10,641 research outputs found
Adaptive Horizon Model Predictive Control and Al'brekht's Method
A standard way of finding a feedback law that stabilizes a control system to
an operating point is to recast the problem as an infinite horizon optimal
control problem. If the optimal cost and the optmal feedback can be found on a
large domain around the operating point then a Lyapunov argument can be used to
verify the asymptotic stability of the closed loop dynamics. The problem with
this approach is that is usually very difficult to find the optimal cost and
the optmal feedback on a large domain for nonlinear problems with or without
constraints. Hence the increasing interest in Model Predictive Control (MPC).
In standard MPC a finite horizon optimal control problem is solved in real time
but just at the current state, the first control action is implimented, the
system evolves one time step and the process is repeated. A terminal cost and
terminal feedback found by Al'brekht's methoddefined in a neighborhood of the
operating point is used to shorten the horizon and thereby make the nonlinear
programs easier to solve because they have less decision variables. Adaptive
Horizon Model Predictive Control (AHMPC) is a scheme for varying the horizon
length of Model Predictive Control (MPC) as needed. Its goal is to achieve
stabilization with horizons as small as possible so that MPC methods can be
used on faster and/or more complicated dynamic processes.Comment: arXiv admin note: text overlap with arXiv:1602.0861
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
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
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