Papers by Mohammad Farrokhi
Optimal Control Applications and Methods
Transactions of the Institute of Measurement and Control
This paper proposes an approach for identification of non-linear dynamic systems with input time ... more This paper proposes an approach for identification of non-linear dynamic systems with input time delay into the block-oriented Wiener model in the presence of measurement noise. The model comprised a linear dynamic subsystem (LDS) at the input side that is cascaded with a non-linear static subsystem (NSS). The LDS comprised Laguerre filters, whereas the NSS is constructed using committee neural networks (CNNs). The Laguerre filter compensates for the input time delay, whereas the CNN finds an appropriate non-linear mapping between its input and output with the useful property of the measurement noise attenuation. The parameters of the Laguerre filters as well as those of the CNN are determined using offline training algorithms. In order to find the optimal values of the weights of the CNN, a noise analysis is conducted. The proposed method is applied to a simulated continuous-stirred tank reactor (CSTR) with input time delay and measurement noise. The results indicate substantial be...
International Journal of Adaptive Control and Signal Processing
International Journal of Control, Automation and Systems, 2016
Jirs, 2009
This paper presents an adaptive output-feedback control method for nonaffine nonlinear non-minimu... more This paper presents an adaptive output-feedback control method for nonaffine nonlinear non-minimum phase systems that have partially known Lipschitz continuous functions in their arguments. The proposed controller is comprised of a linear, a neuro-adaptive and an adaptive robustifying control term. The adaptation law for the neural network weights is obtained using the Lyapunov's direct method. One of the main advantageous of the proposed method is that the control law does not depend on the state estimation. This task is accomplished by introducing a strictly positive-real augmented error dynamic and using the Leftshetz-Kalman-Yakobuvich lemma. The ultimate boundedness of the error signals will be shown analytically using the extension of Lyapunov theory. The effectiveness of the proposed scheme will be shown in simulations for the benchmark problem Translational Oscillator/Rotational Actuator (TORA) system. Keywords Neural networks • Nonlinear non-minimum phase system • Adaptive control • Output feedback • Strictly positive real 1 Introduction Control of nonlinear non-minimum phase systems is a difficult task in control theory. This problem has been an active research area for many years. Several
... Abstract: This paper presents a new steady state Kaman filter for tracking high maneuvering t... more ... Abstract: This paper presents a new steady state Kaman filter for tracking high maneuvering targets. The α β − and the α β γ − − filters are the steady-state Kaman filters for tracking constant speed and constant acceleration targets, respectively. ...
2009 European Control Conference, 2009
This paper presents a nonlinear model predictive control (NMPC) method with adaptive neuro-modell... more This paper presents a nonlinear model predictive control (NMPC) method with adaptive neuro-modelling for redundant robotic manipulators. Using the NMPC, the end-effector of the robot tracks a predefined geometry path in the Cartesian space without colliding with obstacles in the workspace and at the same time avoiding singular configurations of the robot. Furthermore, using the neural network for the model prediction, no knowledge about system parameters is necessary; hence, yielding robustness against changes in parameters of the system. Numerical results for a 4DOF redundant spatial manipulator actuated by DC servomotors shows effectiveness of the proposed method.
Automatica, 2011
This paper, presents a robust adaptive control method for a class of nonlinear non-minimum phase ... more This paper, presents a robust adaptive control method for a class of nonlinear non-minimum phase systems with uncertainties. The development of the control method comprises two steps. First, stabilization of the system is considered based on the availability of the output and internal dynamics of the system. The reference signal is designed to stabilize the internal dynamics with respect to the output tracking error. Moreover, a combined neuro-adaptive controller is proposed to guarantee asymptotic stability of the tracking error. Then, the overall stability is achieved using the small gain theorem. Next, the availability of internal dynamics is relaxed by using a linear error observer. The unmatched uncertainty is compensated using a suitable reference signal. The ultimate boundedness of the reconstruction error signals is analytically shown using an extension of the Lyapunov theory. The theoretical results are applied to a translational oscillator/rotational actuator model to illustrate the effectiveness of the proposed scheme.
Journal of Intelligent Fuzzy Systems Applications in Engineering and Technology, 2006
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004
... M. Farrokhi, Dr., Electrical Engineering Dep., Iran University of science & Techn... more ... M. Farrokhi, Dr., Electrical Engineering Dep., Iran University of science & Technology, Narmak, Tehran, 16844, Iran (e-mail: [email protected]). A. Vahabian Tehrani, Electrical & computer Eng. Group, Faculty of Engineering, Tehran University, Amir-abad, Tehran, Iran (e-mail ...
2015 AI & Robotics (IRANOPEN), 2015
This paper presents a method for line-of-sight stabilization in submarine periscopes with respect... more This paper presents a method for line-of-sight stabilization in submarine periscopes with respect to disturbances in the sea surface movements. These disturbances can cause unstable images taken by the periscope and give altered information from the targets that are located on the see surface, on the land, or in the air. To overcome this problem, an image stabilization method is required to generate reference signals for servomotors to remove the unwanted motions incurred on the image sequences. To drive the stabilization equations, basic rotation and homogeneous transformation matrices will be used. The stabilization equations will be derived for two different cases: 1) the range of target is known and 2) the range of target is unknown. In fact, these equations map the input space to the three-dimensional output space. The input space consists of the deviation angles of the periscope platform, the optional target range, which can be measured or given by the operator (optional data)...
Proceedings of the 18th IFAC World Congress, 2011
In this paper, a combined state-feedback sliding-mode controller for quadruple tank system using ... more In this paper, a combined state-feedback sliding-mode controller for quadruple tank system using fuzzy logic is presented. The quadruple system is set to operate in its non-minimum phase mode, which is more challenging as compared to the minimum phase mode. The Sliding-Mode Control (SMC) method is employed to achieve fast transient response, while the state-feedback controller (SFC) can provides zero steady-state errors. Simulation results show effectiveness of the proposed method as compared to the standalone SMC and SFC methods.
The 2nd International Conference on Control, Instrumentation and Automation, 2011
Robotics and Autonomous Systems, 2014
Proceedings of the 16th IFAC World Congress, 2005, 2005
In this paper, an adaptive control method for hybrid position/force control of robot manipulators... more In this paper, an adaptive control method for hybrid position/force control of robot manipulators, based on neuro-fuzzy modelling, is presented. Also, an adaptive neuro-fuzzy compensator compensates the friction force between the endeffector and the surface of the object. Due to the adaptive neuro-fuzzy modelling, the proposed controller is independent of the robot dynamics. Also, the stability of the controller is guaranteed, since the adaptation law is based on Lyapunov theory. The simulation results show good performance of the proposed controller as compared with other conventional control schemes such as computed torque method.
Proceedings of the 15th IFAC World Congress, 2002, 2002
A fuzzy control design method for four-wheel-steering vehicles, using fuzzy models, is presented.... more A fuzzy control design method for four-wheel-steering vehicles, using fuzzy models, is presented. The design model is obtained from a vehicle model using fuzzy modeling approach. In the first step of the controller design, an optimal steering controller is proposed for each local model using LQR method. Then, the local controllers are combined using fuzzy rules to form a fuzzy controller. In the next step, an adaptive fuzzy controller is obtained using adaptive control law. Simulation results show the good performance of the proposed controller as compared to the nonadaptive fuzzy controllers.
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Papers by Mohammad Farrokhi