Showing papers by "Mojtaba Ahmadieh Khanesar published in 2017"

An Aerial Robot for Rice Farm Quality Inspection With Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm

Abstract: Agricultural robots, or agrobots, have been increasingly adopted in every aspect of farming from surveillance to fruit harvesting in order to improve the overall productivity over the last few decades. Motivated by the compelling growth of the agricultural robots in modern farms, in this work, an autonomous quality inspection over rice farms is proposed by employing quadcopters. Real-time control of these vehicles, however, is still challenging as they exhibit a highly nonlinear behavior especially for agile maneuvers. What is more, these vehicles have to operate under uncertain working conditions such as wind and gust disturbances as well as positioning errors caused by inertial measurement units and global positioning system. To handle these difficulties, as a model-free and learning control algorithm, type-2 fuzzy neural networks (T2-FNNs) are designed for the control of a quadcopter. The novel particle swarm optimization-sliding mode control (PSO-SMC) theory-based hybrid algorithm is proposed for the training of the T2-FNNs. In particular, the continuous version of PSO is adopted for the identification of the antecedent part of the T2-FNNs while the SMC-based update rules are utilized for the online learning of the consequent part during control. In the virtual environment, the quadcopter is expected to perform an autonomous flight including agile maneuvers such as steep turning and sudden altitude changes over a rice terrace farm in Longsheng, China. The simulation results for the T2-FNNs are compared with the outcome of conventional proportional-derivative (PD) controllers for different case studies. The results show that our method decreases the trajectory tracking integral squared error by %26 over PD controllers in the ideal case, while this ratio goes up to %95 under uncertain working conditions.

Improving the Speed of Center of Sets Type Reduction in Interval Type-2 Fuzzy Systems by Eliminating the Need for Sorting

Abstract: In the deployment of interval type-2 fuzzy systems, one of the most important steps is the type reduction. The commonly used center of sets type reducer requires the solution of two nonlinear constrained optimization problems. Frequently used approaches to solve them are the Karnik–Mendel algorithms and their variants. However, these algorithms suffer from the need for sorting, which is known to be computationally very expensive. Using the reformulations proposed in this paper for center of sets type reducer, it is possible to eliminate the need for sorting. This makes interval type-2 fuzzy systems more appropriate for cost-sensitive real-time applications. Extensive simulations are presented to illustrate the faster convergence speed of the proposed method over six other enhanced variants of the Karnik–Mendel algorithm as applied to center of sets type reduction of interval type-2 fuzzy systems.

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Abstract: A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction

Abstract: In this paper, our aim is to compare and contrast various ways of modeling uncertainty by using different type-2 fuzzy membership functions available in literature. In particular we focus on a novel type-2 fuzzy membership function, — “Elliptic membership function”. After briefly explaining the motivation behind the suggestion of the elliptic membership function, we analyse the uncertainty distribution along its support, and we compare its uncertainty modeling capability with the existing membership functions. We also show how the elliptic membership functions perform in fuzzy arithmetic. In addition to its extra advantages over the existing type-2 fuzzy membership functions such as having decoupled parameters for its support and width, this novel membership function has some similar features to the Gaussian and triangular membership functions in addition and multiplication operations. Finally, we have tested the prediction capability of elliptic membership functions using interval type-2 fuzzy logic systems on US Dollar/Euro exchange rate prediction problem. Throughout the simulation studies, an extreme learning machine is used to train the interval type-2 fuzzy logic system. The prediction results show that, in addition to their various advantages mentioned above, elliptic membership functions have comparable prediction results when compared to Gaussian and triangular membership functions.

Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS

Abstract: Graphical abstractDisplay Omitted HighlightsThe consequent part parameters of IT2FLS are tuned using ELM.Three approaches for the generation of antecedent parameters are discussed.Simulation results on noisy chaotic data sets support optimal parameters.The widest FOU generated manually produced good forecasts than random parameters.Randomly generated parameters may be preferable if speed is needed. Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. Moreover, the extreme learning machine does not have any design parameters such as learning rate, covariance matrix, etc. The rigorous proof of universal approximation of extreme learning machine with much milder conditions makes it a preferable choice in many different approaches. Although this algorithm is optimal for the parameters which appear linearly in the consequent part of interval type-2 fuzzy logic systems, it is not optimal for the parameters of the antecedent part as it uses random parameters. In this paper, heuristic optimization approaches such as genetic algorithm and artificial bee colony are used to optimize the parameters of the antecedent part of interval type-2 fuzzy logic systems. As these methods are global optimizers, there is less possibility that they will fall in a local minima and are suitable for the selection of the parameters of the antecedent part. A comparative analysis of the optimal parameters with the randomly and manually generated parameters is presented here using noise-free and noisy Mackey-Glass time series data sets and a real world data set. Simulation results support this idea over randomly and manually generated parameters.

Hybrid multi-objective forecasting of solar photovoltaic output using Kalman filter based interval type-2 fuzzy logic system

Abstract: Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic system in both the hybrid algorithms are tuned using Kalman filter. Whereas the antecedent parameters of the system in the first hybrid algorithm is optimized using the multi-objective particle swarm optimization (MOPSO) and using the multi-objective evolutionary algorithm Based on Decomposition (MOEA/D) in the second hybrid algorithm. Root mean square error and maximum absolute error as the two accuracy objective are utilized to find the Pareto-optimal solution with the MOPSO and MOEA/D respectively. The proposed hybrid multi-objective designs of the interval type-2 fuzzy logic system are utilized to the prediction of solar photovoltaic output. It is observed that MOEA/D outperforms MOPSO in this case in terms of quality of results and its diversity. Finally, one point is selected from the obtained Pareto front and its performance is illustrated.

A novel complexity reduced Levenberge-Marquardt algorithm: Application to the training of interval type-2 fuzzy systems

Abstract: Levenberge-Marquardt (LM) algorithm is a well-known optimization technique which has the advantages of the steepest descent and the Gauss-Newton methods. Unfortunately, LM algorithm-based parameter update rules, regardless of being used to tune the parameters of artificial neural networks or neuro-fuzzy systems, require the calculation of inversion of high dimensional matrices. Matrix inversions are generally computationally expensive, and it is not desired in a real-time application where the computation speed is critical. In this paper, using matrix inversion lemma, LM algorithm is modified to avoid matrix inversion calculations, and therefore lessen its computational burden. The proposed algorithm is compared with the conventional LM algorithm for the training of interval type-2 fuzzy logic systems in terms of its speed. Extensive simulation results demonstrate that that the proposed novel method can increase the speed of LM algorithm by 50% while remaining the same performance.

Guaranteed cost adaptive sliding mode fuzzy control systems

Abstract: In this paper, a novel sliding mode control technique is proposed which benefits from recursive least square adaptation laws. Since this method has high resistance against uncertainty and may result in a desirable transient response, this method is one of the most commonly used nonlinear control methods. In this method, the uncertain dynamics of system is stabilized by applying a discontinuous control signal. The principle of this type of controller is to force the states of the system towards sliding manifold and maintain the states of this stable manifold which defines the desired behavior of the system. It is possible to model dynamic behavior of physical systems in terms of fuzzy systems. In order to tune the parameters of fuzzy system, a cost function based on sliding mode is proposed. The solution to this cost function results in optimal adaptation laws whose stability analysis is done using an appropriate Lyapunov function. The proposed adaptive sliding mode fuzzy controller is simulated on an inverted pendulum to test its efficacy and performance in control of a benchmark system.