Tamás Keviczky

Decentralized receding horizon control for large scale dynamically decoupled systems

We present a detailed study on the design of decentralized receding horizon control (RHC) schemes for decoupled systems. We formulate an optimal control problem for a set of dynamically decoupled systems where the cost function and constraints couple the dynamical behavior of the systems. The coupling is described through a graph where each system is a node, and cost and constraints of the optimization problem associated with each node are only function of its state and the states of its neighbors. The complexity of the problem is addressed by breaking a centralized RHC controller into distinct RHC controllers of smaller sizes. Each RHC controller is associated with a different node and computes the local control inputs based only on the states of the node and of its neighbors. We analyze the properties of the proposed scheme and introduce sufficient stability conditions based on prediction errors. Finally, we focus on linear systems and show how to recast the stability conditions into a set of matrix semi-definiteness tests.

Subgradient methods and consensus algorithms for solving convex optimization problems

In this paper we propose a subgradient method for solving coupled optimization problems in a distributed way given restrictions on the communication topology. The iterative procedure maintains local variables at each node and relies on local subgradient updates in combination with a consensus process. The local subgradient steps are applied simultaneously as opposed to the standard sequential or cyclic procedure. We study convergence properties of the proposed scheme using results from consensus theory and approximate subgradient methods. The framework is illustrated on an optimal distributed finite-time rendezvous problem.

Distributed LQR Design for Identical Dynamically Decoupled Systems

We consider a set of identical decoupled dynamical systems and a control problem where the performance index couples the behavior of the systems. The coupling is described through a communication graph where each system is a node and the control action at each node is only function of its state and the states of its neighbors. A distributed control design method is presented which requires the solution of a single linear quadratic regulator (LQR) problem. The size of the LQR problem is equal to the maximum vertex degree of the communication graph plus one. The design procedure proposed in this paper illustrates how stability of the large-scale system is related to the robustness of local controllers and the spectrum of a matrix representing the desired sparsity pattern of the distributed controller design problem.

Decentralized Receding Horizon Control and Coordination of Autonomous Vehicle Formations

This paper describes the application of a novel methodology for high-level control and coordination of autonomous vehicle teams and its demonstration on high-fidelity models of the organic air vehicle developed at Honeywell Laboratories. The scheme employs decentralized receding horizon controllers that reside on each vehicle to achieve coordination among team members. An appropriate graph structure describes the underlying communication topology between the vehicles. On each vehicle, information about neighbors is used to predict their behavior and plan conflict-free trajectories that maintain coordination and achieve team objectives. When feasibility of the decentralized control is lost, collision avoidance is ensured by invoking emergency maneuvers that are computed via invariant set theory.

MPC-based approach to active steering for autonomous vehicle systems

In this paper a novel approach to autonomous steering systems is presented. A model predictive control (MPC) scheme is designed in order to stabilize a vehicle along a desired path while fulfilling its physical constraints. Simulation results show the benefits of the systematic control methodology used. In particular we show how very effective steering manoeuvres are obtained as a result of the MPC feedback policy. Moreover, we highlight the trade off between the vehicle speed and the required preview on the desired path in order to stabilize the vehicle. The paper concludes with highlights on future research and on the necessary steps for experimental validation of the approach.

Predictive control approach to autonomous vehicle steering

A model predictive control (MPC) approach to active steering is presented for autonomous vehicle systems. The controller is designed to stabilize a vehicle along a desired path while rejecting wind gusts and fulfilling its physical constraints. Simulation results of a side wind rejection scenario and a double lane change maneuver on slippery surfaces show the benefits of the systematic control methodology used. A trade-off between the vehicle speed and the required preview on the desired path for vehicle stabilization is highlighted

Collision-free UAV formation flight using decentralized optimization and invariant sets

We consider the problem of formation flight for a set of unmanned air vehicles (UAV). We propose a decentralized control design procedure which guarantees collision avoidance and constraint fulfillment. The control design is based on a decentralized receding horizon control (RHC) scheme. Vehicle collision avoidance is ensured by considering a collision-free emergency maneuver which is implemented when feasibility of the decentralized RHC scheme is lost. Bounds on speed and accelerations are computed off-line using simple polyhedral invariant set computations. Such bounds guarantee that the implementation of the emergency maneuver leads to collision-free trajectories. The proposed decentralized control scheme is formulated as mixed-integer linear programs of small sizes which can be translated into equivalent piecewise affine state-feedback controllers. These controllers can be implemented in real-time once the corresponding look-up tables are downloaded to the hardware platform of the UAVs.

Hybrid decentralized control of large scale systems

Motivated by three applications which are under investigation at the Honeywell Research Laboratory in Minneapolis, we introduce a class of large scale control problems. In particular we show that a formation flight problem, a paper machine control problem and the coordination of cameras in a monitoring network can be cast into this class. In the second part of the paper we propose a decentralized control scheme to tackle the complexity of the problem. The scheme makes use of logic rules which improve stability and feasibility of the decentralized method by enforcing coordination. The decentralized control laws which respect the rules are computed using hybrid control design.

A Study On Distributed Model Predictive Consensus

We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.

Software-Enabled Receding Horizon Control for Autonomous Unmanned Aerial Vehicle Guidance

This paper describes autonomous unmanned aerial vehicle (UAV) guidance technologies developed and demonstrated in a flight test sponsored by the DARPA Software Enabled Control program. The flight experiment took place in June 2004 using a Boeing UAV testbed and demonstrated important autonomy capabilities enabled by a receding horizon guidance controller and fault detection filter. The receding horizon controller (RHC) design process is presented in detail as well as demonstration scenarios which were designed to exercise and evaluate the primary functionalities of the control system. Simulation results of the key capabilities are shown and compared with recorded flight data for evaluation purposes. Hardware-in-the-loop simulations and other high-fidelity test run results illustrate secondary capabilities such as controller reconfiguration due to actuator fault and maneuvering limit enforcement using output constraints in the receding horizon approach.