11 results on '"Herrmann, Guido"'
Search Results
2. Distributed Motion Synchronisation Control of Humanoid Arms
- Author
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Mahyuddin, Muhammad Nasiruddin, Herrmann, Guido, Omar, Khairuddin, editor, Nordin, Md Jan, editor, Vadakkepat, Prahlad, editor, Prabuwono, Anton Satria, editor, Abdullah, Siti Norul Huda Sheikh, editor, Baltes, Jacky, editor, Amin, Shamsudin Mohd, editor, Hassan, Wan Zuha Wan, editor, and Nasrudin, Mohammad Faidzul, editor
- Published
- 2013
- Full Text
- View/download PDF
3. Real-Time Force Reconstruction in a Transverse Dynamic Force Microscope.
- Author
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Zhang, Kaiqiang, Nguyen, Thang, Edwards, Christopher, Antognozzi, Massimo, Miles, Mervyn, and Herrmann, Guido
- Subjects
FIELD programmable gate arrays ,MEASURING instruments ,SCANNING probe microscopy ,PROGRAMMABLE logic devices - Abstract
One major functionality of force microscopes is their ability to measure forces at a high sensitivity, thereby, allowing understanding of vital mechanisms: for instance, in bio-specimens. The investigation of a specimen’s viscoelasticity on nano-scale can have significant scientific impact, but has been inhibited by the lack of fast, comprehensive scanning instruments. In principle, transverse dynamic force microscopes (TDFMs) permit the measurement of interaction forces within delicate samples in a noncontact manner. The force measurements are reconstructed via complicated offline analysis in TDFMs, therefore, they can hardly be utilized as an online force measuring tool. This article introduces a novel integrated robust design for practical scanning using the TDFM system. The digital design is implemented in fixed-point arithmetic using field programmable gate array devices, thereby, permitting measurement of the interaction force at a high sampling rate. The novel digital design tackles different implementation issues achieving fast and robust force measuring performance. This enables a new force-scan mode for the TDFM, realizing for the first time, online force mapping of sample-surfaces in real-time. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Improving transient performance of adaptive control via a modified reference model and novel adaptation
- Author
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Na, Jing, Herrmann, Guido, and Zhang, Kaiqiang
- Subjects
transient performance ,robustness ,nonlinear system ,adaptive control ,parameter estimation - Abstract
This paper presents a new model reference adaptive control (MRAC) framework for a class of nonlinear systems to address the improvement of transient performance. The main idea is to introduce a nonlinear compensator to reshape the closed-loop system transient, and to suggest a new adaptive law with guaranteed convergence. The compensator captures the unknown system dynamics and modifies the given nominal reference model and the control action. This modified controlled system can approach the response of the ideal reference model. The transient is easily tuned by a new design parameter of this compensator. The nominal adaptive law is augmented by new leakage terms containing the parameter estimation errors. This allows for fast, smooth and exponential convergence of both the tracking error and parameter estimation, which again improves overall reference model following. We also show that the required excitation condition for the estimation convergence is equivalent to the classical persistent excitation (PE) condition. In this respect, this paper provides an intuitive and numerically feasible approach to online validate the PE condition. The salient feature of the suggested methodology is that the rapid suppression of uncertainties in the controlled system can be achieved without using a large, high-gain induced, learning rate in the adaptive laws. Extensive simulations are given to show the effectiveness and the improved response of the proposed schemes.
- Published
- 2017
5. Supervised Parameter Estimation for Road Vehicles, Mitigating Powertrain Induced Uncertainty.
- Author
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Wragge-Morley, Robert, Herrmann, Guido, and Barber, Phil
- Subjects
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PARAMETER estimation , *SLIDING mode control , *ALGORITHMS , *FORD F-Series trucks , *FORECASTING , *UNCERTAINTY , *DATA fusion (Statistics) - Abstract
This paper demonstrates a real world case study of a new method for robust simultaneous estimation of two parameters using multiple data sources. The method is used to simultaneously estimate vehicle mass and road gradient. No additional sensors are required beyond those that would normally be found on a vehicle controller network. The estimation algorithm combines components driven by observer state error and also directly by the parameter error using a sliding-mode inspired regressor structure. The algorithm incorporates a novel information fusion method that is integral to the regressor structure and a supervised data-rejection system to limit estimation activity in periods of recognised error-promoting activity. The estimation method has been demonstrated in real time on a modified production passenger car platform. It has been shown to be effective at robustly predicting road gradient and offering more reliable and stable prediction of vehicle mass than existing estimation methods employed in the same multi-parameter estimation context. The estimator allows prediction of vehicle mass whose limiting factor is the bandwidth and accuracy of the available driveline torque information, not the algorithm itself, allowing the identification of a 150 kg change in mass on a 2000 kg vehicle in this case study. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
6. Robust adaptive finite-time parameter estimation and control for robotic systems
- Author
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Na, Jing, Mahyuddin, Muhammad Nasiruddin, Herrmann, Guido, Ren, Xuemei, and Barber, Phil
- Subjects
robotic systems ,terminal sliding mode control ,finite-time convergence ,adaptive control ,parameter estimation - Abstract
This paper studies adaptive parameter estimation and control for nonlinear robotic systems based on parameter estimation errors. A framework to obtain an expression of the parameter estimation error is proposed first by introducing a set of auxiliary filtered variables. Then three novel adaptive laws driven by the estimation error are presented, where exponential error convergence is proved under the conventional persistent excitation (PE) condition; the direct measurement of the time derivatives of the system states are avoided. The adaptive laws are modified via a sliding mode technique to achieve finite-time convergence, and an online verification of the alternative PE condition is introduced. Leakage terms, functions of the estimation error, are incorporated into the adaptation laws to avoid windup of the adaptation algorithms. The adaptive algorithm applied to robotic systems permits that tracking control and exact parameter estimation are achieved simultaneously in finite time using a terminal sliding mode (TSM) control law. In this case, the PE condition can be replaced with a sufficient richness requirement of the command signals and thus is verifiable a priori. The potential singularity problem encountered in TSM controls is remedied by introducing a two-phase control procedure. The robustness of the proposed methods against disturbances is investigated. Simulations based on the ‘Bristol-Elumotion-Robotic-Torso II’ (BERT II) are provided to validate the efficacy of the introduced methods.
- Published
- 2015
7. Active Adaptive Estimation and Control for Vehicle Suspensions With Prescribed Performance.
- Author
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Na, Jing, Huang, Yingbo, Wu, Xing, Gao, Guanbin, Herrmann, Guido, and Jiang, Jason Zheng
- Subjects
ADAPTIVE control systems ,COMPUTER simulation - Abstract
This paper proposes an adaptive control for vehicle active suspensions with unknown nonlinearities (e.g., nonlinear springs and piecewise dampers). A prescribed performance function that characterizes the convergence rate, maximum overshoot, and steady-state error is incorporated into the control design to stabilize the vertical and pitch motions, such that both the transient and steady-state suspension response are guaranteed. Moreover, a novel adaptive law is used to achieve precise estimation of essential parameters (e.g., mass of vehicle body and moment of inertia for pitch motion), where the parameter estimation error is obtained explicitly and then used as a new leakage term. Theoretical studies prove the convergence of the estimated parameters, and compare the suggested controller with generic adaptive controllers using the gradient descent and e-modification schemes. In addition to motion displacements, dynamic tire loads and suspension travel constraints are also considered. Extensive comparative simulations on a dynamic simulator consisting of commercial vehicle simulation software Carsim 8.1 and MATLAB Simulink are provided to show the efficacy of the proposed control, and to illustrate the improved performance. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
8. Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation.
- Author
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Na, Jing, Chen, Anthony Siming, Herrmann, Guido, Burke, Richard, and Brace, Chris
- Subjects
AUTOMOBILE engines ,INTERNAL combustion engines ,TORQUE ,DYNAMOMETER ,COMPUTER simulation - Abstract
This paper presents two torque estimation methods for vehicle engines: unknown input observer (UIO) and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the engine torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic engine simulator built in a commercial software GT-Power, and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical tests show very encouraging results with small estimation errors even in the presence of sensor noise. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
9. Robotic hand posture and compliant grasping control using operational space and integral sliding mode control.
- Author
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Herrmann, Guido, Jalani, Jamaludin, Mahyuddin, Muhammad Nasiruddin, Khan, Said G., and Melhuish, Chris
- Subjects
- *
ROBOT control systems , *ROBOT hands , *SLIDING mode control , *GEOMETRIC analysis , *PARAMETER estimation - Abstract
This paper establishes a novel approach of robotic hand posture and grasping control. For this purpose, the control uses the operational space approach. This permits the consideration of the shape of the object to be grasped. Thus, the control is split into a task control and a particular optimizing posture control. The task controller employs Cylindrical and Spherical coordinate systems due to their simplicity and geometric suitability. This is achieved by using an integral sliding mode controller (ISMC) as task controller. The ISMC allows us to introduce a model reference approach where a virtual mass-spring-damper system can be used to design a compliant trajectory tracking controller. The optimizing posture controller together with the task controller creates a simple approach to obtain pre-grasping/object approach hand postures. The experimental results show that target trajectories can be easily followed by the task control despite the presence of friction and stiction. When the object is grasped, the compliant control will automatically adjust to a specific compliance level due to an augmented compliance parameter adjustment algorithm. Once a specific compliance model has been achieved, the fixed compliance controller can be tested for a specific object grasp scenario. The experimental results prove that the Bristol Elumotion robot hand (BERUL) can automatically and successfully attain different compliance levels for a particular object via the ISMC. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
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10. An adaptive observer-based parameter estimation algorithm with application to road gradient and vehicle's mass estimation.
- Author
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Mahyuddin, Muhammad Nasiruddin, Na, Jing, Herrmann, Guido, Ren, Xuemei, and Barber, Phil
- Abstract
A novel observer-based parameter estimation algorithm with sliding mode term has been developed to estimate the road gradient and vehicle weight using only the vehicle's velocity and the driving torque from the engine. The estimation algorithm exploits all known terms in the system dynamics and a low pass filtered representation to derive an explicit expression of the parameter estimation error without measuring the acceleration. The proposed algorithm which features a sliding-mode term to ensure the fast and robust convergence of the estimation in the presence of persistent excitation is augmented to an adaptive observer and analyzed using Lyapunov Theory. The analytical results show that the algorithm is stable and ensures finite-time error convergence to a bounded error even in the presence of disturbances. A simple practical method for validating persistent excitation is provided using the new theoretical approach to estimation. This is validated by the practical implementation of the algorithm on a small-scaled vehicle, emulating a car system. The slope gradient as well as the vehicle's mass/weight are estimated online. The algorithm shows a significant improvement over a previous result. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
11. Adaptive Observer-Based Parameter Estimation With Application to Road Gradient and Vehicle Mass Estimation.
- Author
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Mahyuddin, Muhammad Nasiruddin, Na, Jing, Herrmann, Guido, Ren, Xuemei, and Barber, Phil
- Subjects
ADAPTIVE control systems ,PARAMETER estimation ,ELECTRIC driving ,ALGORITHMS ,STOCHASTIC convergence ,ROBUST control - Abstract
A novel observer-based parameter estimation scheme with sliding mode term has been developed to estimate the road gradient and the vehicle weight using only the vehicle's velocity and the driving torque. The estimation algorithm exploits all known terms in the system dynamics and a low-pass filtered representation of the dynamics to derive an explicit expression of the parameter estimation error without measuring the acceleration. The proposed parameter estimation scheme which features a sliding-mode term to ensure the fast and robust convergence of the estimation in the presence of persistent excitation is augmented to an adaptive observer and analyzed using Lyapunov Theory. The analytical results show that the algorithm is stable and ensures finite-time error convergence to a bounded error even in the presence of disturbances. In the absence of disturbances, convergence to the true values in finite time is guaranteed. A simple practical method for validating persistent excitation is provided using the new theoretical approach to estimation. This is validated by the practical implementation of the algorithm on a small-scaled vehicle, emulating a car system. The slope gradient as well as the vehicle's mass/weight are estimated online. The algorithm shows a significant improvement over previous results. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
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