physical and mechanical properties of the rubber which is used for production of the tire, to ensure the quality of the final product. Resilience is an important property of a rubber, which cannot be evaluated through direct measurement in production cycle in this industry. Therefore, non-destructive ultrasonic testing, which has been used in many applications for examination of various material properties, can be used as an alternative approach for this purposes. In this study, the non-destructive ultrasonic testing method has been employed to investigate the resilience of nanoclay reinforced rubber compounds. By changing Physical and mechanical properties of materials, ultrasonic wave velocities are changed. For this purpose, sixteen different samples of nanoclay reinforced rubber compounds with different formulations were prepared and both their resilience and the longitudinal ultrasonic wave velocity through them were measured. At the next step, using the relevance vector machine regression analysis, a mathematical expression for the rubber resilience based on the longitudinal ultrasonic wave velocity was developed, which was proven to be qualified with acceptable accuracy and generalization capability. The results of this research can be used for online evaluation of the rubber resilience in tire production cycle.

Meta-heuristics optimization methods are important techniques for optimal design of the engineering systems. Numerous methods, inspired by different nature phenomena, have been introduced in the literature. A new modified version of Teaching-Learning-Based Optimization (TLBO) Algorithm is introduced in this paper. TLBO, as a parameter free algorithm, is based on the learning procedure of students in a classroom. In the Conventional TLBO (CTLBO), the students enhance their grade in two phases known as teacher phase and student phase. In the former, the teacher tries to enhance the average of the class. In the later, the students share their knowledge in the groups of two. In the proposed Modified TLBO (MTLBO), the students participate in the groups of several students and improve their knowledge based on the grade of these students. Participating in the meeting with more than two students increases the probability of enhancing the student marks. To testify the performance of the proposed algorithm, it is applied on the problem of optimal capacitor placement with the aim of annual net saving maximization and system stability enhancement. The test systems are 34-bus and 94-bus radial test systems. The comparison of the results clears the appropriate performance, fast convergence, and superiority of the proposed algorithm.

In networked control systems, time delay and data dropout can degrade the performance of the control system and even destabilize the system. In the present paper, the Extended Kalman filter is employed to compensate the effects of time delay and data dropout in feedforward and feedback paths of networked control systems. In the proposed method, the extended Kalman filter is used as an observer with nonlinear discrete model along with a predictor and a compensator. The predictor provides a sequence of predictions for state variables, and the controller generates a set of control predictions in the future based on the predictor outputs. The compensator chooses the best control signal, from among the set of control signals transmitted by the predictor, to compensate for the random network transmission time delay and packet dropout. In the cases with periodic load processes, correlation can be seen in delays between samples, and time delay behavior is captured with the Markov chains model. The dependence between feedback and feedforward delays, are also modeled by letting the distribution of the delays be governed by the state of an underlying Markov chains. Simulation results demonstrate the performance of the presented method in time delay and data dropout compensation for networked control systems.

Hyperspectral anomaly detection is one of the main challenging topics in both military and civilian fields. The spectral information contained in a hyperspectral cube provides a high ability for anomaly detection. In addition, the costly spatial information of adjacent pixels such as texture can also improve the discrimination between anomalous targets and background. Most studies miss the worthful spatial characteristics. Moreover, some works that include the spatial features in the anomaly detection process, extract features from each hyperspectral band that is a two dimensional image while the original structure of hyperspectral cube contains three dimensions. Ignoring the nature of hyperspectral image leads to lose the contained spectral-spatial correlations in the hyperspectral cube. To deal with this difficulty in this work, the fused spectral and spatial features obtained by applying 3D Gabor filters are proposed for hyperspectral anomaly detection. Exploiting the 3D structure of hyperspectral cube by capturing multiple scales, orientations and spectral dependent characteristics of it provides an appropriate spectral-spatial feature space for anomalous targets detection. The extracted features are applied to the regularized RX detector to provide the detection map. The experiments show the superior performance of the proposed Gabor 3D based detector in terms of detection accuracy and computation time.

In this paper, a non-linear approach for design and analysis of solid-state power amplifiers is presented and used for AlGaN-GaN high electron-mobility transistor (HEMTs) on SiC substrate for Ku band(12.4 - 13.6 GHz) applications. With combining the output power of 8 transistors, maximum output power of 46.3 dBm (42.6 W), PAE of 43% and linear gain of 22.9 dB were achieved and good agreement has been obtained between the simulation and analysis results.

The State-Dependant Riccati Equation method has been frequently used to design suboptimal controllers applied to nonlinear dynamic systems. Different methods for local stability analysis of SDRE controlled systems of order higher than two such as the attitude dynamics of a general rigid body have been developed in the literature; however, it is still difficult to show global stability properties of closed-loop system with this controller. In this paper, a reduced-form of SDRE formulation for attitude dynamics of a general rigid body is achieved by using Input-State Linearization technique and solved analytically. By using the solution matrix of the reduced-form SDRE in properly defined Lyapunov functions, a class of nonlinear controllers with global stability properties is developed. Numerical simulations are performed to study the stability properties and optimality for attitude stabilization of a general rigid body, and it is concluded that the designed controllers have the capability to provide a balance between optimality and proper stability characteristics.

In this paper, a computational intelligence method is used for solution of fractional optimal control problems (FOCPs) with equality and inequality constraints. According to the Ponteryagin minimum principle (PMP) for FOCP with fractional derivative in the Riemann- Liouville sense and by constructing a suitable error function, we define an unconstrained minimization problem. In the optimization problem, we use trial solutions for the states, Lagrange multipliers and control functions where these trial solutions are constructed by a feed-forward neural network model. We then minimize the error function using a numerical optimization scheme where weight parameters and biases associated with all neurons are unknown. Examples are included to demonstrate the validity and capability of the proposed method. The strength of the proposed method is its equal applicability for the integer-order case as well as fractional order case. Another advantage of the presented approach is to provide results on entire finite continuous domain unlike some other numerical methods which provide solutions only on discrete grid of point.