Author name cluster

Jean-Jacques E. Slotine

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40 papers

2 author rows

ICML Conference 2023 Conference Paper

Learning Control-Oriented Dynamical Structure from Data

Spencer M. Richards
Jean-Jacques E. Slotine
Navid Azizan
Marco Pavone 0001

Even for known nonlinear dynamical systems, feedback controller synthesis is a difficult problem that often requires leveraging the particular structure of the dynamics to induce a stable closed-loop system. For general nonlinear models, including those fit to data, there may not be enough known structure to reliably synthesize a stabilizing feedback controller. In this paper, we discuss a state-dependent nonlinear tracking controller formulation based on a state-dependent Riccati equation for general nonlinear control-affine systems. This formulation depends on a nonlinear factorization of the system of vector fields defining the control-affine dynamics, which always exists under mild smoothness assumptions. We propose a method for learning this factorization from a finite set of data. On a variety of simulated nonlinear dynamical systems, we empirically demonstrate the efficacy of learned versions of this controller in stable trajectory tracking. Alongside our learning method, we evaluate recent ideas in jointly learning a controller and stabilizability certificate for known dynamical systems; we show experimentally that such methods can be frail in comparison.

ICML Conference 2023 Conference Paper

Scaling Spherical CNNs

Carlos Esteves
Jean-Jacques E. Slotine
Ameesh Makadia

Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), which is still costlier than the usual planar convolutions. For this reason, applications of spherical CNNs have so far been limited to small problems that can be approached with low model capacity. In this work, we show how spherical CNNs can be scaled for much larger problems. To achieve this, we make critical improvements including novel variants of common model components, an implementation of core operations to exploit hardware accelerator characteristics, and application-specific input representations that exploit the properties of our model. Experiments show our larger spherical CNNs reach state-of-the-art on several targets of the QM9 molecular benchmark, which was previously dominated by equivariant graph neural networks, and achieve competitive performance on multiple weather forecasting tasks. Our code is available at https: //github. com/google-research/spherical-cnn.

JMLR Journal 2022 Journal Article

Nonparametric adaptive control and prediction: theory and randomized algorithms

Nicholas M. Boffi
Stephen Tu
Jean-Jacques E. Slotine

A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions. While this assumption leads to efficient algorithms, it limits applications to very specific classes of systems. We introduce a novel nonparametric adaptive algorithm that estimates an infinite-dimensional density over parameters online to learn an unknown dynamics in a reproducing kernel Hilbert space. Surprisingly, the resulting control input admits an analytical expression that enables its implementation despite its underlying infinite-dimensional structure. While this adaptive input is rich and expressive -- subsuming, for example, traditional linear parameterizations -- its computational complexity grows linearly with time, making it comparatively more expensive than its parametric counterparts. Leveraging the theory of random Fourier features, we provide an efficient randomized implementation that recovers the complexity of classical parametric methods while provably retaining the expressivity of the nonparametric input. In particular, our explicit bounds only depend polynomially on the underlying parameters of the system, allowing our proposed algorithms to efficiently scale to high-dimensional systems. As an illustration of the method, we demonstrate the ability of the randomized approximation algorithm to learn a predictive model of a 60-dimensional system consisting of ten point masses interacting through Newtonian gravitation. By reinterpretation as a gradient flow on a specific loss, we conclude with a natural extension of our kernel-based adaptive algorithms to deep neural networks. We show empirically that the extra expressivity afforded by deep representations can lead to improved performance at the expense of the closed-loop stability that is rigorously guaranteed and consistently observed for kernel machines. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

ICRA Conference 2022 Conference Paper

Optimizing Trajectories with Closed-Loop Dynamic SQP

Sumeet Singh
Jean-Jacques E. Slotine
Vikas Sindhwani

Indirect trajectory optimization methods such as Differential Dynamic Programming (DDP) have found considerable success when only planning under dynamic feasibility constraints. Meanwhile, nonlinear programming (NLP) has been the state-of-the-art approach when faced with additional constraints (e. g. , control bounds, obstacle avoidance). However, a naïve implementation of NLP algorithms, e. g. , shooting-based sequential quadratic programming (SQP), may suffer from slow convergence – caused from natural instabilities of the underlying system manifesting as poor numerical stability within the optimization. Re-interpreting the DDP closed-loop rollout policy as a sensitivity-based correction to a second-order search direction, we demonstrate how to compute analogous closedloop policies (i. e. , feedback gains) for constrained problems. Our key theoretical result introduces a novel dynamic programmingbased constraint-set recursion that augments the canonical “cost-to-go” backward pass. On the algorithmic front, we develop a hybrid-SQP algorithm incorporating DDP-style closedloop rollouts, enabled via efficient parallelized computation of the feedback gains. Finally, we validate our theoretical and algorithmic contributions on a set of increasingly challenging benchmarks, demonstrating significant improvements in convergence speed over standard open-loop SQP.

ICRA Conference 2021 Conference Paper

Sliding on Manifolds: Geometric Attitude Control with Quaternions

Brett T. Lopez
Jean-Jacques E. Slotine

This work proposes a quaternion-based sliding variable that describes exponentially convergent error dynamics for any forward complete desired attitude trajectory. The proposed sliding variable directly operates on the non-Euclidean space formed by quaternions and explicitly handles the double covering property to enable global attitude tracking when used in feedback. In-depth analysis of the sliding variable is provided and compared to others in the literature. Several feedback controllers including nonlinear PD, robust, and adaptive sliding control are then derived. Simulation results of a rigid body with uncertain dynamics demonstrate the effectiveness and superiority of the approach.

ICRA Conference 2018 Conference Paper

Cooperative Adaptive Control for Cloud-Based Robotics

Patrick M. Wensing
Jean-Jacques E. Slotine

This paper studies collaboration through the cloud in the context of cooperative adaptive control for robot manipulators. We first consider the case of multiple robots manipulating a common object through synchronous centralized update laws to identify unknown inertial parameters. Through this development, we introduce a notion of Collective Sufficient Richness, wherein parameter convergence can be enabled through teamwork in the group. The introduction of this property and the analysis of stable adaptive controllers that benefit from it constitute the main new contributions of this work. Building on this original example, we then consider decentralized update laws, time-varying network topologies, and the influence of communication delays on this process. Perhaps surprisingly, these nonidealized networked conditions inherit the same benefits of convergence being determined through collective effects for the group. Simple simulations of a planar manipulator identifying an unknown load are provided to illustrate the central idea and benefits of Collective Sufficient Richness.

ICRA Conference 2018 Conference Paper

Robust Collision Avoidance via Sliding Control

Brett T. Lopez
Jean-Jacques E. Slotine
Jonathan P. How

Recent advances in perception and planning algorithms have enabled robots to navigate autonomously through unknown, cluttered environments at high-speeds. A key component of these systems is the ability to identify, select, and execute a safe trajectory around obstacles. Many of these systems, however, lack performance guarantees because model uncertainty and external disturbances are ignored when a trajectory is selected for execution. This work leverages results from nonlinear control theory to establish a bound on tracking performance that can be used to select a provably safe trajectory. The Composite Adaptive Sliding Controller (CASC) provides robustness to disturbances and reduces model uncertainty through high-rate parameter estimation. CASC is demonstrated in simulation and hardware to significantly improve the performance of a quadrotor navigating through unknown environments with external disturbances and unknown model parameters.

ICRA Conference 2018 Conference Paper

The UNAV, a Wind-Powered UAV for Ocean Monitoring: Performance, Control and Validation

Gabriel D. Bousquet
Michael S. Triantafyllou
Jean-Jacques E. Slotine

Wind power is the source of propulsive energy for sailboats and albatrosses. We present the UNAv, an Unmanned Nautical Air-water vehicle, that borrows features from both. It is composed of a glider-type airframe fitted with a vertical wing-sail extending above the center of mass of the system and a vertical surface-piercing hydrofoil keel extending below. The sail and keel are both actuated in pitch about their span-wise axes. Like an albatross, the UNAv is fully streamlined, high lift-to-drag ratio and generates the gravity-cancelling force by means of its airborne wings. Like a sailboat, the UNAv interacts with water and may access the full magnitude of the wind. A trim analysis predicts that a 3. 4-meter span, 3 kg system could stay airborne in winds as low as 2. 8 m/s (5. 5 knots), and travel several times faster than the wind speed. Trim flight requires the ability to fly at extreme low height with the keel immersed in water. For that purpose, a multi-input longitudinal flight controller that leverages fast flap actuation is presented. The flight maneuver is demonstrated experimentally.

IROS Conference 2017 Conference Paper

Control of a flexible, surface-piercing hydrofoil for high-speed, small-scale applications

Gabriel D. Bousquet
Michael S. Triantafyllou
Jean-Jacques E. Slotine

In recent years, hydrofoils have become ubiquitous and critical components of high-performance surface vehicles. Twenty-meter-long hydrofoil sailing craft are capable of reaching speeds in excess of 45 knots. Hydrofoil dinghies routinely travel faster than the wind and reach speeds up to 30 knots. Besides, in the quest for super-maneuverability, actuated hydrofoils could enable the efficient generation of large forces on demand. However, the control of hydrofoil systems remains challenging, especially in rough seas. With the intent to ultimately enable the design of versatile, small-scale, high-speed, and super-maneuverable surface vehicles, we investigate the problem of controlling the lift force generated by a flexible, surface-piercing hydrofoil traveling at high speed through a random wave field. We present a test platform composed of a rudder-like vertical hydrofoil actuated in pitch. The system is instrumented with velocity, force, and immersion depth sensors. We carry out high-speed field experiments in the presence of naturally occurring waves. The 2 cm chord hydrofoil is successfully controlled with a LTV/feedback linearization controller at speeds ranging from 4 to 10+ m/s.

ICRA Conference 2017 Conference Paper

Robust online motion planning via contraction theory and convex optimization

Sumeet Singh
Anirudha Majumdar
Jean-Jacques E. Slotine
Marco Pavone 0001

We present a framework for online generation of robust motion plans for robotic systems with nonlinear dynamics subject to bounded disturbances, control constraints, and online state constraints such as obstacles. In an offline phase, one computes the structure of a feedback controller that can be efficiently implemented online to track any feasible nominal trajectory. The offline phase leverages contraction theory and convex optimization to characterize a fixed-size “tube” that the state is guaranteed to remain within while tracking a nominal trajectory (representing the center of the tube). In the online phase, when the robot is faced with obstacles, a motion planner uses such a tube as a robustness margin for collision checking, yielding nominal trajectories that can be safely executed, i. e. , tracked without collisions under disturbances. In contrast to recent work on robust online planning using funnel libraries, our approach is not restricted to a fixed library of maneuvers computed offline and is thus particularly well-suited to applications such as UAV flight in densely cluttered environments where complex maneuvers may be required to reach a goal. We demonstrate our approach through simulations of a 6-state planar quadrotor navigating cluttered environments in the presence of a cross-wind. We also discuss applications of our approach to Tube Model Predictive Control (TMPC) and compare the merits of our method with state-of-the-art nonlinear TMPC techniques.

ICRA Conference 2015 Conference Paper

Robotic manipulation of micro/nanoparticles using optical tweezers with velocity constraints and stochastic perturbations

Xiao Yan 0003
Chien Chern Cheah
Quang-Cuong Pham
Jean-Jacques E. Slotine

Various control approaches have been developed for micro/nanomanipulations using optical tweezers. Most existing methods assume that the micro/nanoparticles stay trapped during manipulations, and stochastic perturbations (Brownian motion) are usually ignored for the simplification of model dynamics. However, the trapped particles could escape from the optical traps especially in motion due to several possible reasons: small trapping stiffness, stochastic perturbations, and kinetic energy gained during manipulation. This paper investigates the conditions under which micro/nanoparticles will stay trapped while in motion. The dynamics of the trapped particles subject to stochastic perturbations is analyzed. Dynamic trapping is considered and the maximum manipulation velocity is determined from a probabilistic perspective. A controller with certain velocity bound is proposed, the stability of the system is analysed in presence of stochastic perturbation. Experimental results are presented to show the effectiveness of the proposed control approach.

ICRA Conference 2009 Conference Paper

Dynamic region following formation control for a swarm of robots

Saing Paul Hou
Chien Chern Cheah
Jean-Jacques E. Slotine

This paper presents a dynamic region following formation control method for a swarm of robots. In this control strategy, a swarm of robots shall move together as a group inside a dynamic region that can rotate or scale to enable the robots to adjust the formation. Various desired shapes can be formed by choosing appropriate functions. Unlike existing formation control methods, the proposed method do not need to have specific identities or orders in the group but yet dynamic formation can be formed for a large group of robots. This enables a swarm of robots to adjust the formation during the course of maneuver. The system is also scalable in the sense that any robot can move into the formation or leave the formation without affecting the other robots. Lyapunov-like function is presented for convergence analysis of the multi-robot systems. Simulation results are presented to illustrate the performance of the proposed controller.

ICRA Conference 2009 Conference Paper

Task-space setpoint control of robots with dual task-space information

Chien Chern Cheah
Jean-Jacques E. Slotine

In conventional task-space control problem of robots, a single task-space information is used for the entire task. When the task-space control problem is formulated in image space, this implies that visual feedback is used throughout the movement. While visual feedback is important to improve the endpoint accuracy in presence of uncertainty, the initial movement is primarily ballistic and hence visual feedback is not necessary. The relatively large delay in visual information would also make the visual feedback ineffective for fast initial movements. Due to limited field of view of the camera, it is also difficult to easure that visual feedback can be used for the entire task. Therefore, the task may fail if any of the features is out of view. In this paper, we present a new task-space control strategy that allows the use of dual task-space information in a single controller. We shall show that the proposed task-space controller can transit smoothly from Cartesian-space feedback at the initial stage to vision-space feedback at the end stage when the target is near.

ICRA Conference 2008 Conference Paper

Consensus learning for distributed coverage control

Mac Schwager
Jean-Jacques E. Slotine
Daniela Rus

A decentralized controller is presented that causes a network of robots to converge to a near optimal sensing configuration, while simultaneously learning the distribution of sensory information in the environment. A consensus (or flocking) term is introduced in the learning law to allow sharing of parameters among neighbors, greatly increasing learning convergence rates. Convergence and consensus is proven using a Lyapunov-type proof. The controller with parameter consensus is shown to perform better than the basic controller in numerical simulations.

ICRA Conference 2008 Conference Paper

Region following formation control for multi-robot systems

Chien Chern Cheah
Saing Paul Hou
Jean-Jacques E. Slotine

In this paper, a region following formation control method for multi-robot systems is proposed. In this control method, the robots move as a group inside a desired region while maintaining a minimum distance among themselves. Various shapes of desired region can be formed by choosing the appropriate objective functions. The robots do not need to have specific identities since the proposed controller does not need specific orders of robots within the group. Therefore, the system is scalable since any robot can come in or go out of the group without affecting the system. Lyapunov-like function is presented for convergence analysis of the multi-robot systems. Simulation results are presented to illustrate the performance of the proposed controller.

ICRA Conference 2007 Conference Paper

Adaptive Vision and Force Tracking Control of Constrained Robots with Structural Uncertainties

Yu Zhao 0001
Chien Chern Cheah
Jean-Jacques E. Slotine

In many applications of robot manipulators, the end-effector is required to make contact with environment. In these applications, it is necessary to control not only the position but also the interaction force between the robot end-effector and environment. Most research so far on motion and force tracking control has assumed that the kinematics and constraint surface are exactly known. In this paper, we propose a visually-servoed adaptive Jacobian controller for motion and force tracking control with structural uncertainties in kinematics, dynamics and constraint surface. It is shown that uniform ultimate boundedness of the tracking errors can be guaranteed. Simulation results are presented to illustrate the performance of the proposed control law.

ICRA Conference 2007 Conference Paper

Adaptive Vision based Tracking Control of Robots with Uncertainty in Depth Information

Chien Chern Cheah
Chao Liu 0003
Jean-Jacques E. Slotine

In this paper, a vision based tracking controller with adaptation to uncertainty in depth information is presented. Depth uncertainty plays a special role in visual tracking as it appears nonlinearly in the overall Jacobian matrix and hence cannot be adapted together with other uncertain kinematic parameters. We propose a novel parameter update law to update the uncertain parameters of the depth. It is proved that system stability can be guaranteed for the visual tracking task in presence of uncertainties in depth information, robot kinematics and dynamics. Simulation results are presented to illustrate the performance of the proposed controller.

ICRA Conference 2007 Conference Paper

Decentralized, Adaptive Control for Coverage with Networked Robots

Mac Schwager
Jean-Jacques E. Slotine
Daniela Rus

A decentralized, adaptive control law is presented to drive a network of mobile robots to a near-optimal sensing configuration. The control law is adaptive in that it integrates sensor measurements to provide a converging estimate of the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. A Lyapunov-type proof is used to show that the control law causes the network to converge to a near-optimal sensing configuration, and the controller is demonstrated in numerical simulations. This technique suggests a broader application of adaptive control methodologies to decentralized control problems in unknown dynamical environments.

ICRA Conference 2007 Conference Paper

Models for Global Synchronization in CPG-based Locomotion

Keehong Seo
Jean-Jacques E. Slotine

Various forms of animal locomotion have been studied in the biological literature. Neuroscience research suggests the existence of central pattern generators (CPGs), neural networks that generate periodic signals for locomotion. We study simplified modular architectures based on CPGs for robotic applications, and show their global exponential stability using partial contraction analysis. The proposed architectures can reproduce periodic CPG signals for swimming or walking motion of various animals. They can be combined towards increasingly complex behaviors while preserving stability

ICRA Conference 2006 Conference Paper

Adaptive Jacobian Motion and Force Tracking Control for Constrained Robots with Uncertainties

Chien Chern Cheah
Yu Zhao 0001
Jean-Jacques E. Slotine

Most research so far on motion and force tracking control of robots has assumed that the kinematics and dynamics are exactly known. In this paper, we propose an adaptive Jacobian controller for motion and force tracking with uncertainties in kinematics and dynamics. It is shown that the robot end-effector can track the desired position and force trajectories with the uncertain parameters updated online. Simulation results are presented to illustrate the performance of the proposed control law

IROS Conference 2006 Conference Paper

Adaptive Jacobian PID Regulation for Robots with Uncertain Kinematics and Actuator Model

Chao Liu 0003
Chien Chern Cheah
Jean-Jacques E. Slotine

This paper presents a task-space Saturated-Proportional, Integral and Differential (SP-ID) regulation approach for robot manipulators with uncertain kinematics and actuator model. The proposed approach is computationally efficient and easy to implement due to its simple structure. It's interesting to observe that in this paper the simple PID type controller is shown not only capable of compensating unknown gravity force, as has been known for long in robot control literature, but also capable of dealing with uncertainties in robot kinematics and actuator model. Sufficient conditions to guarantee system stability are provided and simulation results are presented to show the performance of proposed control method.

ICRA Conference 2006 Conference Paper

Adaptive Task-space Regulation of Rigid-link Flexible-joint Robots with Uncertain Kinematics

Chao Liu 0003
Chien Chern Cheah
Jean-Jacques E. Slotine

Joint flexibility is an important factor to consider in the robot control design if high performance is expected for the robot manipulators. The research work on control of rigid-link flexible-joint (RLFJ) robot in the literature has assumed that the kinematics of the robot is known exactly. There have been no results so far that can deal with the kinematics uncertainty in RLFJ robot. In this paper, we present the first study on this problem and propose an adaptive regulation method which can deal with the kinematics uncertainty and uncertainties in both link and motor dynamics of the RLFJ robot system. An observer is designed to avoid the use of acceleration due to the fourth-order overall dynamics. Sufficient conditions are derived to guarantee the asymptotic stability of the closed-loop system. Simulation result illustrates the effectiveness of proposed control method

IROS Conference 2006 Conference Paper

Adaptive Vision and Force Tracking Control for Constrained Robots

Yu Zhao 0001
Chien Chern Cheah
Jean-Jacques E. Slotine

Most research so far on motion and force tracking has assumed that the kinematics and dynamics are exactly known. In this paper, we propose an visually-servoed adaptive Jacobian controller for motion and force tracking with uncertainties in kinematics, dynamics and camera model. It is shown that the robot end-effector can track the desired position and force trajectories with the uncertain parameters updated online. Simulation results are presented to illustrate the performance of the proposed control law

ICRA Conference 2005 Conference Paper

Adaptive Jacobian Tracking Control of Robots based on Visual Task-space Information

Chien Chern Cheah
Chao Liu 0003
Jean-Jacques E. Slotine

Most research so far on trajectory tracking control of robot has assumed that the kinematics of the robot is known exactly. This paper extends our recent work on adaptive Jacobian tracking control by deriving a new algorithm for trajectory tracking of robots with uncertain kinematics and dynamics. The algorithm requires only to measure the end-effector position in visual space, besides the robot’s joint angles and joint velocities. Experimental results are presented to illustrate the performance of the proposed controllers. In the experiments, we demonstrate that the robot’s shadow can be used to control the robot.

ICRA Conference 2004 Conference Paper

Approximate Jacobian Adaptive Control for Robot Manipulators

Chien Chern Cheah
Chao Liu 0003
Jean-Jacques E. Slotine

Research so far on trajectory tracking control of robot has assumed that the kinematics of the robot is known exactly. In this paper, a new approximate Jacobian adaptive controller is proposed for trajectory tracking of robot with uncertain kinematics and dynamics. It is shown that the robot end effector is able to converge to a desired trajectory with the uncertain kinematics and dynamics parameters being updated online by parameter update laws. Experimental results are presented to illustrate the performance of the proposed controllers.

ICRA Conference 1998 Conference Paper

Towards Force-Reflecting Teleoperation Over the Internet

Günter Niemeyer
Jean-Jacques E. Slotine

This paper extends earlier results on stable force reflecting teleoperation in the presence of significant time-delays to the case, frequent in practice, where the transmission delays are themselves varying with time in an unpredictable fashion. It shows that stability can be preserved through the systematic use of specially designed wave-variable filters. The resulting performance of the teleoperation system is illustrated in simulations, and is consistent with reasonable expectations on "ideal" behavior. The results may provide a practical tool for implementing force-reflecting teleoperation over the Internet.

ICRA Conference 1997 Conference Paper

A simple strategy for opening an unknown door

Günter Niemeyer
Jean-Jacques E. Slotine

Many robotic applications involve interactions with a simple mechanism, such as opening a door or turning a crank. Implementing such tasks using standard controllers may require precise knowledge of the kinematics or result in prohibitively large internal forces. We propose a simple method that learns the shape of the mechanism while in motion and generates little internal forces, in essence following the path of least resistance. The discussion is illustrated experimentally.

ICRA Conference 1997 Conference Paper

Designing force reflecting teleoperators with large time delays to appear as virtual tools

Günter Niemeyer
Jean-Jacques E. Slotine

We examine the behavior of force reflecting teleoperators which are subjected to large time delays up to several seconds. While stability is guaranteed by the use of passive transmission procedures, such systems can demonstrate particular dynamics which interfere with normal operation. Using the notion of wave variables for the analysis and implementation, and making appropriate design choices, we can construct a telerobot system with consistent and predictable behavior. This follows the design goal of a virtual tool accounting for the implicit limitations imposed by the delay.

ICRA Conference 1997 Conference Paper

Real-time path planning using harmonic potentials in dynamic environments

Hans Jacob S. Feder
Jean-Jacques E. Slotine

Motivated by fluid analogies, artificial harmonic potentials can eliminate local minima problems in robot path planning. In this paper, simple analytical solutions to planar harmonic potentials are derived using tools from fluid mechanics, and are applied to two-dimensional planning among multiple moving obstacles. These closed-form solutions enable real-time computation to be readily achieved.

ICRA Conference 1997 Conference Paper

Using wave variables for system analysis and robot control

Günter Niemeyer
Jean-Jacques E. Slotine

Wave variables were originally introduced in the context of time delayed force reflecting teleoperation. Their use provides significant benefits for control, including robustness to arbitrary delays and an inherent hybrid construction, well suited for handling unknown passive environments. They also suggest a new perspective for analysis, presenting information from an alternative viewpoint. In this paper we explore the concept of wave variables in a more general robotic and mechanical setting, leading to alternate control approaches.

ICRA Conference 1991 Conference Paper

Adaptive sliding control of an experimental underwater vehicle

Dana R. Yoerger
Jean-Jacques E. Slotine

It is shown that adaptive extensions of sliding control are effective for precise control of underwater vehicles through experiments with an actual vehicle. Adaptive sliding control permits direct nonlinear control system design, including online parameter estimation. Experimental results are detailed which were obtained from a tethered underwater vehicle equipped with a precision broadband acoustic navigation system and three-degree-of-freedom attitude instrumentation controlled through a real-time network of transputers. Simulation and experimental trials show online determination of vehicle parameters such as mass and nonlinear drag. >

ICRA Conference 1989 Conference Paper

Computational algorithms for adaptive compliant motion

Günter Niemeyer
Jean-Jacques E. Slotine

The authors explore performance issues linked to the effective implementation of adaptive manipulator controllers. In particular, they discuss computational implementations of the algorithm directly in Cartesian space, the utilization of kinematic redundancies, and applications to adaptive compliant motion control. The development is illustrated experimentally on a four-degree-of-freedom whole-arm articulated manipulator. It is suggested that the range of application of adaptive tracking controllers may extend well beyond adaptation to grasped loads. >

ICRA Conference 1988 Conference Paper

Indirect adaptive robot control

Weiping Li
Jean-Jacques E. Slotine

The theoretical issues linked to the development of indirect adaptive robot controllers are discussed, and some possible solutions are proposed. After a review of the prediction models used for robotic parameter estimation, a variety of parameter estimation methods are discussed under a common framework based on an exact solution approach. A novel indirect adaptive controller structure, which consists of a modified computed torque using parameters obtained from any of the estimators discussed, is presented. It is shown that a critical difficulty in using indirect adaptive control is the necessity to explicitly guarantee that the estimated inertia matrix remains positive definite in the course of adaptation, a requirement avoided by both the direct and the composite adaptive controllers. A practical solution to this difficulty is proposed. >

ICRA Conference 1988 Conference Paper

Inverse kinematic algorithms for redundant systems

Hari Das
Jean-Jacques E. Slotine
Thomas B. Sheridan

An iterative method of computing the solution of the inverse kinematic problem is developed for redundant systems using the transpose of the Jacobian matrix instead of the pseudoinverse. The solutions may be optimized on a criterion function or on physical constraints, such as obstacle avoidance. Stability and convergence of the method are shown. Although its convergence rate is only about half that of Newton's method, the advantage of the method is that it remains easily tractable close to the singular configurations of the manipulator. A hybrid method combining the Jacobian transpose and Newton's methods is proposed. Results of the application of the method on a 10-link manipulator in 2-D space are shown. >

ICRA Conference 1987 Conference Paper

Adaptive manipulator control a case study

Jean-Jacques E. Slotine
Weiping Li

Earlier work (Slotine and Li, 1986) exploits the particular structure of manipulator dynamics to develop a simple, globally convergent adaptive algorithm for trajectory control problems. The algorithm does not require measurements or estimates of the manipulator's joint accelerations, nor inversion of the estimated inertia matrix. This paper demonstrates the approach on a high-speed 2 d. o. f. semi-direct-drive robot. It shows that the manipulator mass properties, assumed to be initially unknown, can be precisely estimated within the first half second of a typical run. Similarly, the algorithm allows large loads of unknown mass properties to be precisely manipulated. Further, these experimental results demonstrate that the adaptive controller enjoys essentially the same level of robustness to unmodelled dynamics as a PD controller, yet achieves much better tracking accuracy than either PD or computed-torque schemes. Its superior performance for high speed operations, in the presence of parameter uncertainties, and its relative computational simplicity, make it a attractive option both to address complex industrial tasks, and to simplify high-level programming of more standard operations.

ICRA Conference 1987 Conference Paper

Adaptive strategies in constrained manipulation

Jean-Jacques E. Slotine
Weiping Li

Earlier work (Slotine and Li, 1986) demonstrates that using state feedback to directly modify a manipulator's energy function, rather than its fully expanded dynamics, represents a powerful approach to robot control, and, in particular, yields a simple globally convergent adaptive algorithm for trajectory control problems. An important practical feature of the algorithm is that is does not require measurements or estimates of the manipulator's joint accelerations. This paper rewrites the approach in term of end-effector dynamics, extends it to hybrid motion/force control, and discusses adaptive strategies involving mobile environments, such as the external motion control of an unknown passive mechanism.

ICRA Conference 1987 Conference Paper

Practical design of VSS controller using balance condition-Robotic application

Hideki Hashimoto
Jean-Jacques E. Slotine
J. X. Xu
Fumio Harashima

VSS controller is suited for robotic arms where the robust performances in the presence of parametric variations and disturbances are most important. The practical design of such robust VSS controller is discussed in this paper. The significant issue in the design of VSS is to avoid the chattering caused by the switched input. This problem is solved by introducing the continuous control input instead of switched input. The practical design of such control is obtained by using "Balance Condition". The "Balance Condition" is derived from the careful consideration on the bandwidth of plant dynamics and VSS controller, and gives many benefits in applications of VSS controller to actual systems. This paper shows the validity of balance condition in the robotic arm control. Simulations and experimental results using a position servo system (one degree of freedom robotic arm) are discussed from the practical point of view of robust control. In order to investigate multi-joint cases, simulations with a two-linkage robot arm are provided.

ICRA Conference 1987 Conference Paper

Supervisory control architecture for underwater teleoperation

Dana R. Yoerger
Jean-Jacques E. Slotine

An overall concept and specific system elements for teleoperated vehicles and manipulators are presented. The approach emphasizes continuous, real-time sharing of control between both the human operator and the computer system and is intended for application to the JASON underwater vehicle now in development. As JASON will have extremely high communications bandwidth available through a fiber optic cable, the emphasis will be on aiding and extending the capabilities of the human operator. Specific elements presented include task-resolved motion specification, rule-based inverse kinematics, and robust and adaptive nonlinear tracking control.

ICRA Conference 1986 Conference Paper

On modeling and adaptation in robot control

Jean-Jacques E. Slotine

Performance enhancement through modeling of high-frequency dynamics (such as structural resonant modes or actuator time-delays) and on-line parameter estimation (of large loads with unknown mass properties, or of characteristics of the environment in compliant motion control) is discussed in the context of sliding control. It is shown that such additions can be easily incorporated in existing controllers and yield predictable performance improvements.

ICRA Conference 1985 Conference Paper

Robustness issues in robot control

Jean-Jacques E. Slotine

A scheme is presented for the accurate trajectory control of robot manipulators in the presence of model uncertainty and disturbances. Based on the suction control methodology (an extension of sliding mode control), the scheme addresses the following problem: given the extent of parametric uncertainty (such as imprecisions or inertias, geometry, loads) and the frequency range of unmodeled dynamics (such as unmodeled structural modes, neglected time-delays), design a nonlinear feedback controller to achieve optimal tracking performance (in a suitable sense). The methodology is compared with algorithms such as the computed torque method, and is shown to combine in practice improved performance with simpler and more tractable controller designs. Extensions to compliant motion control are also discussed.