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Babak Salamat
Chair of Embedded Communication Systems, University of Klagenfurt, 9020 Klagenfurt, Austria

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Journal article
Published: 30 August 2021 in IEEE Access
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This paper presents a control-based trajectory generation approach for unmanned aerial vehicles (UAVs) under dynamic constraints. It exploits the concept of optimal control to find closed-form differential equations that satisfy any arbitrary dynamic limitation mapped into kinematic constraints. Pontryagin’s Minimum Principle applies to derive a set of differential equations in which the dynamic environment is considered in the constrained Hamiltonian function. In particular, we aim to minimize the L2-norm of the control input avoiding dynamic obstacles, given initial and final boundary conditions. Lastly, this paper proposes a novel interpolation algorithm based on rational functions, referred to as rational recursive smooth trajectory (RRST) method. The method generates an analytic expression that approximates the control inputs, for which no closed-form solutions are in general attainable.

ACS Style

Babak Salamat; Nunzio A. Letizia; Andrea M. Tonello. Control Based Motion Planning Exploiting Calculus of Variations and Rational Functions: A Formal Approach. IEEE Access 2021, PP, 1 -1.

AMA Style

Babak Salamat, Nunzio A. Letizia, Andrea M. Tonello. Control Based Motion Planning Exploiting Calculus of Variations and Rational Functions: A Formal Approach. IEEE Access. 2021; PP (99):1-1.

Chicago/Turabian Style

Babak Salamat; Nunzio A. Letizia; Andrea M. Tonello. 2021. "Control Based Motion Planning Exploiting Calculus of Variations and Rational Functions: A Formal Approach." IEEE Access PP, no. 99: 1-1.

Journal article
Published: 22 February 2021 in IEEE Transactions on Robotics
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This aticle presents a novel recursive smooth trajectory (RST) generation algorithm for application in robotics and in particular for unmanned aerial vehicles (UAVs). RST builds the trajectory recursively as a smooth polynomial path, thus a closed form trajectory satisfying any arbitrary dynamic limitation that translates into kinematic constraints (e.g., position, velocity, acceleration, etc.). Uncertainties and perturbations in the constraints are also discussed, modeled, and finally included in the calculation of the polynomial trajectory coefficients. Moreover, due to its recursive formulation, RST enables an immediate extension toward the solution of a trajectory optimization problem. In particular, it is shown that the minimum-snap piecewise polynomial trajectory can be interpreted as a special case of RST. The effectiveness of the proposed algorithm is demonstrated numerically via two illustrative scenarios. Its application to a UAV structure is also discussed to highlight the advantage of a smooth path over a piecewise one. Finally, the computational complexity and memory requirements are analyzed.

ACS Style

Nunzio A. Letizia; Babak Salamat; Andrea M. Tonello. A Novel Recursive Smooth Trajectory Generation Method for Unmanned Vehicles. IEEE Transactions on Robotics 2021, PP, 1 -14.

AMA Style

Nunzio A. Letizia, Babak Salamat, Andrea M. Tonello. A Novel Recursive Smooth Trajectory Generation Method for Unmanned Vehicles. IEEE Transactions on Robotics. 2021; PP (99):1-14.

Chicago/Turabian Style

Nunzio A. Letizia; Babak Salamat; Andrea M. Tonello. 2021. "A Novel Recursive Smooth Trajectory Generation Method for Unmanned Vehicles." IEEE Transactions on Robotics PP, no. 99: 1-14.

Journal article
Published: 16 November 2020 in IEEE Robotics and Automation Letters
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We propose a mechanical system named swash mass pendulum (SMP) with application in robotics, and we develop a passivity-based control approach. The SMP is a pendulum made of a rigid shaft connected to a pair of cross-shafts where four swash masses can move under the action of servo mechanisms. The control objective is to stabilize the pendulum at a desired rest orientation. The regulation at the desired orientation is obtained via energy shaping that prevents the solution of partial differential equations. The derivation of the control law consists of a) partial feedback linearization under input coupling, b) a proportional plus integral (PI) controller acting on two new passive outputs. The performance of the controller is assessed by both simulations and experimental results, showing the effectiveness of the control design for the novel proposed pendulum.

ACS Style

Babak Salamat; Andrea M. Tonello. A Swash Mass Pendulum with Passivity-Based Control. IEEE Robotics and Automation Letters 2020, 6, 199 -206.

AMA Style

Babak Salamat, Andrea M. Tonello. A Swash Mass Pendulum with Passivity-Based Control. IEEE Robotics and Automation Letters. 2020; 6 (1):199-206.

Chicago/Turabian Style

Babak Salamat; Andrea M. Tonello. 2020. "A Swash Mass Pendulum with Passivity-Based Control." IEEE Robotics and Automation Letters 6, no. 1: 199-206.

Journal article
Published: 23 April 2020 in IEEE Access
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We consider a small helicopter structure that is maneuvered through the control of moving masses. It is referred to as a swash mass helicopter (SMH). This paper addresses the trajectory tracking control problem for the SMH, with a specific focus on the decoupling change of coordinates of both rotational and translational dynamics.We propose a control scheme in which position tracking is the primary objective, while the attitude tracking task is considered as a secondary objective. The intermediate control signals related to the attitude dynamics exploit the structural properties of the SMH and are enhanced with terms that grant a more accurate tracking of the target trajectory. The closed-loop system stability under the trajectory tracking objective is obtained following the Interconnection and Damping Assignment Passivity- Based Control (IDA-PBC) approach. In addition, the presence of external disturbances can diminish the trajectory tracking performance. For this reason, a nonlinear outer loop controller is added to the IDAPBC to compensate the disturbances. Finally, the results of several simulations are reported to evaluate the performance of the control strategy.

ACS Style

Babak Salamat; Andrea M. Tonello. Energy Based Control of a Swash Mass Helicopter Through Decoupling Change of Coordinates. IEEE Access 2020, 8, 77449 -77458.

AMA Style

Babak Salamat, Andrea M. Tonello. Energy Based Control of a Swash Mass Helicopter Through Decoupling Change of Coordinates. IEEE Access. 2020; 8 (99):77449-77458.

Chicago/Turabian Style

Babak Salamat; Andrea M. Tonello. 2020. "Energy Based Control of a Swash Mass Helicopter Through Decoupling Change of Coordinates." IEEE Access 8, no. 99: 77449-77458.

Preprint
Published: 13 September 2019
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In this paper, a new unmanned aerial vehicle (UAV) structure, referred to as swash mass UAV, is presented. It consists of a double blade coaxial shaft rotor and four swash masses that allow changing the orientation and maneuvering the UAV. The dynamical system model is derived from the Newton\textquotesingle s law framework. The rotational behavior of the UAV is discussed as a function of the design parameters. Given the uniqueness and the form of the obtained non-linear dynamical system model, a back-stepping control mechanism is proposed. It is obtained following the Lyapunov's control approach in each iteration step. Numerical results show that the swashed mass UAV can be maneuvered with the proposed control algorithm so that linear and aggressive trajectories can be accurately tracked.

ACS Style

Andrea M. Tonello; Babak Salamat. A Swash Mass Unmanned Aerial Vehicle: Design, Modeling and Control. 2019, 1 .

AMA Style

Andrea M. Tonello, Babak Salamat. A Swash Mass Unmanned Aerial Vehicle: Design, Modeling and Control. . 2019; ():1.

Chicago/Turabian Style

Andrea M. Tonello; Babak Salamat. 2019. "A Swash Mass Unmanned Aerial Vehicle: Design, Modeling and Control." , no. : 1.

Conference paper
Published: 01 March 2018 in 2018 IEEE Aerospace Conference
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This paper presents a novel realistic trajectory generation method for quadrotor helicopters that minimizes the fifth derivative of location with respect to time (crackle) so that smooth trajectories are obtained in typical flight scenarios. The trajectories are implemented with higher order polynomials that grant smoothness in the motor thrust. In order to tune the parameters of the polynomials in the search space, a multi-objective optimization method called particle swarm optimization (PSO) is used. The proposed technique satisfies the constraints imposed by the configuration of the quadrotor helicopter. Other particular constraints can be introduced such as: obstacle avoidance, speed limitation, attitude constraints and actuator torque limitations due to the practical feasibility of the trajectories. Furthermore, a novel adaptive evolutionary feedback controller (EFC) is described and it is used to follow these trajectories. The solution to the control problem is also obtained using PSO. Several numerical results are presented to assess the performance of the proposed trajectory generation and control methods.

ACS Style

Babak Salamat; Andrea M. Tonello. Novel trajectory generation and adaptive evolutionary feedback controller for quadrotors. 2018 IEEE Aerospace Conference 2018, 1 -8.

AMA Style

Babak Salamat, Andrea M. Tonello. Novel trajectory generation and adaptive evolutionary feedback controller for quadrotors. 2018 IEEE Aerospace Conference. 2018; ():1-8.

Chicago/Turabian Style

Babak Salamat; Andrea M. Tonello. 2018. "Novel trajectory generation and adaptive evolutionary feedback controller for quadrotors." 2018 IEEE Aerospace Conference , no. : 1-8.

Conference paper
Published: 01 October 2017 in 2017 International Conference on Smart Systems and Technologies (SST)
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Although there are classical methods for designing a feedback controller, modern techniques make use of the power of multi objective optimization algorithms. In this paper, we propose a novel evolutionary feedback controller (EFC) for altitude and attitude tracking of a quadrotor helicopter unmanned aerial vehicle (UAV). In particular, an improved genetic algorithm is used to adapt the coefficients of the feedback control gains. Contrary to classical approaches in control theory, the EFC methodology can be used in both nonlinear and linear systems. Furthermore, an inertial navigation system (INS) and global positioning system (GPS) are embedded in the UAV to provide inputs to the controller.

ACS Style

Babak Salamat; Andrea M. Tonello. Altitude and attitude tracking of a quadrotor helicopter UAV using a novel evolutionary feedback controller. 2017 International Conference on Smart Systems and Technologies (SST) 2017, 327 -331.

AMA Style

Babak Salamat, Andrea M. Tonello. Altitude and attitude tracking of a quadrotor helicopter UAV using a novel evolutionary feedback controller. 2017 International Conference on Smart Systems and Technologies (SST). 2017; ():327-331.

Chicago/Turabian Style

Babak Salamat; Andrea M. Tonello. 2017. "Altitude and attitude tracking of a quadrotor helicopter UAV using a novel evolutionary feedback controller." 2017 International Conference on Smart Systems and Technologies (SST) , no. : 327-331.

Journal article
Published: 08 May 2017 in Aerospace
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The aim of this paper is to provide a realistic stochastic trajectory generation method for unmanned aerial vehicles that offers a tool for the emulation of trajectories in typical flight scenarios. Three scenarios are defined in this paper. The trajectories for these scenarios are implemented with quintic B-splines that grant smoothness in the second-order derivatives of Euler angles and accelerations. In order to tune the parameters of the quintic B-spline in the search space, a multi-objective optimization method called particle swarm optimization (PSO) is used. The proposed technique satisfies the constraints imposed by the configuration of the unmanned aerial vehicle (UAV). Further particular constraints can be introduced such as: obstacle avoidance, speed limitation, and actuator torque limitations due to the practical feasibility of the trajectories. Finally, the standard rapidly-exploring random tree (RRT*) algorithm, the standard (A*) algorithm and the genetic algorithm (GA) are simulated to make a comparison with the proposed algorithm in terms of execution time and effectiveness in finding the minimum length trajectory.

ACS Style

Babak Salamat; Andrea M. Tonello. Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs). Aerospace 2017, 4, 27 .

AMA Style

Babak Salamat, Andrea M. Tonello. Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs). Aerospace. 2017; 4 (2):27.

Chicago/Turabian Style

Babak Salamat; Andrea M. Tonello. 2017. "Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs)." Aerospace 4, no. 2: 27.