986 research outputs found
Distributed Receding Horizon Control with Application to Multi-Vehicle Formation Stabilization
We consider the control of interacting subsystems whose dynamics and constraints are uncoupled, but whose state vectors are coupled non-separably in a single centralized cost function of a finite horizon optimal control problem. For a given centralized cost structure, we generate distributed optimal control problems for each subsystem and establish that the distributed receding horizon implementation is asymptotically stabilizing. The communication requirements between subsystems with coupling in the cost function are that each subsystem obtain the previous optimal control trajectory of those subsystems at each receding horizon update. The key requirements for stability are that each distributed optimal control not deviate too far from the previous optimal control, and that the receding horizon updates happen sufficiently fast. The theory is applied in simulation for stabilization of a formation of vehicles
UAV as a Reliable Wingman: A Flight Demonstration
In this brief, we present the results from a flight experiment demonstrating two significant advances in software enabled control: optimization-based control using real-time trajectory generation and logical programming environments for formal analysis of control software. Our demonstration platform consisted of a human-piloted F-15 jet flying together with an autonomous T-33 jet. We describe the behavior of the system in two scenarios. In the first, nominal state communications were present and the autonomous aircraft maintained formation as the human pilot flew maneuvers. In the second, we imposed the loss of high-rate communications and demonstrated an autonomous safe “lost wingman” procedure to increase separation and reacquire contact. The flight demonstration included both a nominal formation flight component and an execution of the lost wingman scenario
Implementation of Nonlinear Model Predictive Path-Following Control for an Industrial Robot
Many robotic applications, such as milling, gluing, or high precision
measurements, require the exact following of a pre-defined geometric path. In
this paper, we investigate the real-time feasible implementation of model
predictive path-following control for an industrial robot. We consider
constrained output path following with and without reference speed assignment.
We present results from an implementation of the proposed model predictive
path-following controller on a KUKA LWR IV robot.Comment: 8 pages, 3 figures; final revised versio
Robust nonlinear control of vectored thrust aircraft
An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations
Optimisation based control framework for autonomous vehicles: algorithm and experiment
This paper addresses both path tracking and local
trajectory generation for autonomous ground vehicles. An optimisation
based two-level control framework is proposed for this
task. The high-level control operates in a receding horizon fashion
by taking into account real-time sensory information. It generates
a feasible trajectory satisfying the nonlinear vehicle model and
various constraints, and resolves possible short term conflicts
through on-line optimisation. The low-level controller drives the
vehicle tracking the local trajectory in the presence of uncertainty
and disturbance. It is shown that the time varying controller
proposed in this paper guarantees stability under all possible
trajectories. The two-level control structure significantly facilitates
the real-time implementation of optimisation based control
techniques on systems with fast dynamics such as autonomous
vehicle systems. The proposed technique is implemented on a
small-scale autonomous vehicle in the lab. Both simulation and
experimental results demonstrate the efficiency of the proposed
technique
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