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At least 19 records

Robot trajectory tracking with self-tuning predicted control

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

Cui, Xianzhong↗

A Trajectory-Tracking Algorithm for the LSMS Family of Cable-Driven Cranes

The Lightweight Surface Manipulation System, or LSMS, is a family of scalable long-reach cable-actuated manipulators. The design of the LSMS has a high payload ratio for efficient operations on planetary surfaces like the Moon or Mars. The LSMS has nonlinear, coupled, and hybrid dynamics. The engineering decisions that led to these challenging dynamics make this structure light and efficient. This paper proposes a novel trajectory tracking algorithm for these cranes that facilitates precise autonomous and teleoperated operations. This algorithm enables these robots to follow complex trajectories that avoid obstacles and pickup regolith in a construction site.

trajectory tracking↗

A Trajectory Tracking Algorithm for the LSMS Family of Cable-Driven Crane

The Lightweight Surface Manipulation System, or LSMS, is a family of scalable long-reach cable-actuated manipulators. The design of the LSMS has a high payload ratio for efficient operations on planetary surfaces like the Moon or Mars. The LSMS has nonlinear, coupled, and hybrid dynamics. The engineering decisions that led to these challenging dynamics make this structure light and efficient. This paper proposes a novel trajectory tracking algorithm for these cranes that facilitates precise autonomous and teleoperated operations. This algorithm enables these robots to follow complex trajectories that avoid obstacles and pickup regolith in a construction site.

lunar operations↗

Passivity/Lyapunov based controller design for trajectory tracking of flexible joint manipulators

A passivity and Lyapunov based approach for the control design for the trajectory tracking problem of flexible joint robots is presented. The basic structure of the proposed controller is the sum of a model-based feedforward and a model-independent feedback. Feedforward selection and solution is analyzed for a general model for flexible joints, and for more specific and practical model structures. Passivity theory is used to design a motor state-based controller in order to input-output stabilize the error system formed by the feedforward. Observability conditions for asymptotic stability are stated and verified. In order to accommodate for modeling uncertainties and to allow for the implementation of a simplified feedforward compensation, the stability of the system is analyzed in presence of approximations in the feedforward by using a Lyapunov based robustness analysis. It is shown that under certain conditions, e.g., the desired trajectory is varying slowly enough, stability is maintained for various approximations of a canonical feedforward.

Sicard, Pierre↗

A control scheme for trajectory tracking of robot manipulators

The authors put forward a simple scheme for multivariable control of robot manipulators. The scheme is composed of an inner-loop stabilizing controller and an outerloop tracking controller. The inner loop utilizes a multivariable PD (proportional differential) controller to stabilize the robot by placing the poles at some desired locations. The outer loop uses a multivariable PID (proportional integral differential) controller to achieve input-output decoupling and trajectory tracking. The gains of the PD and PID controllers are related directly to the linearized robot model by simple closed-form expressions. The controller gains are updated online to cope with variations in the robot model for gross motion and payload change. Alternatively, the use of high-gain controllers for gross motion and payload change are discussed.

Tarokh, M.↗

Simultaneous Trajectory Tracking Control and Online Mass Estimation for a Regolith Excavating Robot via Integral Concurrent Learning

An adaptive controller is developed for a regolith excavation robot to determine the mass of excavated material and to account for the effects of gravity and friction while on the surface of other celestial bodies. A data-based integral concurrent learning (ICL) parameter update law accounts for and estimates the unknown mass, gravity, and friction parameters. A Lyapunov-based analysis proves that the trajectory tracking error and the parameter estimate errors exponentially converge to zero. An estimation of the mass of regolith excavated by the robot is calculated from the estimated parameters. A simulation study is performed to show the performance of the developed technique. Simulation results show that for 3 kilograms of excavated material, the mass estimate has an error of 4.9 grams.

Max L. Greene↗

Inverse dynamics: Simultaneous trajectory tracking and vibration reduction with distributed actuators

This paper addresses the problem of inverse dynamics for articulated flexible structures with both lumped and distributed actuators. This problem arises, for example, in the combined vibration minimization and trajectory control of space robots and structures. A new inverse dynamics scheme for computing the nominal lumped and distributed inputs for tracking a prescribed trajectory is given.

Devasia, Santosh↗

Back to the Future: Consistency-Based Trajectory Tracking

Given a model of a physical process and a sequence of commands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algorithms for incrementally generating approximate belief states for a restricted but relevant class of partially observable Markov decision processes with very large state spaces. The algorithm presented incrementally generates, rather than revises, an approximate belief state at any point by abstracting and summarizing segments of the likely trajectories of the process. This enables applications to efficiently maintain a partial belief state when it remains consistent with observations and revisit past assumptions about the process' evolution when the belief state is ruled out. The system presented has been implemented and results on examples from the domain of spacecraft control are presented.

Kurien, James↗

Decentralized adaptive control of manipulators - Theory, simulation, and experimentation

The author presents a simple decentralized adaptive-control scheme for multijoint robot manipulators based on the independent joint control concept. The control objective is to achieve accurate tracking of desired joint trajectories. The proposed control scheme does not use the complex manipulator dynamic model, and each joint is controlled simply by a PID (proportional-integral-derivative) feedback controller and a position-velocity-acceleration feedforward controller, both with adjustable gains. Simulation results are given for a two-link direct-drive manipulator under adaptive independent joint control. The results illustrate trajectory tracking under coupled dynamics and varying payload. The proposed scheme is implemented on a MicroVAX II computer for motion control of the three major joints of a PUMA 560 arm. Experimental results are presented to demonstrate that trajectory tracking is achieved despite coupled nonlinear joint dynamics.

Seraji, Homayoun↗

Comparison of Two Alternative Methods for Tracking Toe Trajectory

Toe trajectory during the swing phase of locomotion has been identified as a precise motor control task (Karst, et al., 1999). The standard method for tracking toe trajectory is to place a marker on the superior aspect of the distal end of the 2nd toe itself (Karst, et al., 1999; Winter, 1992). However, others have based their toe trajectory results either on a marker positioned on the lateral aspect of the 5th metatarsal head (Dingwell, et al., 1999; Osaki, et al., 2007), or on a virtual toe marker computed at the anterior tip of the second toe based on the positions of other real foot markers (Miller, et al., 2006). While these methods for tracking the toe may seem similar, their results may not be directly comparable. The purpose of this study was to compute toe trajectory parameters using a 5th metatarsal marker and a virtual toe marker, and compare their results with those of the standard toe marker.

Miller, Chris↗

Simulating the Trajectory and Biomass Growth of Free-Floating Macroalgal Cultivation Platforms along the U.S. West Coast

Trajectory tracking and macroalgal growth models were coupled to support a novel macroalgae-harvesting concept known as the Nautical Off-shore Macroalgal Autonomous Device (NOMAD). The NOMAD consists of 5 km long carbon-fiber longlines that are seeded and free float southward along the U.S. West Coast for approximately 3 months before harvesting off the California coast, taking advantage of favorable environmental conditions. The trajectory and macroalgal growth models were applied to answer planning questions pertinent to the techno-economic analysis such as identifying the preferred release location, approximate pathway, timing until harvest, and estimated growth. Trajectories were determined with the General NOAA Operational Modeling Environment (GNOME) model, using 11 years of current and wind data, determining probabilities by running nearly 40,000 Monte Carlo simulations varying the start time and location. An accompanying macroalgal growth model was used to estimate the growth of macroalgae based on the trajectory tracks and environmental forcing products, including light, temperature and nutrients. Model results show that NOMAD lines transit south in the months of April to September due to seasonal currents, taking approximately 3 months to reach Southern California. During transit, NOMAD lines are dispersed but typically avoid beaching or passing through marine sanctuaries. NOMAD lines can yield up to 30 kg wet weight per meter of cultivation line.

09 BIOMASS FUELS↗

Adaptive control of space-based robot manipulators

A control method is presented that achieves globally stable trajectory tracking in the presence of uncertainties in the inertial parameters of the system. The 15-DOF system dynamics are divided into two components: a 9-DOF invertible portion and 6-DOF noninvertible portion. A controller is then designed to achieve trajectory tracking of the invertible portion of the system, which consists of the manipulator-joint positions and the orientation of the base. The motion of the noninvertible portion is bounded but otherwise unspecified. This portion of the system consists of the position of the robot's base and the position of the reaction wheels. A simulation is presented to demonstrate the effectiveness of the controller. A quadratic polynomial is used to generate the desired trajectory to illustrate the trajectory-tracking capability of the controller.

Walker, Michael W.↗

Effect of control sampling rates on model-based manipulator control schemes

The effect of changing the control sampling period on the performance of the computed-torque and independent joint control schemes is discussed. While the former utilizes the complete dynamics model of the manipulator, the latter assumes a decoupled and linear model of the manipulator dynamics. Researchers discuss the design of controller gains for both the computed-torque and the independent joint control schemes and establish a framework for comparing their trajectory tracking performance. Experiments show that within each scheme the trajectory tracking accuracy varies slightly with the change of the sampling rate. However, at low sampling rates the computed-torque scheme outperforms the independent joint control scheme. Based on experimental results, researchers also conclusively establish the importance of high sampling rates as they result in an increased stiffness of the system.

Khosla, P. K.↗

Tracking Virtual Trajectories

Current models of smooth pursuit eye movements assume that it is largely driven by retinal image motion. We tested this hypothesis by measuring pursuit of elliptical motion (3.2s, 0.9 Hz, 1.4 deg x 1.6 deg, 4 randomly interleaved phases) of either a small spot ("real" motion) or of a line-figure diamond viewed through apertures such that only the motion of four isolated oblique line segments was visible ("virtual" motion). Each segment moved sinusoidally along a linear trajectory yet subjects perceived a diamond moving along an elliptical path behind the aperture. We found, as expected, that real motion produced accurate tracking (N = 2) with mean gain (over horizontal and vertical) of 0.9, mean phase of -6 deg (lag), mean relative phase (H vs V) of 90 +/- 8 deg (RMS error). Virtual motion behind an X-shaped aperture (N= 4 with one naive) yielded a mean gain of 0.7, mean phase of -11 deg, mean relative phase of 87 +/- 15 deg. We also measured pursuit with the X-shaped aperture using a higher segment luminance which prevents the segments from being grouped into a coherently moving diamond while keeping the motion otherwise identical. In this incoherent case, the same four subjects no longer showed consistent elliptical tracking (RMS error in relative phase rose to 60 deg) suggesting that perceptual coherence is critical. Furthermore, to rule out tracking of the centroid, we also used vertical apertures so that all segment motion was vertical (N = 3). This stimulus still produced elliptical tracking (mean relative phase of 84 +/- 19 deg), albeit with a lower gain (0.6). These data show that humans can track moving objects reasonably accurately even when the trajectory can only be derived by spatial integration of motion signals. Models that merely seek to minimize retinal or local stimulus motion cannot explain these results.

Stone, Leland S.↗

Discrete-time adaptive control of robot manipulators

A discrete-time model reference adaptive control scheme is developed for trajectory tracking of robot manipulators. Hyperstability theory is utilized to derive the adaptation laws for the controller gain matrices. It is shown that asymptotic trajectory tracking is achieved despite gross robot parameter variation and uncertainties. The method offers considerable design flexibility and enables the designer to improve the performance of the control system by adjusting free design parameters. The discrete-time adaptation algorithm is extremely simple and is therefore suitable for real-time implementation.

Tarokh, M.↗