33 research outputs found
Learning and Reacting with Inaccurate Prediction: Applications to Autonomous Excavation
Motivated by autonomous excavation, this work investigates solutions to a class of problem where disturbance prediction is critical to overcoming poor performance of a feedback controller, but where the disturbance prediction is intrinsically inaccurate. Poor feedback controller performance is related to a fundamental control problem: there is only a limited amount of disturbance rejection that feedback compensation can provide. It is known, however, that predictive action can improve the disturbance rejection of a control system beyond the limitations of feedback. While prediction is desirable, the problem in excavation is that disturbance predictions are prone to error due to the variability and complexity of soil-tool interaction forces. This work proposes the use of iterative learning control to map the repetitive components of excavation forces into feedforward commands. Although feedforward action shows useful to improve excavation performance, the non-repetitive nature of soil-tool interaction forces is a source of inaccurate predictions. To explicitly address the use of imperfect predictive compensation, a disturbance observer is used to estimate the prediction error. To quantify inaccuracy in prediction, a feedforward model of excavation disturbances is interpreted as a communication channel that transmits corrupted disturbance previews, for which metrics based on the sensitivity function exist. During field trials the proposed method demonstrated the ability to iteratively achieve a desired dig geometry, independent of the initial feasibility of the excavation passes in relation to actuator saturation. Predictive commands adapted to different soil conditions and passes were repeated autonomously until a pre-specified finish quality of the trench was achieved. Evidence of improvement in disturbance rejection is presented as a comparison of sensitivity functions of systems with and without the use of predictive disturbance compensation
Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview
Disturbance Observer has been one of the most widely used robust control
tools since it was proposed in 1983. This paper introduces the origins of
Disturbance Observer and presents a survey of the major results on Disturbance
Observer-based robust control in the last thirty-five years. Furthermore, it
explains the analysis and synthesis techniques of Disturbance Observer-based
robust control for linear and nonlinear systems by using a unified framework.
In the last section, this paper presents concluding remarks on Disturbance
Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure
Gain-scheduled sliding-mode-type iterative learning control design for mechanical systems
In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM variable, the proposed controller is synthesized involving a feedback regulation item, a feedforward learning item, and a robust switching item. The feedback regulation item is adopted to regulate the position and velocity tracking errors, the feedforward learning item is applied to handle the model uncertainties and repetitive disturbance, and the robust switching item is introduced to compensate the nonrepetitive disturbance and linearization residual error. Moreover, the gain-scheduled mechanism is employed for both the feedback regulation item and feedforward learning item to enhance the convergence speed. Convergence analysis illustrates that the position and velocity tracking errors can eventually regulate to zero under the proposed controller. By combining the advantages of both SM control and IL control, the proposed controller has strong robustness against model uncertainties and disturbances. Lastly, simulations and comparisons are provided to evaluate the efficiency and excellent performance of the proposed control approach
Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances
State estimation has long been a fundamental problem in signal processing and
control areas. The main challenge is to design filters with ability to reject
or attenuate various disturbances. With the arrival of big data era, the
disturbances of complicated systems are physically multi-source, mathematically
heterogenous, affecting the system dynamics via isomeric (additive,
multiplicative and recessive) channels, and deeply coupled with each other. In
traditional filtering schemes, the multi-source heterogenous disturbances are
usually simplified as a lumped one so that the "single" disturbance can be
either rejected or attenuated. Since the pioneering work in 2012, a novel state
estimation methodology called {\it composite disturbance filtering} (CDF) has
been proposed, which deals with the multi-source, heterogenous, and isomeric
disturbances based on their specific characteristics. With the CDF, enhanced
anti-disturbance capability can be achieved via refined quantification,
effective separation, and simultaneous rejection and attenuation of the
disturbances. In this paper, an overview of the CDF scheme is provided, which
includes the basic principle, general design procedure, application scenarios
(e.g. alignment, localization and navigation), and future research directions.
In summary, it is expected that the CDF offers an effective tool for state
estimation, especially in the presence of multi-source heterogeneous
disturbances
Dynamic Modeling of Bucket-Soil Interactions Using Koopman-DFL Lifting Linearization for Model Predictive Contouring Control of Autonomous Excavators
A lifting-linearization method based on the Koopman operator and Dual Faceted
Linearization is applied to the control of a robotic excavator. In excavation,
a bucket interacts with the surrounding soil in a highly nonlinear and complex
manner. Here, we propose to represent the nonlinear bucket-soil dynamics with a
set of linear state equations in a higher-dimensional space. The space of
independent state variables is augmented by adding variables associated with
nonlinear elements involved in the bucket-soil dynamics. These include
nonlinear resistive forces and moment acting on the bucket from the soil, and
the effective inertia of the bucket that varies as the soil is captured into
the bucket. Variables associated with these nonlinear resistive and inertia
elements are treated as additional state variables, and their time evolution is
represented as another set of linear differential equations. The lifted linear
dynamic model is then applied to Model Predictive Contouring Control, where a
cost functional is minimized as a convex optimization problem thanks to the
linear dynamics in the lifted space. The lifted linear model is tuned based on
a data-driven method by using a soil dynamics simulator. Simulation experiments
verify the effectiveness of the proposed lifting linearization compared to its
counterpart
Proceedings of the NASA Conference on Space Telerobotics, volume 5
Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotics technology to the space systems planned for the 1990's and beyond. Volume 5 contains papers related to the following subject areas: robot arm modeling and control, special topics in telerobotics, telerobotic space operations, manipulator control, flight experiment concepts, manipulator coordination, issues in artificial intelligence systems, and research activities at the Johnson Space Center
Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14
Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences
OEXP exploration studies technical report. Volume 3: Special reports, studies, and indepth systems assessments
The Office of Exploration (OEXP) at NASA has been tasked with defining and recommending alternatives for an early 1990's national decision on a focused program of manned exploration of the Solar System. The Mission analysis and System Engineering (MASE) group, which is managed by the Exploration Studies Office at the Johnson Space Center, is responsible for coordinating the technical studies necessary for accomplishing such a task. This technical report, produced by the MASE, describes the process used to conduct exploration studies and discusses the mission developed in a case study approach. The four case studies developed in FY88 include: (1) a manned expedition to PHOBOS; (2) a manned expedition to MARS; (3) a lunar surface observatory; and a lunar outpost to early Mars evolution. The final outcome of this effort is a set of programmatic and technical conclusions and recommendations for the following year's work