Robotics and Mechatronics

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    ADVANCED STEPS IN BIPED ROBOTICS: INNOVATIVE DESIGN AND INTUITIVE CONTROL THROUGH SPRING-DAMPER ACTUATOR
    (4th IEEE/RAS International Conference on Humanoid Robots, 2004) Scarfogliero, Umberto; Folgheraiter, Michele; Gini, Giuseppina
    This paper focuses on the study and design of an anthropomorphical light biped robot. The robot presents a total of twelve degree of freedom that will permit it to act a walk in a three dimensional space, right now tested only in simulation. Each joint resemble the functionalities of the human articulation and is moved by tendon connected with actuator located in the robot’s pelvis. We implemented and tested an innovative actuator that permits to set the joint stiffness in real time maintaining a simple position control paradigm. The controller is able to estimate the external load measuring the spring deflection and demonstrated to be particularly robust respect to system uncertainties, such as inertia value changes. Comparing the resulting control law with existing models we found several similarities with the Equilibrium Point Theory.
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    A Multi-Modal haptic interface for Virtual Reality and Robotics
    (Journal of Intelligent and Robotic Systems, 2008-08) Folgheraiter, Michele; Gini, Giuseppina; Vercesi, Dario
    In this paper we present an innovative haptic device that combines the electro-tactile stimulation with the force and visual feedbacks in order to improve the perception of a virtual world. We discuss about the sensation evoked in a user by the haptic, force, and visual interface provided by this device, implemented as a special glove, equipped with sensors and actua- tors connected to a PC. The techniques used to recreate tactile and kines- thetic sensations are based on an innovative use of cutaneous stimulation integrated with actuators and 3D modelling techniques. We discuss about the specificity of haptic interfaces, their controllers, their open problems. We present results about generating the sensation of touching virtual ob- jects with our device. Experiments show also that, using a multi-modal sensorial pattern of stimulation, the subject perceives more realistically the virtual object. We discuss about possible use of the same technique as a way to interface intelligent robots.
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    SDRE-Based Near Optimal Control System Design for PM Synchronous Motor
    (IEEE, 2011) Do, Ton Duc; Choi, Han Ho; Jung, Jin-Woo
    This paper presents a nonlinear optimal speed controller based on a state-dependent Riccati equation (SDRE) for permanent magnet synchronous motor (PMSM). An SDREbased near optimal load torque observer is also proposed to provide the load torque information for the controller. In both designs, the stability is analytically proven and Taylor series method is used to find an approximate solution because the SDRE cannot be directly solved. The SDRE-based optimal controller and observer can ensure better control performance such as no overshoot and fast transient response in speed tracking than the linear conventional controllers such as LQ regulator and PI controller even under the variations of the model parameters and load torque. The proposed SDRE-based control strategy is implemented on a PMSM testbed using TMS320F28335 DSP. The simulation and experimental results are given to prove the feasibility of the proposed control scheme
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    An Adaptive Voltage Control Strategy of Three- Phase Inverter for Standalone Distributed Generation Systems
    (2012) Do, Ton Duc; Leu, Viet Quoc; Choi, Young-Sik; Choi, Han Ho; Jung, Jin-Woo
    This paper proposes an adaptive control method of three-phase inverter for standalone distributed generation systems (DGSs). The proposed voltage controller includes two control terms: an adaptive compensating term and a stabilizing term. The adaptive compensating control term is constructed to avoid directly calculating the time derivatives of state variables. Meanwhile, the stabilizing control term is designed to asymptotically stabilize the error dynamics of the system. Also, a fourth-order optimal load current observer is proposed to reduce the number of current sensors and enhance the system reliability and cost effectiveness. Stability of the proposed voltage controller and the proposed load current observer is fully proven by using Lyapunov theory. The proposed control system can establish good voltage regulation such as fast dynamic response, small steady state error, and low total harmonic distortion (THD) under sudden load change, unbalanced load, and nonlinear load. Finally, the validity of the proposed control strategy is verified through simulations and experiments on a prototype DGS testbed with a TMS320F28335 DSP. For a comparative study, the feedback linearization for multi-input and multi-output (FLMIMO) control scheme is implemented and its results are presented in this paper
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    MOBY-DIC: A MATLAB Toolbox for Circuit-Oriented Design of Explicit MPC
    (IFAC Proceedings Volumes, 2012-01-01) Oliveri, Alberto; Barcelli, Davide; Bemporad, Alberto; Genuit, Bart; Heemels, Maurice; Poggi, Tomaso; Rubagotti, Matteo; Storace, Marco; Alberto, Oliveri
    Abstract This paper describes a MATLAB Toolbox for the integrated design of Model Predictive Control (MPC) state-feedback control laws and the digital circuits implementing them. Explicit MPC laws can be designed using optimal and sub-optimal formulations, directly taking into account the specifications of the digital circuit implementing the control law (such as latency and size), together with the usual control specifications (stability, performance, constraint satisfaction). Tools for a-posteriori stability analysis of the closed-loop system, and for the simulation of the circuit in Simulink, are also included in the toolbox.
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    Minimizing inter-subject variability in fNIRS-based brain–computer interfaces via multiple-kernel support vector learning
    (Medical Engineering & Physics, 2013-12-01) Abibullaev, Berdakh; An, Jinung; Jin, Sang-Hyeon; Lee, Seung Hyun; Moon, Jeon Il; Berdakh, Abibullaev
    Abstract Brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain–computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers’ feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects.
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    Actuation Design Methodology for Haptic Interfaces and Rehabilitation Systems
    (IEEE 8th International Conference on Application of Information and Communication Technologies, 2014) Folgheraiter, Michele
    This paper introduces a methodology and a software framework intended to optimize and speed up the design process of a haptic interface or a rehabilitation system. Starting from an initial mechanical design the procedure allows to export the kinematic and dynamic properties of the robotic system in a simulation environment. The software receives as additional input the Cartesian or joints trajectories and generates as output the required torques at the joints. From the recorded measurements the program extracts the torque ranges necessary to choose a suitable actuation system for the robot. The possibility to run the simulation in batch modality allows also to define different optimization techniques that may be used to reduce the overall system weight or increase its payload
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    A Chaotic Neural Network as Motor Path Generator for Mobile Robotics
    (IEEE International Conference on Robotics and Biomimetics, 2014) Folgheraiter, Michele; Gini, Giuseppina
    This work aims at developing a motor path generator for applications in mobile robotics based on a chaotic neural network. The computational paradigm inspired by the neural structure of microcircuits located in the human prefrontal cortex is adapted to work in real-time and used to generate the joints trajectories of a lightweight quadruped robot. The recurrent neural network was implemented in Matlab and a software framework was developed to test the performances of the system with the robot dynamic model. Preliminary results demonstrate the capability of the neural controller to learn period signals in a short period of time allowing adaptation during the robot operation
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    Adaptive PID Speed Control Design for Permanent Magnet Synchronous Motor Drives
    (2014) Jung, Jin-Woo; Leu, Viet Quoc; Do, Ton Duc; Kim, Eun-Kyung; Choi, Han Ho; Leu, Viet Quoc
    This paper proposes an adaptive proportionalintegralderivative (PID) speed control scheme for permanent magnet synchronous motor (PMSM) drives. The proposed controller consists of three control terms: a decoupling term, a PID term, and a supervisory term. The first control term is employed to compensate for the nonlinear factors, the second term is made to automatically adjust the control gains, and the third one is designed to guarantee the system stability. Different from the offline-tuning PID controllers, the proposed adaptive controller includes adaptive tuning laws to online adjust the control gains based on the gradient descent method. Thus, it can adaptively deal with any system parameter uncertainties in reality. The proposed scheme is not only simple and easy to implement, but also it guarantees an accurate and fast speed tracking. It is proven that the control system is asymptotically stable. To confirm the effectiveness of the proposed algorithm, the comparative experiments between the proposed adaptive PID controller and the conventional PID controller are performed on the PMSM drive. Finally, it is validated that the proposed design scheme accomplishes the superior control performance (faster transient response and smaller steady-state error) compared to the conventional PID method in the presence of parameter uncertainties
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    Asymptotic Vision-Based Tracking Control of the Quadrotor Aerial Vehicle
    (2015) Asl, Hamed Jabbari; Do, Ton Duc
    This paper proposes an image-based visual servo (IBVS) controller for the 3D translational motion of the quadrotor unmanned aerial vehicle (UAV). The main purpose of this paper is to provide asymptotic stability for vision-based tracking control of the quadrotor in the presence of uncertainty in the dynamic model of the system. The aim of the paper also includes the use of ow of image features as the velocity information to compensate for the unreliable linear velocity data measured by accelerometers. For this purpose, the mathematical model of the quadrotor is presented based on the optic ow of image features which provides the possibility of designing a velocity-free IBVS controller with considering the dynamics of the robot. The image features are de ned from a suitable combination of perspective image moments without using the model of the object. This property allows the application of the proposed controller in unknown places. The controller is robust with respect to the uncertainties in the transla- tional dynamics of the system associated with the target motion, image depth and external disturbances. Simulation results and a comparison study are presented which demonstrate the e ectiveness of the proposed approach.
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    An Optimized Field Function Scheme for Nanoparticle Guidance in Magnetic Drug Targeting systems
    (IEEE, 2015) Do, Ton Duc; Noh, Yeongil; Kim, Myeong Ok; Yoon, Jungwon
    Magnetic drug targeting is an approach to guide and concentrate magnetic nanoparticles (MNPs) into the diseased target organ after being injected into blood vessels. Although many works for drug targeting have been conducted, there are few studies on delivering the nanoparticles to the target region. Drug delivery performance has not been addressed sufficiently or fully. In this paper, we investigate the effect of dominant factors to MNPs delivery performance. Then, an optimized field function scheme with a pulsed magnetic actuation is proposed to significantly improve the MNPs guidance performance. With a specific condition of blood vessel size, particle size, and applied magnetic field, the optimized parameters of the field function are selected through extensive simulation studies. We find out that the optimal negative and positive time for the magnetic pulsed field mainly depends on the exit time for particles to reach the bifurcation and the critical time as the maximum time for them to reach the vessels wall, respectively. With the chosen parameters, we show that ratios of correctly guided particles in a Y-channel are reached to 100%. In addition, to minimize the power consumption, a modified field function (MFF) scheme is introduced. The MFF includes a no-power time, called zero-time, between the positive and negative time. It is shown that with the proposed MFF, the energy consumption and the heating problem of the actuator system can be significantly reduced. Therefore, the proposed guidance scheme for MNPs can overcome the sticking issue and maximize the guidance performance as well as reducing the power consumption. It should be noted that the MFF can be easily implement by programmable DC power supplies connected to electromagnetic coils
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    θ-D Approximation Technique for Nonlinear Optimal Speed Control Design of Surface-Mounted PMSM Drives
    (2015) Do, Ton Duc; Choi, Han Ho; Jung, Jin-Woo
    This paper proposes nonlinear optimal controller and observer schemes based on a θ-D approximation approach for surface-mounted permanent magnet synchronous motors (PMSMs). By applying the θ-D method in both the controller and observer designs, the unsolvable Hamilton–Jacobi–Bellman equations are switched to an algebraic Riccati equation and statedependent Lyapunov equations (SDLEs). Then, through selecting the suitable coefficient matrices, the SDLEs become algebraic, so the complex matrix operation technique, i.e., the Kronecker product applied in the previous papers to solve the SDLEs is eliminated. Moreover, the proposed technique not only solves the problem of controlling the large initial states, but also avoids the excessive online computations. By utilizing a more accurate approximation method, the proposed control system achieves superior control performance (e.g., faster transient response, more robustness under the parameter uncertainties and load torque variations) compared to the state-dependent Riccati equation-based control method and conventional PI controlmethod. The proposed observer-based control methodology is tested with an experimental setup of a PMSM servo drive using a Texas Instruments TMS320F28335 DSP. Finally, the experimental results are shown for proving the effectiveness of the proposed control approach
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    An Electromagnetic Steering System for Magnetic Nanoparticle Drug Delivery
    (2015) Do, Ton Duc; Noh, Yeongil; Kim, Myeong Ok; Yoon, Jungwon
    Targeted delivery of pharmaceutical agents to the brain using magnetic nanoparticles (MNPs) is an efficient technique to transport molecules to disease locations. MNPs can cross the blood–brain barrier (BBB) and can be concentrated at a specific location in the brain using non-invasive electromagnetic forces. The proposed EMA consists of two coil-core system. The cores are added in the center of each coil to concentrate the flux in the region of interest. The EMA can enhance the gradient field 10 times compared to only coil system and generate the maximum magnetic field of 160 mT and 5.6 T/m. A 12-kW direct-current power supply was used to generate sufficient magnetic forces on the MNPs by regulating the input currents of the coils. Effective guidance of MNPs is demonstrated via simulations and experiments using 800-nm-diameter MNPs in a Y-shaped channel. The developed EMA system has high potentials to increase BBB crossing of MNPs for efficient drug targeting to brain regions
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    Children’s perception of synthesized voice: Robot’s gender, age and accent
    (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015) Sandygulova, Anara; O’Hare, Gregory M. P.
    This paper presents a study of children’s responses to the perceived gender and age of a humanoid robot Nao that communicated with four genuine synthesized child voices. This research investigates children’s preferences for an English accent. Results indicate that manipulations of robot’s age and gender are successful for all voice conditions, however some voices are preferred over the others by children in Ireland.
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    A Nonlinear Sliding Mode Controller for IPMSM Drives with an Adaptive Gain Tuning Rule
    (2015) Jung, Jin-Woo; Dang, Dong Quang; Vu, Nga Thi-Thuy; Justo, Jackson John; Do, Ton Duc; Choi, Han Ho; Kim, Tae Heoung
    This paper presents a nonlinear sliding mode control (SMC) scheme with a variable damping ratio for interior permanent magnet synchronous motors (IPMSMs). First, a nonlinear sliding surface whose parameters change continuously with time is designed. Actually, the proposed SMC has the ability to reduce the settling time without an overshoot by giving a low damping ratio at the initial time and a high damping ratio as the output reaches the desired setpoint. At the same time, it enables a fast convergence in finite time and eliminates the singularity problem with the upper bound of an uncertain term, which cannot be measured in practice, by using a simple adaptation law. To improve the efficiency of a system in the constant torque region, the control system incorporates the maximum torque per ampere (MTPA) algorithm. The stability of the nonlinear sliding surface is guaranteed by Lyapunov stability theory. Moreover, a simple sliding mode observer is used to estimate the load torque and system uncertainties. The effectiveness of the proposed nonlinear SMC scheme is verified using comparative experimental results of the linear SMC scheme when the speed reference and load torque change under system uncertainties. From these experimental results, the proposed nonlinear SMC method reveals a faster transient response, smaller steady-state speed error, and less sensitivity to system uncertainties than the linear SMC method
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    Nonlinear Optimal DTC Design and Stability Analysis for Interior Permanent Magnet Synchronous Motor Drives
    (2015) Do, Ton Duc; Choi, Han Ho; Jung, Jin-Woo
    This paper presents a nonlinear optimal direct torque control (DTC) scheme of interior permanent magnet synchronous motors (IPMSMs) based on an offline approximation approach for electric vehicle (EV) applications. First, the DTC problem is reformulated in the stationary reference frame in order to avoid estimating the stator flux angle, which the previous DTC schemes in the rotating stator reference frame require. Thus, the proposed DTC method eliminates the Park’s transformation and consequently it reduces the computational efforts. Particularly, since the estimated stator flux angle is not accurate in low speed range, the proposed method that does not need this information can significantly improve the control performance. Moreover, a nonlinear optimal DTC algorithm is proposed to deal with the nonlinearity of the IPMSM drive system. In this paper, a simple offline θ-D approximation technique is utilized to appropriately determine the controller gains. Via an IPMSM test-bed with a TI TMS320F28335 DSP, the experimental results demonstrate the feasibility of the proposed DTC method by accomplishing better control performances (e.g., more stable in low speed region, much smaller speed and torque ripples, and faster dynamic responses) compared to the conventional proportionalintegral (PI) DTC scheme under various scenarios with the existence of parameter uncertainties
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    Locomotion Strategy Selection for a Hybrid Mobile Robot Using Time of Flight Depth Sensor
    (Journal of Sensors, 2015-03-22) Saudabayev, Artur; Kungozhin, Farabi; Nurseitov, Damir; Varol, Huseyin Atakan
    The performance of a mobile robot can be improved by utilizing different locomotion modes in various terrain conditions. This creates the necessity of having a supervisory controller capable of recognizing different terrain types and changing the locomotion mode of the robot accordingly. This work focuses on the locomotion strategy selection problem for a hybrid legged wheeled mobile robot. Supervisory control of the robot is accomplished by the terrain recognizer, which classifies depth images obtained from a commercial time of flight depth sensor and selects different locomotion mode subcontrollers based on the recognized terrain type. For the terrain recognizer, a database is generated consisting of five terrain classes (Uneven, LevelGround, StairUp, StairDown, and Nontraversable). Depth images are enhanced using confidence map based filtering. The accuracy of the terrain classification using Support VectorMachine classifier for the testing database in five-class terrain recognition problem is 97%. Real-world experiments assess the locomotion abilities of the quadruped and the capability of the terrain recognizer in real-time settings. The results of these experiments show depth images processed in real time using machine learning algorithms can be used for the supervisory control of hybrid robots with legged andwheeled locomotion capabilities.
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    Closed-Loop Control of Variable Stiffness Actuated Robots via Nonlinear Model Predictive Control
    (IEEE Access, 2015-04-10) Zhakatayev, Altay; Rubagotti, Matteo; Varol, Huseyin Atakan
    Variable stiffness actuation has recently attracted great interest in robotics, especially in areas involving a high degree of human robot interaction. After investigating various design approaches for variable stiffness actuated (VSA) robots, currently the focus is shifting to the control of these systems. Control of VSA robots is challenging due to the intrinsic nonlinearity of their dynamics and the need to satisfy constraints on input and state variables.Contrary to the partially open-loop state-of-the-art approaches, in this paper, we present a close-loop control framework for VSA robots leveraging recent increases in computational resources and advances in optimization algorithms. In particular, we generate reference trajectories by means of open-loop optimal control, and track these trajectories via nonlinear model predictive control in a closed-loop manner. In order to show the advantages of our proposed scheme with respect to the previous (partially open-loop) ones, extensive simulation and real-world experiments were conducted using a two link planar manipulator for a ball throwing task. The results of these experiments indicate that the closed-loop scheme outperforms the partially open loop one due to its ability to compensate for model uncertainties and external disturbances, while satisfying the imposed constraints.
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    Sensors for Robotic Hands: A Survey of State of the Art
    (IEEE Access, 2015-10-12) Saudabayev, Artur; Varol, Huseyin Atakan
    Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands.
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    Functionalized Magnetic Force Enhances Magnetic Nanoparticle Guidance: From Simulation to Crossing of the Blood-Brain Barrier in vivo
    (2016) Do, Ton Duc; Amin, Faiz Ul; Noh, Yeongil; Kim, Myeong Ok; Yoon, Jungwon
    In recent studies, we introduced the concept of functionalized magnetic force as a method to prevent nanoparticles from sticking to vessel walls caused by extensive simulation and in vitro experiments involving a Y-shaped channel. In this study, we further investigated the effectiveness of the functionalized magnetic force with a realistic 3D vessel through simulations. For the simulations, we considered a more realistic continuous injection of particles with different magnetic forces and frequencies. Based on the results from our simulation studies, we performed in vivo mice experiments to evaluate the effectiveness of using a functionalized magnetic force to aid magnetic nanoparticles (MNPs) in crossing the blood-brain barrier (BBB). To implement the functionalized magnetic force, we developed an electromagnetic actuator regulated by a programmable direct current (DC) power supply. Our results indicate that a functionalized magnetic field can effectively prevent MNPs from sticking, and also guide them across the BBB. We used 770-nm fluorescent carboxyl MNPs in this study. Following intravenous administration of MNPs into mice, we applied an external magnetic field (EMF) to mediate transport of the MNPs across the BBB and into the brain. Furthermore, we evaluated the differential effects of functionalized magnetic fields (0.25, 0.5, and 1 Hz) and constant magnetic fields on the transport of MNPs across the BBB. Our results showed that a functionalized magnetic field is more effective than a constant magnetic field in the transport and uptake of MNPs across the BBB in mice. Specifically, applying a functionalized magnetic field with a 3 A current and 0.5 Hz frequency mediated the greatest transport and uptake of MNPs across the BBB in mice