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Force-Guided Robotic Assembly Process: Control and Contact-State Recognition Jasim, Ibrahim Doctoral thesis (2016) This thesis addresses developing novel Contact-State (CS) modeling, control strategy, and environment position localization (position searching) for force-guided robotic assembly processes of rigid and ... [more ▼] This thesis addresses developing novel Contact-State (CS) modeling, control strategy, and environment position localization (position searching) for force-guided robotic assembly processes of rigid and flexible objects. For the CS modeling, the wrench (Cartesian force and torque) signals of the manipulated object are captured for different phases of the considered assembly processes and using the Expectation Maximization-based Gaussian Mixtures Model (EM-GMM), a recognizer is developed for each CS of the assembly. The suggested EM-GMM CS modeling scheme is shown to have excellent Classification Success Rate (CSR) with reduced computational efforts. For the control part, it is shown throughout the thesis that a force-guided robotic assembly process is a hybrid nonlinear system with arbitrary switching signal resulted from the constraints arbitrary switching during the assembly. Furthermore, the robot dynamics is frequently unknown, which is the case in many industrial robots, that would make the force-guided robotic assembly process to be an unknown hybrid nonlinear system with arbitrary switching. In order to overcome such a control challenge, a Decentralized Robust Adaptive Fuzzy Control (DRAFC) strategy is derived that guarantees stable performance under constraints arbitrary switching and unknown dynamics. For the environment position localization, the EM-GMM CS modeling scheme is integrated with a spiral search path and the precise hole position is identified for cases of position uncertainty. Experiments are conducted on a KUKA Lightweight Robot (LWR) doing different force-guided assembly tasks for rigid and flexible objects. Excellent performance is reported for the proposed EM-GMM CS recognition scheme, the DRAFC strategy, and the suggested position searching algorithm. The suggested EM-GMM CS recognition, DRAFC strategy, and position localization schemes are compared with the availably corresponding schemes and the superiority of the suggested schemes is shown. The reasons behind the superiority of the EM-GMM CS recognition scheme are the accommodation of the captured signals non-stationary behavior, employing optimized number of GMM components in the modeling process, and employing the EM algorithm that iteratively increases the log-likelihood. The causes behind the superiority of the DRAFC strategy are addressing the unknown nonlinear dynamics of the robot, accommodating the constraints arbitrary switching, and the robustness against possible dynamics parameters drift. The reasons behind the surpassing of the suggested position localization strategy are the robustness against the surface roughness and reduced computational efforts. The proposed EM-GMM CS modeling scheme, DRAFC strategy, and position searching scheme are applied to the entire peg-in-hole assembly processes of rigid and flexible objects. Excellent Localization Success Rate (LSR) was resulted when using the suggested schemes. Furthermore, the proposed CS modeling scheme, control strategy, and localization approach are applied to a couple of applications in automotive industry; the first one is the camshaft caps assembly of a cylinder head and the other is the air-intake manifold assembly of a powertrain. Efficient force-guided robotic assembly processes are obtained for both considered applications. [less ▲] Detailed reference viewed: 393 (66 UL)Force-Controlled Robotic Assembly Processes of Rigid and Flexible Objects: Methodologies and Applications Jasim, Ibrahim Book published by Springer (2016) Detailed reference viewed: 102 (5 UL)Model-Free Robust Adaptive Control for Flexible Rubber Objects Manipulation Jasim, Ibrahim ; Plapper, Peter ; Voos, Holger in 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015), Luxembourg 8-11 September 2015 (2015, September 08) This article addresses the control problem of robots with unknown dynamics and manipulating flexible rubber objects of unknown elasticity. The manipulated rubber object is considered to be interacting ... [more ▼] This article addresses the control problem of robots with unknown dynamics and manipulating flexible rubber objects of unknown elasticity. The manipulated rubber object is considered to be interacting with arbitrarily-switched constraints. Such a kind of robot system is shown to have switched impedance parameters during a task execution that results in an unknown hybrid nonlinear system with arbitrarily switched signal. A Model-Free Robust Adaptive Control (MFRAC) strategy is proposed for such a robot system that is proved to guarantee global stable performance with all closed loop signals are assured to be bounded. The suggested MFRAC strategy relies on the synergy of the Adaptive Fuzzy System (AFS), the Sliding Mode Control (SMC), and the notion of Common Lyapunov Functions (CLF). The AFS relaxes the need for knowing the precise robot dynamics, the SMC adds robustness against the drift of the dynamics parameters, and the CLF accommodates the arbitrary switching of the impedance parameters. The bounds of the impedance parameters are adapted online and incorporated in the MFRAC design such that a convergent performance is achieved. Experiment is conducted on a KUKA Lightweight Robot (LWR) doing flexible rubber peg-in-hole assembly process that falls in the category of systems considered in this article. From the experimental results, excellent tracking performance is reported when using the proposed MFRAC strategy for the considered robotic system despite the dynamics anonymity and the unknown impedance parameters arbitrary switching. [less ▲] Detailed reference viewed: 110 (6 UL)Gaussian Filtering for Enhanced Impedance Parameters Identification in Robotic Assembly Processes Jasim, Ibrahim ; Plapper, Peter ; Voos, Holger in 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015), Luxembourg 8-11 September 2015 (2015, September 08) Robot interaction with the environment is normally described as a mass-spring-damping impedance model and the estimation of such interaction impedance parameters requires the computation of the joint (or ... [more ▼] Robot interaction with the environment is normally described as a mass-spring-damping impedance model and the estimation of such interaction impedance parameters requires the computation of the joint (or task) space velocity and acceleration. In many cases, the velocity and acceleration are computed by numerically computing the first and second derivatives of the sensed position signal. The numerical differentiation results in approximation errors when computing the velocity and acceleration signals that would have a direct impact on the estimation of the impedance parameters. This article proposes enhancing the estimation of the impedance parameters by smoothing the velocity and acceleration signals prior to the considered estimation process. Gaussian Smoothing Filter (GSF) is employed in smoothing the considered signals. After the smoothing process, impedance parameters estimation becomes more feasible using the available strategies like the Least Mean Square (LMS) or any other estimation approach. Experiments are conducted on a KUKA Lightweight Robot (LWR) doing the assembly of the air-intake manifold of an automotive powertrain. The impedance parameters are estimated for the smoothed and unsmoothed cases in order to show the enhancement in the estimation process. [less ▲] Detailed reference viewed: 121 (7 UL)Adaptive sliding mode fuzzy control for unknown robots with arbitrarily-switched constraints Jasim, Ibrahim ; Plapper, Peter ; Voos, Holger in Mechatronics (2015), 30C This article addresses the control problem of robots with unknown dynamics and arbitrarily-switched unknown constraints. Such kind of robots will be shown to be unknown hybrid systems with arbitrary ... [more ▼] This article addresses the control problem of robots with unknown dynamics and arbitrarily-switched unknown constraints. Such kind of robots will be shown to be unknown hybrid systems with arbitrary switching and an Adaptive Sliding Mode Fuzzy Control (ASMFC) strategy is proposed that handles the unknown dynamics of the robot along with the unknown constraints arbitrary switching. The ASMFC is a synergy of finding a Common Lyapunov Function (CLF) between the resulted switched subsystems of the considered robots, employing the Fuzzy Logic Systems (FLS), and the use of the Sliding Mode Control (SMC). The CLF accommodates the constraints arbitrary switching, the SMC adds robustness against possible parameters drift, and the FLS approximates the unknown robot dynamics. All unknown parameters are adapted online and all closed loop signals are guaranteed to be bounded. The proposed strategy is validated by conducting an experiment on a KUKA Lightweight Robot (LWR) doing a typical force-guided peg-in-hole assembly task that falls in the category of robot systems under consideration. Excellent tracking performance is obtained when using the ASMFC strategy. Comparison is conducted with the performance of a PD controller that is widely used in commanding industrial robots and the superiority of the proposed strategy is shown. [less ▲] Detailed reference viewed: 177 (17 UL)Contact-State Modelling in Force-Controlled Robotic Peg-in-Hole Assembly Processes of Flexible Objects Using Optimised Gaussian Mixtures Jasim, Ibrahim ; Plapper, Peter ; Voos, Holger in Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture (2015) This article proposes the distribution similarity measure–based Gaussian mixtures model for the contact-state (CS) modelling in force-guided robotic assembly processes of flexible rubber parts. The wrench ... [more ▼] This article proposes the distribution similarity measure–based Gaussian mixtures model for the contact-state (CS) modelling in force-guided robotic assembly processes of flexible rubber parts. The wrench (Cartesian force and torque) signals of the manipulated object are captured for different states of the given assembly process. The distribution similarity measure–based Gaussian mixtures model CS modelling scheme is employed in modelling the captured wrench signals for different CSs. The proposed distribution similarity measure–based Gaussian mixtures model CS modelling scheme uses the Gaussian mixtures model in modelling the captured signals. The parameters of the Gaussian mixtures models are computed using expectation maximisation. The optimal number of Gaussian mixtures model components for each CS model is determined by considering the classification success rate as an index for the similarity measure between the distribution of the captured signals and the developed models. The optimal number of Gaussian mixtures model components corresponds to the highest classification success rate; hence, object elasticity variation would be accommodated by properly choosing the optimal number of Gaussian mixtures model components. The performance of the proposed distribution similarity measure–based Gaussian mixtures model CS modelling strategy is evaluated by a test stand composed of a KUKA lightweight robot doing peg-in-hole assembly processes for flexible rubber objects. Two rubber objects with different elasticity are considered for two experiments; in the first experiment, an elastic peg of 30 Shore A hardness is considered and that of the second experiment has hardness of 6 Shore A which is even softer than the one used in experiment 1. Employing the proposed distribution similarity measure–based Gaussian mixtures model CS modelling strategy excellent classification success rate was obtained for both experiments. However, more Gaussian mixtures model components are required for the softer one that gives a strong impression of the non-stationarity behaviour increment for softer materials. Comparison is performed with the available CS modelling schemes and the distribution similarity measure–based Gaussian mixtures model is shown to provide the best classification success rate performance with a reduced computational time. [less ▲] Detailed reference viewed: 147 (9 UL)Position Identification in Force-Guided Robotic Peg-in-Hole Assembly Tasks Jasim, Ibrahim ; Plapper, Peter ; Voos, Holger in Procedia CIRP (2014), 22 Position uncertainty is inevitable in many force-guided robotic assembly tasks. Such uncertainty can cause a significant delay, extra energy expenditure, and may even results in detriments to the mated ... [more ▼] Position uncertainty is inevitable in many force-guided robotic assembly tasks. Such uncertainty can cause a significant delay, extra energy expenditure, and may even results in detriments to the mated parts or the robot itself. This article suggests a strategy for identifying the accurate hole position in force-guided robotic peg-in-hole assembly tasks through employing only the captured wrench (the Cartesian forces and torques) signals of the manipulated. In the framework of using the Contact-State (CS) modeling for such robotic tasks, the identification of the hole position is realized through detecting the CS that corresponds for the phase of the peg-on-hole, that is the phase in which the peg is located precisely on the hole. Expectation Maximization-based Gaussian Mixtures Model (EM-GMM) CS modeling scheme is employed in detecting the CS corresponding for the peg-on-hole phase. Only the wrench signals are used in modeling and detecting the phases of the assembly process. The considered peg-in-hole assembly process starts from free space and as soon as the peg touches the environment with missing the hole, a spiral search path is followed that would survey the whole environment surface. When the CS of the peg-on-hole is detected, the hole position is identified. Experiments are conducted on a KUKA Lightweight Robot (LWR) doing typical peg-in-hole assembly tasks. Multiple hole positions are considered and excellent performance of the proposed identification strategy is shown. [less ▲] Detailed reference viewed: 141 (16 UL)Enhanced Decentralized Robust Adaptive Control of Robots With Arbitrarily-Switched Unknown Constraints Jasim, Ibrahim ; Plapper, Peter in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC2014) (2014, October 05) In this article, we propose an Enhanced Decentralized Robust Adaptive Control (EDRAC) strategy that guarantees global stable performance for robots under arbitrarily-switched and unknown constraints. The ... [more ▼] In this article, we propose an Enhanced Decentralized Robust Adaptive Control (EDRAC) strategy that guarantees global stable performance for robots under arbitrarily-switched and unknown constraints. The EDRAC strategy is a synergy of the concepts of the decentralized control, finding a Common Lyapunov Function (CLF), and the Sliding Mode Control (SMC). The decentralization in the control action is realized through decomposing the joints dynamics into two parts; a function of the specific joint variables and another one in term of the all joints variables. The first term is assumed to be known and the latter is compensated through adapting its upper bound in the control loop hence relaxing the need for knowing part of the robot dynamics and simplifying the control strategy. The CLF guarantees global stable performance under arbitrary constraints switching, and the SMC makes the EDRAC strategy robust against possible parameters drift. Simulation is performed for a two link robot interacting with two switching constraints and the efficiency of the EDRAC strategy is shown. [less ▲] Detailed reference viewed: 92 (10 UL)Robust Direct Adaptive Fuzzy Control of Flexible Joints Robots with Time-Varying Stiffness/Damping Parameters Jasim, Ibrahim ; Plapper, Peter in Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014) (2014, June 02) In this article, we address the problem of controlling unknown flexible-joint robots with unknown time-varying stiffness and damping parameters. We propose a Robust Direct Adaptive Fuzzy Control (RDAFC ... [more ▼] In this article, we address the problem of controlling unknown flexible-joint robots with unknown time-varying stiffness and damping parameters. We propose a Robust Direct Adaptive Fuzzy Control (RDAFC) strategy that accommodates the dynamics anonymity and joints stiffness/damping variations. The RDAFC strategy relies on the synergy of the concepts of fuzzy logic approximation and the Sliding Mode Control (SMC). The fuzzy logic approximation relaxes the need for knowing the robot dynamics and the SMC accommodates the parameters variations. We also modify the RDAFC strategy to be suited to the KUKA Lightweight Robot (LWR) and propose a control strategy that can accommodates dynamics anonymity, uncertainty and joints elasticity variations. Experimental results are performed on a KUKA LWR moving in free space with its joints stiffness and damping vary with time in sine and cosine waveforms respectively. From the experiments, we can see that excellent tracking performance is obtained when using the RDAFC strategy despite the joints elasticity parameters time-variance and the robot dynamics unavailability. [less ▲] Detailed reference viewed: 57 (6 UL)Contact-State Recognition of Compliant Motion Robots Using Expectation Maximization-Based Gaussian Mixtures Jasim, Ibrahim ; Plapper, Peter in Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014) (2014, June 02) In this article, we address the problem of Contact-State (CS) recognition for force-controlled robotic tasks. At first, the wrench (Cartesian forces and torques) and pose (Cartesian position and ... [more ▼] In this article, we address the problem of Contact-State (CS) recognition for force-controlled robotic tasks. At first, the wrench (Cartesian forces and torques) and pose (Cartesian position and orientation) signals of the manipulated object, in different Contact Formations (CFs) of a task, are collected. Then in the framework of the Bayesian classification, the Expectation Maximization-based Gaussian Mixtures Model (EM-GMM) is used in building efficient CFs classifiers. The use of the EM-GMM in developing the captured signals models accommodates possible signals non-stationarity, i.e. signals abnormal distribution, and enhanced recognition performance would be resulted. Experiments are performed on a KUKA Lightweight Robot (LWR) doing the cube-in-corner assembly task, which is a rigid cube object interacting with an environment composed of three orthogonal planes, and different CFs are considered. From the experimental results, the EM-GMM is shown to have an excellent recognition performance with an enhanced computational time. In order to compare the EM-GMM with the available CF recognition schemes, we developed the corresponding CF classifiers using the Gravitational Search-Fuzzy Clustering Algorithm (GS-FCA), Stochastic Gradient Boosting (SGB), and the Conventional Fuzzy Classifier(CFC) approaches. From the comparison results it is obvious that the EM-GMM scheme is outperforming the rest. [less ▲] Detailed reference viewed: 144 (4 UL)Robust Direct Adaptive Fuzzy Control of Switched Constrained Manipulators with Unknown Dynamics Jasim, Ibrahim ; Plapper, Peter in Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014) (2014, June 02) In this article, we address the problem of controlling robots with arbitrarily-switched constraints and unknown dynamics. Switching between different constraints of a robot would result in a switched ... [more ▼] In this article, we address the problem of controlling robots with arbitrarily-switched constraints and unknown dynamics. Switching between different constraints of a robot would result in a switched nonlinear system that does not inherit the behavior of its individual subsystems. In order to guarantee stable performance of robots with arbitrarily switched constraints and unknown dynamics, we propose a Robust Adaptive Fuzzy Control (RAFC) strategy that can guarantee global stable performance under such challenging conditions. The suggested control strategy relies on the synergy of the Sliding Mode Control (SMC) that adds robustness against possible dynamics parameters drift, finding a Common Lyapunov Function (CLF) that guarantees stability under arbitrary constraints switching, and Direct Adaptive Fuzzy System (DAFS) that relaxes the need for knowing the precise robot dynamics. Experiments are performed on a KUKA Lightweight Robot (LWR) doing camshaft caps assembly of an automotive powertrain. The given robotic assembly process falls in the category of switched constrained robots and the efficiency of the suggested RAFC strategy in controlling such a robotic task will be shown. [less ▲] Detailed reference viewed: 59 (6 UL)Contact-State Monitoring of Force-Guided Robotic Assembly Tasks Using Expectation Maximization-based Gaussian Mixtures Models Jasim, Ibrahim ; Plapper, Peter in International Journal of Advanced Manufacturing Technology (2014), 73(5), 623-633 This article addresses the problem of Contact-State (CS) monitoring for peg-in-hole force-controlled robotic assembly tasks. In order to perform such a monitoring target, the wrench (Cartesian forces and ... [more ▼] This article addresses the problem of Contact-State (CS) monitoring for peg-in-hole force-controlled robotic assembly tasks. In order to perform such a monitoring target, the wrench (Cartesian forces and torques) and pose (Cartesian position and orientation) signals of the manipulated object are firstly captured for different CS's of the object (peg) with respect to the environment including the hole. The captured signals are employed in building a model (a recognizer) for each CS and in the framework of pattern classification the CS monitoring would be addressed. It will be shown that the captured signals are non-stationary, i.e. they have non-normal distribution that would result in performance degradation if using the available monitoring approaches. In this article, the concept of the Gaussian Mixtures Models (GMM) is used in building the likelihood of each signal and the Expectation Maximization (EM) algorithm is employed in finding the GMM parameters. The use of the GMM would accommodate the signals non-stationary behavior and the EM algorithm would guarantee the estimation of the optimal parameters set of the GMM for each signal and hence the modeling accuracy would be significantly enhanced. In order to see the performance of the suggested CS monitoring scheme, we installed a test stand that is composed of a KUKA Lightweight Robot (LWR) doing a typical peg-in-hole tasks. Two experiments are considered; in the first experiment we use the EM-GMM in monitoring a typical peg-in-hole robotic assembly process and in the second experiment we consider the robotic assembly of camshaft caps assembly of an automotive powertrain and use the EM-GMM in monitoring its CS's. For both experiments, the excellent monitoring performance will be shown. Furthermore, we compare the performance of the EM-GMM with that obtained when using available CS monitoring approaches. Classification Success Rate (CSR) and computational time will be considered as comparison indices and the EM-GMM will be shown to have a superior CSR performance with reduced a computational time. [less ▲] Detailed reference viewed: 180 (19 UL)Contact-state modeling of robotic assembly tasks using Gaussian mixture models Jasim, Ibrahim ; Plapper, Peter in Procedia CIRP (2014), 23 This article addresses the Contact-State (CS) modeling problem for the force-controlled robotic peg-in-hole assembly tasks. The wrench (Cartesian forces and torques) and pose (Cartesian position and ... [more ▼] This article addresses the Contact-State (CS) modeling problem for the force-controlled robotic peg-in-hole assembly tasks. The wrench (Cartesian forces and torques) and pose (Cartesian position and orientation) signals, of the manipulated object, are captured for different phases of the robotic assembly task. Those signals are utilized in building a CS model for each phase. Gaussian Mixture Models (GMM) is employed in building the likelihood of each signal and Expectation Maximization (EM) is used in finding the GMM parameters. Experiments are performed on a KUKA Lightweight Robot (LWR) doing camshaft caps assembly of an automotive powertrain. Comparisons are also performed with the available assembly modeling schemes, and the superiority of the EM-GMM scheme is shown with a reduced computational time. [less ▲] Detailed reference viewed: 114 (5 UL)Stable Robust Adaptive Control of Robotic Manipulators with Switched Constraints Jasim, Ibrahim ; Plapper, Peter in Proceedings of the 2013 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013 (2013, August) In this paper, the problem of controlling switched constrained robotic manipulators is addressed. Switched constrained robots are those robots interacting with multiple switched constraints. We start our ... [more ▼] In this paper, the problem of controlling switched constrained robotic manipulators is addressed. Switched constrained robots are those robots interacting with multiple switched constraints. We start our control algorithm with suggesting a sliding mode controller that is proved to provide stable system performance. However, the bounds of the functions, on each link, caused from the constraints are assumed to be known. Then an adaptive sliding mode control strategy is suggested that relaxes the need for knowing the bounds of the constraints functions with guaranteeing global stable performance of the given switched constrained robotic system. Finally, we complement the control strategy above through deriving an improved robust adaptive control scheme that is proved to give a stable performance with reduced chattering. All of the three stages of the suggested control strategy are derived through finding a common Lyapunov function that can stabilize all of the subsystems for the overall switched system. Simulation is carried out for a two link robotic manipulator interacting with two switched constraints. From the simulation results we can see the excellent tracking performance and the high efficiency of the suggested control strategy in controlling switched constrained robotic systems. [less ▲] Detailed reference viewed: 125 (21 UL)Adaptive Sliding Mode Control of Switched Constrained Robotic Manipulators Jasim, Ibrahim ; Plapper, Peter in 2013 IEEE International Conference on Industrial Informatics, Bochum 28-30 July 2013 (2013, July) In this paper, we address the control problem of a constrained robotic manipulators with their constraints continuously switched from one to another. Such a switching in the constraints causes a switching ... [more ▼] In this paper, we address the control problem of a constrained robotic manipulators with their constraints continuously switched from one to another. Such a switching in the constraints causes a switching function to be inserted in the equation of the robot dynamics which may cause transient instability for the overall system. Two robust control strategies are presented in this paper to handle such switched robotic systems. In the first strategy, we assume that the bounds of the constraints are known. A sliding mode stabilizing controller is developed that can guarantee global stable performance of the given robotic system. In the second one, we relax the assumption of knowing the constraints bounds through deriving update laws for those bounds and new control actions that can guarantee global stable performance under such switching constraints. Simulation is performed for a two link robotic system having two switching constraints. The results obtained from the simulation verify the efficacy of the suggested control strategy. [less ▲] Detailed reference viewed: 107 (12 UL)Human Error Identification in Programming by Demonstration of Compliant Motion Robotic Tasks Jasim, Ibrahim ; Plapper, Peter in 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013), Hyderabad 7-10 July 2013 (2013, July) In this paper, we suggest a scheme for error identification in human skills transfer when using the Programming by Demonstration (PbD) in adding a set of skills from a human operator to the force ... [more ▼] In this paper, we suggest a scheme for error identification in human skills transfer when using the Programming by Demonstration (PbD) in adding a set of skills from a human operator to the force- controlled robotic tasks. Such errors in human skills transfer is majorly caused from the difficulty of properly synchronizing the human and machine responses. Based on the captured Cartesian forces and torques signals of the manipulated object, we present an approach of identifying the errors stemmed from human wrong skills transfer in a PbD process. The scheme is composed of using the Gravitational Search- Fuzzy Clustering Algorithm (GS-FSA) in finding the centroid of the captured forces and torques signals for each Contact Formation (CF). Then using a distance- based outlier identification approach along with the centroid of each signal, the human errors can be identified in the framework of data outlier identification. In order to validate the approach, a test stand, composed of a KUKA Light Weight Robot manipulating a rigid cube object, is built. The manipulated object is assumed to interact with an environment composed of three orthogonal planes. Error identification for two case studies will be considered and other cases can be dealt with in a similar manner. From the experimental results, excellent human error identification is shown when using the suggested approach. [less ▲] Detailed reference viewed: 94 (6 UL)T-S Fuzzy Contact State Recognition for Compliant Motion Robotic Tasks Using Gravitational Search-Based Clustering Algorithm Jasim, Ibrahim ; Plapper, Peter in 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013) (2013, July) In this paper, we address the problem of contact state recognition for compliant motion robotic systems. The wrench (Cartesian forces and torques) and pose (position and orientation) of the manipulated ... [more ▼] In this paper, we address the problem of contact state recognition for compliant motion robotic systems. The wrench (Cartesian forces and torques) and pose (position and orientation) of the manipulated object in different Contact Formations (CFs) are firstly captured during a certain task execution. Then for each CF, we develop an efficient Takagi- Sugeno (T-S) fuzzy inference system that can model that specific CF using the available input (wrench and pose) - output (the desired model output for each CF) data. The antecedent part parameters are computed using the Gravitational Search- based Fuzzy Clustering Algorithm (GS- FCA) and the consequent parts parameters are tuned by the Least Mean Square (LMS). Excellent mapping and hence recognition capabilities can be expected from the suggested scheme. In order to validate the approach; experimental test stand is built which is composed of a KUKA Light Weight Robot (LWR) manipulating a cube rigid object that interacts with an environment composed of three orthogonal planes. The manipulated object is rigidly attached to the robot arm. The robot is programmed, by a human operator, to move in different CFs and for each CF, the wrench and pose readings are captured via the Fast Research Interface (FRI) available at the KUKA LWR. Using the suggested approach, excellent modeling is obtained for different CFs during the robot task execution. A comparison with the available CF recognition approaches is also performed and the superiority of the suggested scheme is shown. [less ▲] Detailed reference viewed: 220 (11 UL)Improved Observer-Based Robust Adaptive Control for a Class of Nonlinear Systems with Unknown Deadzone Jasim, Ibrahim in Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering (2013), 227(2), 184-197 This article presents an improved observer-based robust adaptive control strategy for a class of nonlinear systems that have two features: (1) the coupling of unmeasured states and unknown parameters ... [more ▼] This article presents an improved observer-based robust adaptive control strategy for a class of nonlinear systems that have two features: (1) the coupling of unmeasured states and unknown parameters exist at the measured states dynamics and (2) unknown deadzone exists at the system actuation. At first, the bounds of the deadzone parameters are assumed to be known and a suitable control strategy is derived. This strategy involves the derivation of a control action, observer of the unmeasured states, and estimators of the unknown parameters such that global stable system performance is guaranteed. Then, another control strategy is proposed when the bounds of the deadzone parameters are unavailable. The second control algorithm comprises the derivation of suitable control action, observer of the unmeasured states, and estimators of the unknown parameters such that global stable system performance is assured and the anonymity of the bounds of the deadzone parameters is accommodated. Simulations are performed when using both control strategies for a nonlinear system that falls in the category of systems addressed in this article. The system is a single-link mechanical joint that suffers from friction torque, modeled by LuGre friction model, with unknown deadzone exists at its actuation. The simulation results show the high performance of the suggested control approaches. [less ▲] Detailed reference viewed: 97 (15 UL)Robust adaptive control of spacecraft attitude systems with unknown dead zones of unknown bounds Jasim, Ibrahim ; in Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering (2012), 226(7), 947-955 This paper addresses the problem of attitude control of a spacecraft when unknown dead zones of unknown bounds exist at the actuators. A robust adaptive controller with parameter update laws are designed ... [more ▼] This paper addresses the problem of attitude control of a spacecraft when unknown dead zones of unknown bounds exist at the actuators. A robust adaptive controller with parameter update laws are designed for the spacecraft’s attitude and an asymptotically stable tracking performance is mathematically proven based on the proposed design. Numerical simulation results along with the theoretical proof show that the proposed control scheme can successfully stabilize the attitude of the spacecraft with the unknown actuator dead zones. [less ▲] Detailed reference viewed: 109 (3 UL)Robust Adaptive Pitch Control of Floating Wind Turbines Jasim, Ibrahim ; in Journal of Energy and Power Engineering (2012), 6 In this paper, a modified sliding-mode adaptive controller is derived to achieve stability and output regulation for a class of dynamical systems represented by a non-homogeneous differential equation ... [more ▼] In this paper, a modified sliding-mode adaptive controller is derived to achieve stability and output regulation for a class of dynamical systems represented by a non-homogeneous differential equation with unknown time-varying coefficients and unknown force function. In this scheme, the control law is constructed in terms of estimated values for the bounds of the unknown coefficients, where these values are continuously updated by adaptive laws to ensure asymptotic convergence to zero for the output function. The proposed controller is applied to solve the problem of pitch angle regulation for a floating wind turbine with dynamic uncertainty and external disturbances. Numerical simulations are performed to demonstrate the validity of the designed controller to achieve the desired pitch angle for the floating turbine’s body. [less ▲] Detailed reference viewed: 79 (5 UL) |
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