Showing papers presented at "American Control Conference in 2008"

Decentralized estimation and control of graph connectivity in mobile sensor networks

Abstract: The ability of a robot team to reconfigure itself is useful in many applications: for metamorphic robots to change shape, for swarm motion towards a goal, for biological systems to avoid predators, or for mobile buoys to clean up oil spills. In many situations, auxiliary constraints, such as connectivity between team members and limits on the maximum hop-count, must be satisfied during reconfiguration. In this paper, we show that both the estimation and control of the graph connectivity can be accomplished in a decentralized manner. We describe a decentralized estimation procedure that allows each agent to track the algebraic connectivity of a time-varying graph. Based on this estimator, we further propose a decentralized gradient controller for each agent to maintain global connectivity during motion.

Computed torque control with nonparametric regression models

Abstract: Computed torque control allows the design of considerably more precise, energy-efficient and compliant controls for robots. However, the major obstacle is the requirement of an accurate model for torque generation, which cannot be obtained in some cases using rigid-body formulations due to unmodeled nonlinearities, such as complex friction or actuator dynamics. In such cases, models approximated from robot data present an appealing alternative. In this paper, we compare two non- parametric regression methods for model approximation, i.e., locally weighted projection regression (LWPR) and Gaussian process regression (GPR). While locally weighted regression was employed for real-time model estimation in learning adaptive control, Gaussian process regression has not been used in control to-date due to high computational requirements. The comparison includes the assessment of model approximation for both regression methods using data originated from SARCOS robot arm, as well as an evaluation of the robot tracking performance in computed torque control employing the approximated models. Our results show that GPR can be applied for realtime control achieving higher accuracy. However, for the online learning LWPR is superior by reason of lower computational requirements.

Distributed tracking in sensor networks with limited sensing range

Abstract: In this paper, we address the problem of distributed tracking of a maneuvering target using sensor networks with nodes that possess limited sensing range (LSR). In such sensor networks, a target can only be observed by a small percentage of the sensors and is practically hidden to the remaining majority of the nodes. This feature is shared among most of today's wireless sensor networks and differentiates them from their traditional counterparts involving data fusion for long-range sensors such as radars and sonars. Distributed Kalman filters have proven to be effective and scalable algorithms for distributed tracking in sensor networks. Our main contribution is to give a message-passing version of the Kalman- Consensus Filter (KCF) - introduced by the first author in CDC '07 - that is capable of distributed tracking of a maneuvering target with a satisfactory performance. The architecture of this filter is a peer-to-peer (P2P) network of microfllters as extensions of local Kalman filters. The model proposed for the maneuvering target is a piece-wise linear switching system with two distinct modes of behavior that enables the target to stay inside a rectangular region in all time (for a bounded set of initial conditions). Simulation results are provided for a lattice-type sensor network with 100 LSR nodes tracking a target with switching modes of behavior which demonstrate the effectiveness of the proposed distributed data fusion and tracking algorithms.

Backstepping/nonlinear H ∞ control for path tracking of a quadrotor unmanned aerial vehicle

Abstract: This paper presents a nonlinear robust control strategy to solve the path tracking problem for a quadrotor unmanned aerial vehicle. The helicopter motion equations is obtained by the Lagrange- Euler formalism. The control structure is performed through a nonlinear Hinfin controller to stabilize the rotational movements and a control law based on backstepping approach to track the reference trajectory. Finally, simulations results in presence of aerodynamic moments disturbances and parametric uncertainty is carried out to corroborate the effectiveness and the robustness of the strategy proposed.

Self-triggered stabilization of homogeneous control systems

Abstract: Digital implementations of feedback laws commonly consider periodic execution of control tasks. In this paper we go beyond the periodic model by developing self-triggered schedules for the execution of control tasks. These schedules guarantee asymptotic stability under sample-and-hold implementations while drastically reducing processor usage when compared with the more traditional periodic implementations. At the technical level the results rely on a homogeneity assumption on the continuous dynamics and extend to the self-triggered framework some of the advantages of event-triggered implementations recently studied by the authors. The results presented in this paper can be seen as an effort towards understanding the real-time scheduling requirements of control tasks.

Jensen inequality approach to stability analysis of discrete-time systems with time-varying delay

Abstract: This paper studies the problem of stability analysis for discrete-time delay systems. By using new Lyapunov functional and the discrete Jensen inequality, new stability criteria are presented in terms of linear matrix inequalities (LMIs) and proved to be less conservative than the existing ones. Compared with the existing results, the computational complexity of the obtained stability criteria is reduced greatly since less decision variables are involved. Numerical examples are given to illustrate the effectiveness and advantages of the proposed method.

Event-triggered broadcasting across distributed networked control systems

Abstract: This paper examines event-triggered broadcasting of state information in distributed control systems implemented over wireless communication networks. Event-triggering requires a subsystem to only broadcast its state information when the local state error exceeds a given threshold. The paper designs an event triggering scheme that assures asymptotic stability of the entire networked system. The results apply to networks of linear time-invariant systems. We derive a lower bound on the estimated time to next broadcast. Simulation results show that event triggering allows a subsystem to adjust its broadcast periods to the amount of activity in its immediate neighborhood. Simulation results also show that event-triggering's average broadcast period scales well with system size. These results are significant because they show how one might stabilize distributed control systems over ad hoc wireless networks without necessarily requiring a high degree of synchronization within the communication network.

Formation UAV flight control using virtual structure and motion synchronization

Abstract: In this paper, the synchronized position tracking controller is incorporated in formation flight control for multiple flying wings. With this technology, the performance and effectiveness of the formation controller are improved when the virtual structure approach is utilized to maintain formation geometry. Simulations are conducted on the nonlinear model of two flying wings to verify the proposed controller.

Leader-follower cooperative attitude control of multiple rigid bodies

Abstract: In this paper we extend our previous results on coordinated control of rotating rigid bodies to the case of teams with heterogenous agents. We assume that only a certain subgroup of the agents (the leaders) are vested with the main control objective, that is, maintain constant relative orientation amongst themselves. The rest of the team must meet relaxed control specifications, namely maintain their respective orientations within certain limits dictated by the orientation of the leaders. The proposed control laws respect the limited information each rigid body has with respect to the rest of its peers (leaders or followers) as well as with the rest of the team. Each rigid body is equipped with a control law that utilizes the Laplacian matrix of the associated communication graph, which encodes the communication links between the team members. Similarly to the linear case, the convergence of the multi-agent system relies on the connectivity of the communication graph.

Tire-road forces estimation using extended Kalman filter and sideslip angle evaluation

Abstract: Main task in driving safety is the understanding and prevention of risky situations. While looking closer at the accidents data analysis, it appears that vehicle loss of control represents a huge part of car accidents. Preventing such kind of accidents, using assistance systems needs several type of information about vehicle state and vehicle-road interaction phenomenon. Longitudinal velocity, acceleration and yaw rate are easily measured using low cost sensors that are actually mounted in standard on a large part of vehicles. However, other parameters, which have a major impact on vehicle dynamics, are more difficult to measure using vehicle industry technology sensors. These are for example the used friction coefficient and the sideslip angle. Using an appropriate vehicle model and available measurements, the vehicle state as well as the road/tire interaction forces are reconstructed by implementing an extended Kalman filter. Thereafter, we evaluate the used friction coefficient and the sideslip angle estimates. Simulation and estimation results are then compared to real measurements collected by an equipped test vehicle on Satory test track.

Online vehicle mass estimation using recursive least squares and supervisory data extraction

Abstract: This paper examines the online estimation of onroad vehicles' mass. It classifies existing estimators based on the dynamics they use for estimation and whether they are event-seeking or averaging. It then proposes an algorithm comparable to this literature in accuracy and speed, but unique in its minimal instrumentation needs and ability to provide conservative mass error estimates, in the 3sigma sense. The algorithm builds on the simple idea, inspired by perturbation theory, that inertial dynamics dominate vehicle motion over certain types of maneuvers. A supervisory algorithm searches for those maneuvers, and feeds the resulting filtered data into a recursive least squares-based mass estimator and conservative mass error estimator. Both simulation and field data demonstrate the viability of the resulting approach.

Consensus tracking under directed interaction topologies: Algorithms and experiments

Abstract: Consensus tracking problems with, respectively, bounded control effort and directed switching interaction topologies are considered when a time-varying consensus reference state is available to only a subgroup of a team. A consensus tracking algorithm explicitly accounting for bounded control effort is proposed and analyzed under a fixed directed interaction topology. Furthermore, convergence analysis for a consensus tracking algorithm is provided when the time-varying consensus reference state is available to a dynamically changing subgroup of the team under directed switching inter-vehicle interaction topologies. Experimental results of a formation control application are demonstrated on a multi-robot platform to validate one of the proposed consensus tracking algorithms.

Multi-UAV dynamic routing with partial observations using restless bandit allocation indices

Abstract: Motivated by the type of missions currently performed by unmanned aerial vehicles, we investigate a discrete dynamic vehicle routing problem with a potentially large number of targets and vehicles. Each target is modeled as an independent two-state Markov chain, whose state is not observed if the target is not visited by some vehicle. The goal for the vehicles is to collect rewards obtained when they visit the targets in a particular state. This problem can be seen as a type of restless bandits problem with partial information. We compute an upper bound on the achievable performance and obtain in closed form an index policy proposed by Whittle. Simulation results provide evidence for the outstanding performance of this index heuristic and for the quality of the upper bound.

Optimal pairs trading: A stochastic control approach

Abstract: In this paper, we propose a stochastic control approach to the problem of pairs trading. We model the log-relationship between a pair of stock prices as an Ornstein-Uhlenbeck process and use this to formulate a portfolio optimization based stochastic control problem. We are able to obtain the optimal solution to this control problem in closed form via the corresponding Hamilton-Jacobi-Bellman equation. We also provide closed form maximum-likelihood estimation values for the parameters in the model. The approach is illustrated with a numerical example involving simulated data for a pair of stocks.

Harmonic potential field path planning for high speed vehicles

Abstract: This paper presents a method to represent complex shaped obstacles in harmonic potential fields used for vehicle path planning. The proposed method involves calculating the potential field for a series of circular obstacles inserted into the unobstructed potential field. The potential field for the total obstacle is a weighted average of the circular obstacle potential fields. This method explicitly calculates a stream function for the potential field. The need for the stream function is explained for situations involving controlling a dynamic system such as a high speed ground vehicle. The traditional potential field controller is also augmented to take the stream function into account. Simulation results are presented to show the effectiveness of the potential field generation technique and the augmented vehicle controller.

Finite-time stability for time-varying nonlinear dynamical systems

Abstract: Finite-time stability involves dynamical systems whose trajectories converge to an equilibrium state in finite time. Since finite-time convergence implies non-uniqueness of system solutions in backward time, such systems possess non-Lipschitzian dynamics. In this paper, we address finite-time and uniform finite-time stability of time-varying systems. Specifically, we provide Lyapunov and converse Lyapunov conditions for finite-time stability of a time-varying system. Furthermore, we show that finite-time stability leads to uniqueness of solutions in forward time. In addition, we establish necessary and sufficient conditions for continuity of the settling-time function of a nonlinear time-varying system.

Optimal control of power split for a hybrid electric refuse vehicle

Abstract: An optimal power split strategy in a hybrid electric refuse truck is presented. Using Pontryagin's Minimum Principle, a set of solution candidates is found and evaluated in order to find the optimal control strategy. Simulation results are shown to demonstrate the effectiveness of the strategy.

A hierarchical Model Predictive Control framework for autonomous ground vehicles

Abstract: A hierarchical framework based on Model Predictive Control (MPC) for autonomous vehicles is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle while fulfilling various physical and design constraints. We start from the low-level active steering-controller presented in [3], [9] and integrate it with a high level trajectory planner. At both levels MPC design is used. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model. At the low- level a MPC controller computes the vehicle inputs in order to best follow the desired trajectory based on detailed nonlinear vehicle model. This article presents the approach, the method for implementing it, and successful preliminary simulative results on slippery roads at high entry speed.

A multiple UAV system for vision-based search and localization

Abstract: The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined. Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error co- variance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.

Observer with sample-and-hold updating for Lipschitz nonlinear systems with nonuniformly sampled measurements

Abstract: This paper presents a new observer design for Lipschitz nonlinear continuous-time systems with nonuniformly sampled measurements. Based on recent results in sampled-data control of linear continuous-time systems, linear matrix inequality (LMI) conditions are established to guarantee global stability of the estimation error dynamics and to design the observer matrix. The applicability of the proposed observer is demonstrated via two examples, that are the flexible joint robotic arm and Chua's circuit.

The effect of nonminimum-phase zero locations on the performance of feedforward model-inverse control techniques in discrete-time systems

Abstract: Noncollocated sensors and actuators, and/or fast sample rates with plants having high relative degree, can lead to nonminimum-phase (NMP) discrete-time zero dynamics that complicate the control system design. In this paper, we examine three stable approximate model-inverse feedforward control techniques, the nonmimimum-phase zeros ignore (NPZ-Ignore), the zero-phase-error tracking controller (ZPETC) and the zero-magnitude-error tracking controller (ZMETC), which have frequently been used for NMP systems. We analyze how the discrete-time NMP zero locations in the z-plane affect the success of the NPZ-Ignore, ZPETC, and ZMETC model-inverse techniques. We also provide simulation examples using plants based on the system identification of an atomic force microscope and a hard disk drive, showing the tradeoffs in performance relative to NMP zero locations in these different application systems.

Decentralized reactive collision avoidance for multiple unicycle-type vehicles

Abstract: This paper addresses a novel approach to the n-vehicle collision avoidance problem. The vehicle model used is a planar constant-speed unicycle, chosen for its wide applicability to ground, sea, and air vehicles. An algorithm is developed which guarantees all vehicles remain free of collisions while attempting to attain their trajectory goals (given certain restrictions on their initial conditions). This controller is reactive and decentralized, making it well suited for real time applications, and explicitly accounts for actuation limits. Results are demonstrated in simulation.

Group health management of UAV teams with applications to persistent surveillance

Abstract: Unmanned aerial vehicles (UAVs) are well-suited to a wide range of mission scenarios, such as search and rescue, border patrol, and military surveillance. The complex and distributed nature of these missions often requires teams of UAVs to work together. Furthermore, overall mission performance can be strongly influenced by vehicle failures or degradations, so an autonomous mission system must account for the possibility of these anomalies if it is to maximize performance. This paper presents a general health management methodology for designing mission systems that can anticipate the negative effects of various types of anomalies on the future mission state and choose actions that mitigate those effects. The formulation is then specialized to the problem of providing persistent surveillance coverage using a group of UAVs, where uncertain fuel usage dynamics and strong interdependence effects between vehicles must be considered. Finally, the paper presents results showing that the health-aware persistent surveillance planner based on this formulation exhibits excellent performance in both simulated and real flight test experiments.

Stability analysis for a class of networked/embedded control systems: A discrete-time approach

Abstract: Motivated by the widespread use of networked and embedded control systems, an algorithm for stability analysis is proposed for sampled-data feedback control systems with uncertainly time-varying sampling intervals. The algorithm is based on the robustness of discrete-time systems against perturbation caused by the variation of sampling intervals. The validity of the algorithm is demonstrated by numerical examples.

Autonomous ground vehicle control system for high-speed and safe operation

Abstract: This paper describes a new trajectory tracking control system for autonomous ground vehicles (AGV) toward safe and high-speed operation enabled by incorporating vehicle dynamics control (VDC) into the AGV. The control system consists of two levels: an AGV desired yaw rate generator based on a kinematic model, and a yaw rate controller based on the vehicle/tire dynamic models. The separation between AGV trajectory tracking and low-level actuation allows the incorporation of the VDC into the AGV systems. Sliding mode control is utilized to handle the system uncertainties. The performance of the proposed control system is evaluated by using a high-fidelity (experimentally validated) full-vehicle sport utility vehicle (SUV) model (rear-drive and front-steer) provided by CarSimregon a race track. Compared with the results for typically-employed position error based AGV control, significant performance improvement is observed.

Robust nonlinear sequential loop closure control design for an air-breathing hypersonic vehicle model

Abstract: This paper describes the design of a nonlinear robust/adaptive controller for an air-breathing hypersonic vehicle model. Due to its complexity, a high fidelity model of the vehicle dynamics derived from first principles is used only in simulations, while a simplified model is adopted for control design. This control-oriented model retains most of the features of the high fidelity model, including non-minimum phase characteristic of the flight-path angle dynamics and strong couplings between the engine and flight dynamics, whereas flexibility effects are regarded as a dynamic perturbation. A nonlinear sequential loop-closure approach is adopted to design a dynamic state-feedback controller that provides stable tracking of velocity and altitude reference trajectories and allows to impose a desired trim value for the angle of attack. Simulation results show that the proposed methodology achieves excellent tracking performances in spite of parameter uncertainties.

L 1 adaptive controller for a class of systems with unknown nonlinearities: Part I

Abstract: This paper presents an extension of the L 1 adaptive controller to a class of general nonlinear uncertain systems, nonaffine in control The control signal interacts with system states and time-varying uncertainties in an unknown nonlinear way The adaptive controller ensures uniformly bounded transient for system's both input and output signals simultaneously The performance bounds can be systematically improved by increasing the adaptation rate Simulation results verify the theoretical findings

Lyapunov tools for predictor feedbacks for delay systems: Inverse optimality and robustness to delay mismatch

Abstract: We consider LTI finite-dimensional, completely controllable, but possibly open-loop unstable, plants, with arbitrarily long actuator delay, and the corresponding predictor-based feedback for delay compensation. We study the problem of inverse-optimal re-design of the predictor-based feedback law. We obtain a simple modification of the basic predictor-based controller, which employs a low-pass filter, and has been proposed previously by Mondie and Michiels for achieving robustness to discretization of the integral term in the predictor feedback law. The key element in our work is the employment of an infinite-dimensional "backstepping" transformation, and the resulting complete Lyapunov function, for the infinite dimensional systems consisting of the state of the ODE plant and the delay state. The Lyapunov function allows us to establish inverse optimality of the modified feedback and its disturbance attenuation properties. For the basic predictor feedback, the availability of the Lyapunov function also allows us to prove robustness to small delay mismatch (in both positive and negative directions).

A heave compensation approach for offshore cranes

Abstract: Offshore installations during harsh sea conditions results in rigorous requirements in terms of safety and efficiency for the involved crane system. Hence a heave compensation system based on heave motion prediction and an inversion based control strategy is proposed. The control objective is to let the rope suspended payload track a desired reference trajectory in an earth fixed frame without being influenced by the heave motion of the ship or vessel. Therefor a combination of a trajectory tracking disturbance decoupling controller and a prediction algorithm is presented and evaluated with simulation and measurement results.

Voronoi based coverage control with anisotropic sensors

Abstract: In this paper the coverage control problem for mobile sensor networks is studied. The novelty is to consider an anisotropic sensor model where the performance of the sensor depends not only on the distance but also on the orientation to the target. By adapting the Lloyd algorithm and assuming a fixed and equal sensor orientation, a distributed control law is derived. Aside from coverage, the control law also guarantees collision avoidance between the agents. A simulation is provided to illustrate the results obtained in this paper. Furthermore, a numerical performance analysis to compare the anisotropic sensors modelling to isotropic approximations is performed.