1,682 research outputs found
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
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking
This manuscript presents control of a high-DOF fully actuated lower-limb
exoskeleton for paraplegic individuals. The key novelty is the ability for the
user to walk without the use of crutches or other external means of
stabilization. We harness the power of modern optimization techniques and
supervised machine learning to develop a smooth feedback control policy that
provides robust velocity regulation and perturbation rejection. Preliminary
evaluation of the stability and robustness of the proposed approach is
demonstrated through the Gazebo simulation environment. In addition,
preliminary experimental results with (complete) paraplegic individuals are
included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses
reviewers' concerns about the robustness of the algorithm and the motivation
for using such exoskeleton
Optimization Algorithms as Robust Feedback Controllers
Mathematical optimization is one of the cornerstones of modern engineering
research and practice. Yet, throughout all application domains, mathematical
optimization is, for the most part, considered to be a numerical discipline.
Optimization problems are formulated to be solved numerically with specific
algorithms running on microprocessors. An emerging alternative is to view
optimization algorithms as dynamical systems. Besides being insightful in
itself, this perspective liberates optimization methods from specific numerical
and algorithmic aspects and opens up new possibilities to endow complex
real-world systems with sophisticated self-optimizing behavior. Towards this
goal, it is necessary to understand how numerical optimization algorithms can
be converted into feedback controllers to enable robust "closed-loop
optimization". In this article, we focus on recent control designs under the
name of "feedback-based optimization" which implement optimization algorithms
directly in closed loop with physical systems. In addition to a brief overview
of selected continuous-time dynamical systems for optimization, our particular
emphasis in this survey lies on closed-loop stability as well as the robust
enforcement of physical and operational constraints in closed-loop
implementations. To bypass accessing partial model information of physical
systems, we further elaborate on fully data-driven and model-free operations.
We highlight an emerging application in autonomous reserve dispatch in power
systems, where the theory has transitioned to practice by now. We also provide
short expository reviews of pioneering applications in communication networks
and electricity grids, as well as related research streams, including extremum
seeking and pertinent methods from model predictive and process control, to
facilitate high-level comparisons with the main topic of this survey
Frequency-Aware Model Predictive Control
Transferring solutions found by trajectory optimization to robotic hardware
remains a challenging task. When the optimization fully exploits the provided
model to perform dynamic tasks, the presence of unmodeled dynamics renders the
motion infeasible on the real system. Model errors can be a result of model
simplifications, but also naturally arise when deploying the robot in
unstructured and nondeterministic environments. Predominantly, compliant
contacts and actuator dynamics lead to bandwidth limitations. While classical
control methods provide tools to synthesize controllers that are robust to a
class of model errors, such a notion is missing in modern trajectory
optimization, which is solved in the time domain. We propose frequency-shaped
cost functions to achieve robust solutions in the context of optimal control
for legged robots. Through simulation and hardware experiments we show that
motion plans can be made compatible with bandwidth limits set by actuators and
contact dynamics. The smoothness of the model predictive solutions can be
continuously tuned without compromising the feasibility of the problem.
Experiments with the quadrupedal robot ANYmal, which is driven by
highly-compliant series elastic actuators, showed significantly improved
tracking performance of the planned motion, torque, and force trajectories and
enabled the machine to walk robustly on terrain with unmodeled compliance
Adaptive Robust Control of Biomass Fuel Co-Combustion Process
The share of biomass in energy production is constantly growing. This is caused by environmental and industry standards and EU guidelines. Biomass is used in the process of co-firing in large power plants and industrial installations. In the existing power stations, biomass is milled and burned simultaneously with coal. However, low-emission combustion techniques, including biomass co-combustion, have some negative side effects that can be split into two categories. The direct effects influence the process control stability, whereas the indirect ones on combustion installations via increased corrosion or boiler slagging. The effects can be minimised using additional information about the process. The proper combustion diagnosis as well as an appropriate, robust control system ought to be applied. The chapter is devoted to the analysis of modern, robust control techniques for complex power engineering applications
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