Saeed Khorashadizadeh

Bio: Saeed Khorashadizadeh is an academic researcher from University of Birjand. The author has contributed to research in topics: Control theory & Adaptive control. The author has an hindex of 16, co-authored 36 publications receiving 650 citations. Previous affiliations of Saeed Khorashadizadeh include University of Shahrood.

Robust control of electrically driven robots by adaptive fuzzy estimation of uncertainty

Abstract: This paper presents a novel robust decentralized control of electrically driven robot manipulators by adaptive fuzzy estimation and compensation of uncertainty. The proposed control employs voltage control strategy, which is simpler and more efficient than the conventional strategy, the so-called torque control strategy, due to being free from manipulator dynamics. It is verified that the proposed adaptive fuzzy system can model the uncertainty as a nonlinear function of the joint position error and its time derivative. The adaptive fuzzy system has an advantage that does not employ all system states to estimate the uncertainty. The stability analysis, performance evaluation, and simulation results are presented to verify the effectiveness of the method. A comparison between the proposed Nonlinear Adaptive Fuzzy Control (NAFC) and a Robust Nonlinear Control (RNC) is presented. Both control approaches are robust with a very good tracking performance. The NAFC is superior to the RNC in the face of smooth uncertainty. In contrast, the RNC is superior to the NAFC in the face of sudden changes in uncertainty. The case study is an articulated manipulator driven by permanent magnet dc motors.

Uncertainty estimation in robust tracking control of robot manipulators using the Fourier series expansion

Abstract: This paper presents a novel control algorithm for electrically driven robot manipulators. The proposed control law is simple and model-free based on the voltage control strategy with the decentralized structure and only joint position feedback. It works for both repetitive and non-repetitive tasks. Recently, some control approaches based on the uncertainty estimation using the Fourier series have been presented. However, the proper value for the fundamental period duration has been left as an open problem. This paper addresses this issue and intuitively shows that in order to perform repetitive tasks; the least common multiple (LCM) of fundamental period durations of the desired trajectories of the joints is a proper value for the fundamental period duration of the Fourier series expansion. Selecting the LCM results in the least tracking error. Moreover, the truncation error is compensated by the proposed control law to make the tracking error as small as possible. Adaptation laws for determining the Fourier series coefficients are derived according to the stability analysis. The case study is an SCARA robot manipulator driven by permanent magnet DC motors. Simulation results and comparisons with a voltage-based controller using adaptive neuro-fuzzy systems show the effectiveness of the proposed control approach in tracking various periodic trajectories. Moreover, the experimental results on a real SCARA robot manipulator verify the successful practical implementation of the proposed controller.

Robust task-space control of robot manipulators using Legendre polynomials for uncertainty estimation

Abstract: This paper deals with the robust task-space control of electrically driven robot manipulators using voltage control strategy. In conventional robust control approaches, the uncertainty bound is needed to design the control law. Usually, this bound is proposed conservatively which may increase the amplitude of the control signal and damage the system. Moreover, calculation of this bound requires some feedbacks of the system states which providing them may be expensive. The novelty of this paper is proposing a robust control law in which the uncertainty bound is calculated by Legendre polynomials. Compared to conventional robust controllers, the proposed controller is simpler, less computational and requires less feedbacks. Simulation results and comparisons verify the effectiveness of the proposed control approach applied on a SCARA robot manipulator driven by permanent magnet DC motors.

Optimal robust voltage control of electrically driven robot manipulators

Abstract: This paper develops a novel robust optimal voltage control of electrically driven robot manipulators. The whole robotic system including the robot manipulator and motors is considered in the control problem. Particle Swarm Optimization (PSO) is used to optimize the control design parameters, thus the performance of control system is highly improved. Beside this, we use Voltage Control Strategy (VCS) which is more robust, faster, less coupled, and less computational compared with the common strategy called as Torque Control Strategy (TCS). To state these advantages, it is reasoning that the TCS is dependent on the manipulator dynamics whereas the VCS can be free from it. The robust optimal voltage control is verified by convergence analysis. A comparative study between the VCS and the TCS confirms superiority of the VCS to the TCS. Simulation results present effectiveness of the proposed methods applied on a spherical robot manipulator driven by permanent magnet dc motors.

Robust task-space control of robot manipulators using differential equations for uncertainty estimation

Abstract: SUMMARY Most control algorithms for rigid-link electrically driven robots are given in joint coordinates. However, since the task to be accomplished is expressed in Cartesian coordinates, inverse kinematics has to be computed in order to implement the control law. Alternatively, one can develop the necessary theory directly in workspace coordinates. This has the disadvantage of a more complex robot model. In this paper, a robust control scheme is given to achieve exact Cartesian tracking without the knowledge of the manipulator kinematics and dynamics, actuator dynamics and nor computing inverse kinematics. The control design procedure is based on a new form of universal approximation theory and using Stone–Weierstrass theorem, to mitigate structured and unstructured uncertainties associated with external disturbances and actuated manipulator dynamics. It has been assumed that the lumped uncertainty can be modeled by linear differential equations. As the method is Model-Free, a broad range of manipulators can be controlled. Numerical case studies are developed for an industrial robot manipulator.

Pivot friction compensation using an accelerometer and a disturbance observer for Hard Disk drives

Abstract: This paper is concerned with the combined use of an accelerometer and a Disturbance Observer (DOB) to compensate for pivot nonlinearities that are subject to uncertainties in Hard Disk Drives (HDDs). The DOB estimates the bias due to pivot nonlinearities such as friction, and this estimate becomes a feedback signal for bias cancellation. Experiments have confirmed that the cancellation scheme is effective in the frequency range from 0 to 700 Hz. Since the proposed cancellation scheme can be implemented as an analog system, it provides wider-bandwidth bias rejection and better settling performance than traditional bias compensation schemes such as integral action based on sampled position error signal. The proposed method can make the distribution of the positioning error at the end of high-speed track-to-track seeks much smaller than that when a digital integral action is used.

Analysis and FPGA Realization of a Novel 5D Hyperchaotic Four-Wing Memristive System, Active Control Synchronization, and Secure Communication Application

Abstract: By introducing a flux-controlled memristor with quadratic nonlinearity into a 4D hyperchaotic system as a feedback term, a novel 5D hyperchaotic four-wing memristive system (HFWMS) is derived in this paper. The HFWMS with multiline equilibrium and three positive Lyapunov exponents presented very complex dynamic characteristics, such as the existence of chaos, hyperchaos, limit cycles, and periods. The dynamic characteristics of the HFWMS are analyzed by using equilibria, phase portraits, poincare map, Lyapunov exponential spectrum, bifurcation diagram, and spectral entropy. Of particular interest is that this novel system can generate two-wing hyperchaotic attractor under appropriate parameters and initial conditions. Moreover, the FPGA realization of the novel 5D HFWMS is reported, which prove that the system has complex dynamic behavior. Finally, synchronization of the 5D hyperchaotic system with different structures by active control and a secure signal masking application of the HFWMS are implemented based on numerical simulations and FPGA. This research demonstrates that the hardware-based design of the 5D HFWMS can be applied to various chaos-based embedded system applications including random number generation, cryptography, and secure communication.