1,298 research outputs found
Sparse Iterative Learning Control with Application to a Wafer Stage: Achieving Performance, Resource Efficiency, and Task Flexibility
Trial-varying disturbances are a key concern in Iterative Learning Control
(ILC) and may lead to inefficient and expensive implementations and severe
performance deterioration. The aim of this paper is to develop a general
framework for optimization-based ILC that allows for enforcing additional
structure, including sparsity. The proposed method enforces sparsity in a
generalized setting through convex relaxations using norms. The
proposed ILC framework is applied to the optimization of sampling sequences for
resource efficient implementation, trial-varying disturbance attenuation, and
basis function selection. The framework has a large potential in control
applications such as mechatronics, as is confirmed through an application on a
wafer stage.Comment: 12 pages, 14 figure
Distributionally Robust Chance Constrained Data-enabled Predictive Control
We study the problem of finite-time constrained optimal control of unknown
stochastic linear time-invariant systems, which is the key ingredient of a
predictive control algorithm -- albeit typically having access to a model. We
propose a novel distributionally robust data-enabled predictive control (DeePC)
algorithm which uses noise-corrupted input/output data to predict future
trajectories and compute optimal control inputs while satisfying output chance
constraints. The algorithm is based on (i) a non-parametric representation of
the subspace spanning the system behaviour, where past trajectories are sorted
in Page or Hankel matrices; and (ii) a distributionally robust optimization
formulation which gives rise to strong probabilistic performance guarantees. We
show that for certain objective functions, DeePC exhibits strong out-of-sample
performance, and at the same time respects constraints with high probability.
The algorithm provides an end-to-end approach to control design for unknown
stochastic linear time-invariant systems. We illustrate the closed-loop
performance of the DeePC in an aerial robotics case study
Relaxing Fundamental Assumptions in Iterative Learning Control
Iterative learning control (ILC) is perhaps best decribed as an open loop feedforward control technique where the feedforward signal is learned through repetition of a single task. As the name suggests, given a dynamic system operating on a finite time horizon with the same desired trajectory, ILC aims to iteratively construct the inverse image (or its approximation) of the desired trajectory to improve transient tracking. In the literature, ILC is often interpreted as feedback control in the iteration domain due to the fact that learning controllers use information from past trials to drive the tracking error towards zero. However, despite the significant body of literature and powerful features, ILC is yet to reach widespread adoption by the control community, due to several assumptions that restrict its generality when compared to feedback control. In this dissertation, we relax some of these assumptions, mainly the fundamental invariance assumption, and move from the idea of learning through repetition to two dimensional systems, specifically repetitive processes, that appear in the modeling of engineering applications such as additive manufacturing, and sketch out future research directions for increased practicality: We develop an L1 adaptive feedback control based ILC architecture for increased robustness, fast convergence, and high performance under time varying uncertainties and disturbances. Simulation studies of the behavior of this combined L1-ILC scheme under iteration varying uncertainties lead us to the robust stability analysis of iteration varying systems, where we show that these systems are guaranteed to be stable when the ILC update laws are designed to be robust, which can be done using existing methods from the literature. As a next step to the signal space approach adopted in the analysis of iteration varying systems, we shift the focus of our work to repetitive processes, and show that the exponential stability of a nonlinear repetitive system is equivalent to that of its linearization, and consequently uniform stability of the corresponding state space matrix.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133232/1/altin_1.pd
Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints
In the recent literature, significant and substantial efforts have been
dedicated to the important area of multi-agent decision-making problems.
Particularly here, the model predictive control (MPC) methodology has
demonstrated its effectiveness in various applications, such as mobile robots,
unmanned vehicles, and drones. Nevertheless, in many specific scenarios
involving the MPC methodology, accurate and effective system identification is
a commonly encountered challenge. As a consequence, the overall system
performance could be significantly weakened in outcome when the traditional MPC
algorithm is adopted under such circumstances. To cater to this rather major
shortcoming, this paper investigates an alternate data-driven approach to solve
the multi-agent decision-making problem. Utilizing an innovative modified
methodology with suitable closed-loop input/output measurements that comply
with the appropriate persistency of excitation condition, a non-parametric
predictive model is suitably constructed. This non-parametric predictive model
approach in the work here attains the key advantage of alleviating the rather
heavy computational burden encountered in the optimization procedures typical
in alternative methodologies requiring open-loop input/output measurement data
collection and parametric system identification. Then with a conservative
approximation of probabilistic chance constraints for the MPC problem, a
resulting deterministic optimization problem is formulated and solved
efficiently and effectively. In the work here, this intuitive data-driven
approach is also shown to preserve good robustness properties. Finally, a
multi-drone system is used to demonstrate the practical appeal and highly
effective outcome of this promising development in achieving very good system
performance.Comment: 10 pages, 6 figure
Model-Free Synthesis via Adversarial Reinforcement Learning
Motivated by the recent empirical success of policy-based reinforcement
learning (RL), there has been a research trend studying the performance of
policy-based RL methods on standard control benchmark problems. In this paper,
we examine the effectiveness of policy-based RL methods on an important robust
control problem, namely synthesis. We build a connection between robust
adversarial RL and synthesis, and develop a model-free version of the
well-known -iteration for solving state-feedback synthesis with
static -scaling. In the proposed algorithm, the step mimics the
classical central path algorithm via incorporating a recently-developed
double-loop adversarial RL method as a subroutine, and the step is based on
model-free finite difference approximation. Extensive numerical study is also
presented to demonstrate the utility of our proposed model-free algorithm. Our
study sheds new light on the connections between adversarial RL and robust
control.Comment: Accepted to ACC 202
Adaptive Output Feedback Model Predictive Control
Model predictive control (MPC) for uncertain systems in the presence of hard
constraints on state and input is a non-trivial problem, and the challenge is
increased manyfold in the absence of state measurements. In this paper, we
propose an adaptive output feedback MPC technique, based on a novel combination
of an adaptive observer and robust MPC, for single-input single-output
discrete-time linear time-invariant systems. At each time instant, the adaptive
observer provides estimates of the states and the system parameters that are
then leveraged in the MPC optimization routine while robustly accounting for
the estimation errors. The solution to the optimization problem results in a
homothetic tube where the state estimate trajectory lies. The true state
evolves inside a larger outer tube obtained by augmenting a set, invariant to
the state estimation error, around the homothetic tube sections. The proof for
recursive feasibility for the proposed `homothetic and invariant' two-tube
approach is provided, along with simulation results on an academic system.Comment: 6 page
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