Mohammad Reza Jahed Motlagh

Robust control and modeling a 2-DOF Inertial Stabilized Platform

This paper presents an uncertain linear model for an Inertial Stabilization Platform (ISP). The system has a 2 degree of freedom (DOF) gimbal which will be attached to a moving vehicle and the optical sensors mounted on stabilized gimbal. The main purpose of an ISP is to eliminate the various disturbance and body motion to make the line of sight (LOS), hold steady in an inertial space. Due to dynamical model of the 2-DOF gimbal, friction, cable restraint, noise, other disturbances from the outside environment and the motions of the vehicles body (as a result of maneuvering or vibration), the pointing and tracking accuracy of the gimbaled system may strongly degrade. These disturbances are typically nonlinear. Modeling and controlling the stabilization loop with these nonlinearities are the main problem. The approach of this paper is considering a linear doubted model. Linearity makes the model simple and so linear classic controller can be used to meeting design requirements. Therefore the cost of the design and implementation stages will decrease. In addition uncertainty makes the model practical and so the nonlinear nature of mentioned disturbance can be interpreted by this model. So this built ISP by a PI controller which is capable enough to overcome the disturbances and to meet the implication requirements will be controlled. For decreasing the effect of these uncertainties and other probable variation in ISPs parameters on system performance, a determined simple structure for robust enhancer compensator is used and designed.

A socially-based distributed self-organizing algorithm for holonic multi-agent systems

Holonic multi-agent systems (HMASs) have recently attracted many researches in multi-agent systems community. Inspired from the multi-level and self-similar structures of social and biological system, holonic multi-agent systems have been widely used to model and solve complex real-world problems. The main concern in deploying HMASs is the problem of building the hierarchical holonic structure, called holarchy, and dynamically managing it during its lifetime. The way an HMAS is organized has a great impact on its applicability and performance. This paper proposes a self-organizing algorithm to build and manage the holoic structures in multi-agent systems. This algorithm is based on the local information of the agents about other agents they can communicate with. Using common social concepts, like skills, diversity, social exchange theory, and norms in definition of the algorithm, the outcomes of this research can be used in wide ranges of distributed applications. The proposed model is extensively tested in a task allocation problem; and its performance based on various design parameters is studied. Empirical results show that the proposed model properly increases the performance of the system in terms of effectiveness and efficiency.

Multivariable robust optimal PID controller design for a non-minimum phase boiler system using loop transfer recovery technique

LQG/Loop Transfer Recovery (LQG/LTR) technique based on Kalman filter as observer, has some difficulties in facing with non-minimum phase systems. The main problem is that the observer becomes unstable when the compensator recovers the target loop asymptotically. In this paper, we propose an optimal robust recoverable target loops for a boiler system. Using asymptotic time-scale eigenstructure assignment ATEA algorithm a stable observer is designed that makes the target loop recovered. Then, the controller is reduced to a PID controller for experimental purposes. Recoverability of target loop under controller reduction has been analyzed. Numerical results of two controllers are given in time and frequency domain. Comparing with a similar work based on loop-shaping Hinfin approach, LQG/LTR controller shows improvements in energy consumption and time responses.

A genetic-based input variable selection algorithm using mutual information and wavelet network for time series prediction

In this paper we presented a genetic-based optimal input selection method. This method uses mutual information as similarity measure between variables and output. Based on mutual information the proper input variables, which describe the time series dynamics properly, will be selected. The selected inputs have a maximum relevance with output variable and there exists minimum redundancy between them. This algorithm prepares proper input for wavelet neural network (WNN) prediction model. The WNN prediction model utilized for time series prediction benchmark in NN3 competition and sunspot data. Presented result shows that selected input with GA outperform other input selection method like correlation analysis, gamma test and greedy alg. prediction result indicates that proper inputs have a great impact on prediction efficiency.

Adaptive Control of Continuous-Time Systems by Chaotification

The generation of chaos by a linear system with nonlinear feedback has been extensively studied by many researchers. In this paper, we use a simple method to make an arbitrary nonlinear continuous-time system chaotic. For this, we apply a nonlinear time-delay feedback to the linearized system. Then we propose a model reference adaptive controller to control the resulted system in order to converge to a desired reference trajectory with uncertain parameters. An example is included to show the effectiveness of the method. Simulation results reveal that the controlled system tracks the reference system in spite of uncertain parameter.

Maximum power point tracking with constraint feedback linearization controller and modified incremental conductance algorithm

In this paper, a robust and constraint feedback linearization controller (FLC) with a modified incremental conductance (Inc.Cond) is proposed for maximum power point tracking (MPPT) in the photovoltaic (PV) systems and overall closed-loop internal stability is guaranteed. The proposed technique is independent with respect to load and is robust against disturbances in the load voltage. A boost chopper converter is utilized as an interface between the PV panel and load to control the system at the best operating point. A modified Inc.Cond method based on current orientation and without division equations is presented. The Inc.Cond method is utilized to generate the desired current for the FLC. The FLC navigates the PV panel to the maximum power point with high speed, whereas the control signal (duty cycle) constraints are monitored. Finally, the MPPT technique is validated through simulation and experimental results and two scenarios are defined to confirm controller robustness and modified Inc.Cond performance.

A novel robust generalized backstepping controlling method for a class of nonlinear systems

Abstract This study is aimed at introducing a new robust control strategy in developed backstepping method designed for a special class of nonlinear systems, which does not require any information on the upper bound of parametric uncertainties. A robust generalized backstepping method (RGBM) is introduced by using of both generalized backstepping method and nonlinear damping of Lyapunov redesign. The RGBM is more efficient than the standard backstepping method because the standard backstepping method can only be applied to strictly feedback systems while RGBM expands this class. In addition, RGBM has could be applied to a special class of nonlinear systems with unmatched uncertainty and unknown upper bound. Numerical simulations demonstrate the feasibility and advantages of the proposed algorithm. Finally, the compressor surge control system is reviewed to show the robustness of the proposed scheme.

Computing Lyapunov exponents in coupled map lattice for controlling spatiotemporal chaos

Abstract The most common and useful tool for the characterization of chaos is given by the Lyapunov exponents. In this paper, the computation of the entire Lyapunov exponents of the coupled logistic map lattice is considered. For this, the Jacobian matrix of coupled map lattice with constant coupling strength is first determined. The conditions of multiplicative ergodic theorem for nonlinear coupled map lattice are proven and then the Jacobian matrix of this map in order to computing Lyapunov exponents is calculated. The feasibility of this approach is illustrated in one system. The method for controlling spatiotemporal chaos based on Lyapunov exponents is also explained.

Reduced-order synchronization of chaotic systems: A modular adaptive design

In this paper, an adaptive algorithm is proposed for reduced-order synchronization of chaotic systems with different orders. Since the exact model of the system is not known a priory in real applications, or the parameters may change with time, adaptive control strategy is applied to make states of the slave system track those of the master, despite the uncertainties. Here, the adaptive control system is designed based on modularity approach, where the identification and control modules are designed completely independently. One of the most advantages of this algorithm, which is applied for the first time in chaos synchronization, is its flexibility in choosing a proper method for both identification and control modules. A modified recursive least square algorithm is used to identify the unknown parameters of the slave system, and the control module is designed based on active control method. As a case study, the Lorenz system and the chaotic pendulum are considered as the master and slave systems, respectively. Simulation results confirm the effectiveness of the proposed method.

Enlarging region of attraction in input-output linearization method

This paper proposes a new approach to enlarge region of attraction (RA) of polynomial nonlinear systems using an optimization strategy. In this method, an input-output linearization approach is applied to the system. Then a generalized eigenvalue problem (GEVP) is formulated based on system constraints and the desired output performance. The solution to this GEVP leads to a controller which makes the output performance desirable and at the same time the estimated region of attraction is maximized. Some illustrative examples and simulations are presented to verify the proposed strategy.