Ashkan Jasour
jasour.mit.edu/ashkan
Address: Model-based Embedded and Robotic Systems Group, Massachusetts Institute of Technology,
Building 32-22432, Vassar Street,
Cambridge, MA 02139
Address: Model-based Embedded and Robotic Systems Group, Massachusetts Institute of Technology,
Building 32-22432, Vassar Street,
Cambridge, MA 02139
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Papers by Ashkan Jasour
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.
connected converter is one of the recently developed multimodule
converters. The suitable control scheme for this type
of converter necessitates equal voltage sharing for inputseries
connected modules and output current sharing for the
output-parallel connected modules for all operating
conditions even with existence of dissimilarity in devices and
components used in each module. Such control scheme
consisting of several controllers can be implemented using
analog controllers. This paper presents a genetic algorithm
(GA) which optimizes the parameters of analog controllers
for each control loop in the control scheme of an ISOP
connected converter. Using GA eliminates the need for
tedious and time consuming small signal analysis of multimodule
converters. Moreover, because GA chooses the best
value for each parameters in different control loops
simultaneously it can best consider the interaction among all
loops and results in better large signal performance
especially in the case of large variations in the parameters or
considerable difference among modules of converter.
manipulators in non-stationary environments. Using NMPC, the end-effector of the robot could track
predefined desired path and reaches a moving target in the Cartesian space, while at the same time avoids
collision with moving obstacles and singular configurations in the workspace. To avoid collisions with
moving obstacles and capturing moving target, the future position of obstacles and the moving target in
3D space is predicted using artificial neural networks. Using online training of the neural network, no
knowledge about obstacles and motion of the moving object is required. The nonlinear dynamic of the
robot including actuators dynamic is also considered. Numerical simulations performed on a 4DOF
redundant spatial manipulator actuated by DC servomotors, shows effectiveness of the proposed method.
have been introduced to obviate demands for different level of
voltage and power in input and output. The suitable control
scheme for this type of converter necessitates equal voltage
sharing for series connected modules and equal current sharing
for the parallel connected modules for all operating conditions
even with existence of dissimilarity in devices and components
used in each module. Such a control scheme can be designed by
means of linear controllers. But in the case of large variations in
the parameters or considerable difference between modules of
converter, linear scheme cannot accomplish equal sharing
correctly. In this paper a MIMO controller scheme based on
artificial neural network (ANN) will be developed to solve this
problem in an input-series and output-parallel (ISOP) Dc-Dc
converter. The proposed MIMO controller is trained using
Particle Swarm Optimization (PSO). Using PSO to train the
ANN based controller, eliminates the need for a prior knowledge
of the system dynamics. The latter merit shows its significant
usefulness when the under study system is a large multi-module
system and consequently the derivation if its dynamic equations is a tedious and time consuming work. To implement the proposed controller for the ISOP converter MATLAB software will be used in this paper.
probabilistic robust controllers for discrete-time systems whose
objective is to reach and remain in a given target set with
high probability. More precisely, given probability distributions
for the initial state, uncertain parameters and disturbances, we
develop algorithms for designing a control law that i) maximizes
the probability of reaching the target set in N steps and ii)
makes the target set robustly positively invariant. As defined
the problem is nonconvex. To solve this problem, a sequence of
convex relaxations is provided, whose optimal value is shown
to converge to solution of the original problem. In other words,
we provide a sequence of semidefinite programs of increasing
dimension and complexity which can arbitrarily approximate
the solution of the probabilistic robust control design problem
addressed in this paper. Two numerical examples are presented
to illustrate preliminary results
general approach to chance constrained algebraic problems. In
this type of problems, one aims at maximizing the probability
of a set defined by polynomial inequalities. These problems
are, in general, nonconvex and computationally complex. With
the objective of developing systematic numerical procedures
to solve such problems, a sequence of convex relaxations
is provided, whose optimal value is shown to converge to
solution of the original problem. In other words, we provide
a sequence of semidefinite programs of increasing dimension
and complexity which can arbitrarily approximate the solution
of the probability maximization problem. Two numerical
examples are presented to illustrate preliminary results on the
numerical performance of the proposed approach.
inspired optimization strategy, an Imperialist
Competitive Algorithm, is applied to the problem of
designing a fuzzy controller. The goal is to design a
controller to enhance the transient and steady state
behaviour of the system output. Having fixed the rule
base of the fuzzy system, the controller is designed
through determining the membership functions of the
input and output variables. The design method is
applied to a model of vehicle. The inputs of the fuzzy
system are velocity of the vehicle and the sloop of the
road. The fuzzy controller controls the speed of the
vehicle by adjusting the amount of gas into vehicle
engine. Comparison results among the designed
controller and the controller designed by the expert
show that the controller obtained by Imperialist
competitive algorithm has better performance than the
expert controller.
maximizing the probability of a set defined by polynomial inequalities. These problems are, in general,
nonconvex and computationally hard. With the objective of developing systematic numerical
procedures to solve such problems, a sequence of convex relaxations based on the theory of measures
and moments is provided, whose sequence of optimal values is shown to converge to the optimal value
of the original problem. Indeed, we provide a sequence of semidefinite programs of increasing dimension
which can arbitrarily approximate the solution of the original problem. To be able to efficiently
solve the resulting large-scale semidefinite relaxations, a first-order augmented Lagrangian algorithm
is implemented. Numerical examples are presented to illustrate the computational performance of
the proposed approach.
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.
connected converter is one of the recently developed multimodule
converters. The suitable control scheme for this type
of converter necessitates equal voltage sharing for inputseries
connected modules and output current sharing for the
output-parallel connected modules for all operating
conditions even with existence of dissimilarity in devices and
components used in each module. Such control scheme
consisting of several controllers can be implemented using
analog controllers. This paper presents a genetic algorithm
(GA) which optimizes the parameters of analog controllers
for each control loop in the control scheme of an ISOP
connected converter. Using GA eliminates the need for
tedious and time consuming small signal analysis of multimodule
converters. Moreover, because GA chooses the best
value for each parameters in different control loops
simultaneously it can best consider the interaction among all
loops and results in better large signal performance
especially in the case of large variations in the parameters or
considerable difference among modules of converter.
manipulators in non-stationary environments. Using NMPC, the end-effector of the robot could track
predefined desired path and reaches a moving target in the Cartesian space, while at the same time avoids
collision with moving obstacles and singular configurations in the workspace. To avoid collisions with
moving obstacles and capturing moving target, the future position of obstacles and the moving target in
3D space is predicted using artificial neural networks. Using online training of the neural network, no
knowledge about obstacles and motion of the moving object is required. The nonlinear dynamic of the
robot including actuators dynamic is also considered. Numerical simulations performed on a 4DOF
redundant spatial manipulator actuated by DC servomotors, shows effectiveness of the proposed method.
have been introduced to obviate demands for different level of
voltage and power in input and output. The suitable control
scheme for this type of converter necessitates equal voltage
sharing for series connected modules and equal current sharing
for the parallel connected modules for all operating conditions
even with existence of dissimilarity in devices and components
used in each module. Such a control scheme can be designed by
means of linear controllers. But in the case of large variations in
the parameters or considerable difference between modules of
converter, linear scheme cannot accomplish equal sharing
correctly. In this paper a MIMO controller scheme based on
artificial neural network (ANN) will be developed to solve this
problem in an input-series and output-parallel (ISOP) Dc-Dc
converter. The proposed MIMO controller is trained using
Particle Swarm Optimization (PSO). Using PSO to train the
ANN based controller, eliminates the need for a prior knowledge
of the system dynamics. The latter merit shows its significant
usefulness when the under study system is a large multi-module
system and consequently the derivation if its dynamic equations is a tedious and time consuming work. To implement the proposed controller for the ISOP converter MATLAB software will be used in this paper.
probabilistic robust controllers for discrete-time systems whose
objective is to reach and remain in a given target set with
high probability. More precisely, given probability distributions
for the initial state, uncertain parameters and disturbances, we
develop algorithms for designing a control law that i) maximizes
the probability of reaching the target set in N steps and ii)
makes the target set robustly positively invariant. As defined
the problem is nonconvex. To solve this problem, a sequence of
convex relaxations is provided, whose optimal value is shown
to converge to solution of the original problem. In other words,
we provide a sequence of semidefinite programs of increasing
dimension and complexity which can arbitrarily approximate
the solution of the probabilistic robust control design problem
addressed in this paper. Two numerical examples are presented
to illustrate preliminary results
general approach to chance constrained algebraic problems. In
this type of problems, one aims at maximizing the probability
of a set defined by polynomial inequalities. These problems
are, in general, nonconvex and computationally complex. With
the objective of developing systematic numerical procedures
to solve such problems, a sequence of convex relaxations
is provided, whose optimal value is shown to converge to
solution of the original problem. In other words, we provide
a sequence of semidefinite programs of increasing dimension
and complexity which can arbitrarily approximate the solution
of the probability maximization problem. Two numerical
examples are presented to illustrate preliminary results on the
numerical performance of the proposed approach.
inspired optimization strategy, an Imperialist
Competitive Algorithm, is applied to the problem of
designing a fuzzy controller. The goal is to design a
controller to enhance the transient and steady state
behaviour of the system output. Having fixed the rule
base of the fuzzy system, the controller is designed
through determining the membership functions of the
input and output variables. The design method is
applied to a model of vehicle. The inputs of the fuzzy
system are velocity of the vehicle and the sloop of the
road. The fuzzy controller controls the speed of the
vehicle by adjusting the amount of gas into vehicle
engine. Comparison results among the designed
controller and the controller designed by the expert
show that the controller obtained by Imperialist
competitive algorithm has better performance than the
expert controller.
maximizing the probability of a set defined by polynomial inequalities. These problems are, in general,
nonconvex and computationally hard. With the objective of developing systematic numerical
procedures to solve such problems, a sequence of convex relaxations based on the theory of measures
and moments is provided, whose sequence of optimal values is shown to converge to the optimal value
of the original problem. Indeed, we provide a sequence of semidefinite programs of increasing dimension
which can arbitrarily approximate the solution of the original problem. To be able to efficiently
solve the resulting large-scale semidefinite relaxations, a first-order augmented Lagrangian algorithm
is implemented. Numerical examples are presented to illustrate the computational performance of
the proposed approach.