559,766 research outputs found
Design of an embedded inverse-feedforward biomolecular trackingcontroller for enzymatic reaction processes
Feedback control is widely used in chemical engineering to improve the performance and robustness of chemical processes. Feedback controllers require a ‘subtractor’ that is able to compute the error between the process output and the reference signal. In the case of embedded biomolecular control circuits, subtractors designed using standard chemical reaction network theory can only realise one-sided subtraction, rendering standard controller design approaches inadequate. Here, we show how a biomolecular controller that allows tracking of required changes in the outputs of enzymatic reaction processes can be designed and implemented within the framework of chemical reaction network theory. The controller architecture employs an inversion-based feedforward controller that compensates for the limitations of the one-sided subtractor that generates the error signals for a feedback controller. The proposed approach requires significantly fewer chemical reactions to implement than alternative designs, and should have wide applicability throughout the fields of synthetic biology and biological engineering
DESIGN AND ANALYSIS OF CONTROLLER FOR MOTION CONTROL SYSTEM
In motor drive applications, the mechanical properties of electrical motor under
various load and operating conditions are different. These changes highly influence
the performance of motor drive systems as the system's dynamic response under
these variations is affected. This project presents the design and development of a
system controller for motion control system. The basic approach used in this project
is by using the modem control engineering theory through the state space approach.
The state space design approach was used instead of the conventional control because
state space design is most suitable for nonlinear system and multi-input multi-output
(MIMO) system set-up. In the initial stage, the controller system for motion control
system was designed using the linearized equation of motion. For this motion control
system, the load torque was selected as the input with mechanical angnlar position as
the output. This project consists of modelling and simulation using Simulink, and
performance evaluation is done through the system analysis. A satisfactory
performance is achieved from the designed controller system which is better than the
conventional control in terms of controllability, observability and stability to be used
in motion control system
Understanding robust control theory via stick balancing
Robust control theory studies the effect of noise, disturbances, and other uncertainty on system performance. Despite growing recognition across science and engineering that robustness and efficiency tradeoffs dominate the evolution and design of complex systems, the use of robust control theory remains limited, partly because the mathematics involved is relatively inaccessible to nonexperts, and the important concepts have been inexplicable without a fairly rich mathematics background. This paper aims to begin changing that by presenting the most essential concepts in robust control using human stick balancing, a simple case study popular in both the sensorimotor control literature and extremely familiar to engineers. With minimal and familiar models and mathematics, we can explore the impact of unstable poles and zeros, delays, and noise, which can then be easily verified with simple experiments using a standard extensible pointer. Despite its simplicity, this case study has extremes of robustness and fragility that are initially counter-intuitive but for which simple mathematics and experiments are clear and compelling. The theory used here has been well-known for many decades, and the cart-pendulum example is a standard in undergrad controls courses, yet a careful reconsidering of both leads to striking new insights that we argue are of great pedagogical value
Data-driven Bayesian Control of Port-Hamiltonian Systems
Port-Hamiltonian theory is an established way to describe nonlinear physical
systems widely used in various fields such as robotics, energy management, and
mechanical engineering. This has led to considerable research interest in the
control of Port-Hamiltonian systems, resulting in numerous model-based control
techniques. However, the performance and stability of the closed-loop typically
depend on the quality of the PH model, which is often difficult to obtain using
first principles. We propose a Gaussian Processes (GP) based control approach
for Port-Hamiltonian systems (GPC-PHS) by leveraging gathered data. The
Bayesian characteristics of GPs enable the creation of a distribution
encompassing all potential Hamiltonians instead of providing a singular point
estimate. Using this uncertainty quantification, the proposed approach takes
advantage of passivity-based robust control with interconnection and damping
assignment to establish probabilistic stability guarantees
Project-Based Learning for Robot Control Theory: A Robot Operating System (ROS) Based Approach
Control theory is an important cornerstone of the robotics field and is
considered a fundamental subject in an undergraduate and postgraduate robotics
curriculum. Furthermore, project-based learning has shown significant benefits
in engineering domains, specifically in interdisciplinary fields such as
robotics which require hands-on experience to master the discipline adequately.
However, designing a project-based learning experience to teach control theory
in a hands-on setting can be challenging, due to the rigor of mathematical
concepts involved in the subject. Moreover, access to reliable hardware
required for a robotics control lab, including the robots, sensors, interfaces,
and measurement instruments, may not be feasible in developing countries and
even many academic institutions in the US. The current paper presents a set of
six project-based assignments for an advanced postgraduate Robot Control
course. The assignments leverage the Robot Operating System (ROS), an
open-source set of tools, libraries, and software, which is a de facto standard
for the development of robotics applications. The use of ROS, along with its
physics engine simulation framework, Gazebo, provides a hands-on robotics
experience equivalent to working with real hardware. Learning outcomes include:
i) theoretical analysis of linear and nonlinear dynamical systems, ii)
formulation and implementation of advanced model-based robot control algorithms
using classical and modern control theory, and iii) programming and performance
evaluation of robotic systems on physics engine robot simulators. Course
evaluations and student surveys demonstrate that the proposed project-based
assignments successfully bridge the gap between theory and practice, and
facilitate learning of control theory concepts and state-of-the-art robotics
techniques through a hands-on approach.Comment: 24 pages, 15 figures, accepted for publication in the 2023 ASEE
Annual Conference Proceedings, American Society for Engineering Educatio
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