9,579 research outputs found
An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation
We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499
Plume: A Framework for High Performance Deep RL Network Controllers via Prioritized Trace Sampling
Deep Reinforcement Learning (DRL) has shown promise in various networking
environments. However, these environments present several fundamental
challenges for standard DRL techniques. They are difficult to explore and
exhibit high levels of noise and uncertainty. Although these challenges
complicate the training process, we find that in practice we can substantially
mitigate their effects and even achieve state-of-the-art real-world performance
by addressing a factor that has been previously overlooked: the skewed input
trace distribution in DRL training datasets.
We introduce a generalized framework, Plume, to automatically identify and
balance the skew using a three-stage process. First, we identify the critical
features that determine the behavior of the traces. Second, we classify the
traces into clusters. Finally, we prioritize the salient clusters to improve
the overall performance of the controller. Plume seamlessly works across DRL
algorithms, without requiring any changes to the DRL workflow. We evaluated
Plume on three networking environments, including Adaptive Bitrate Streaming,
Congestion Control, and Load Balancing. Plume offers superior performance in
both simulation and real-world settings, across different controllers and DRL
algorithms. For example, our novel ABR controller, Gelato trained with Plume
consistently outperforms prior state-of-the-art controllers on the live
streaming platform Puffer for over a year. It is the first controller on the
platform to deliver statistically significant improvements in both video
quality and stalling, decreasing stalls by as much as 75%
A model-free control strategy for an experimental greenhouse with an application to fault accommodation
Writing down mathematical models of agricultural greenhouses and regulating
them via advanced controllers are challenging tasks since strong perturbations,
like meteorological variations, have to be taken into account. This is why we
are developing here a new model-free control approach and the corresponding
intelligent controllers, where the need of a good model disappears. This
setting, which has been introduced quite recently and is easy to implement, is
already successful in many engineering domains. Tests on a concrete greenhouse
and comparisons with Boolean controllers are reported. They not only
demonstrate an excellent climate control, where the reference may be modified
in a straightforward way, but also an efficient fault accommodation with
respect to the actuators
Feedback control by online learning an inverse model
A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made
Two Separate Continually Online-Trained Neurocontrollers for Excitation and Turbine Control of a Turbogenerator
This paper presents the design of two separate continually online trained (COT) neurocontrollers for excitation and turbine control of a turbogenerator connected to the infinite bus through a transmission line. These neurocontrollers augment/replace the conventional automatic voltage regulator and the turbine governor of a generator. A third COT artificial neural network is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT neurocontrollers can control turbogenerators under steady-state as well as transient conditions and, thus, allow turbogenerators to operate more closely to their steady-state stability limit
Two Separate Continually Online Trained Neurocontrollers for Excitation and Turbine Control of a Turbogenerator
This paper presents the design of two separate continually online trained (GOT) artificial neural network (ANN) controllers for excitation and turbine control of a turbogenerator connected to the infinite bus through a transmission line. These neurocontrollers augment/replace the conventional automatic voltage regulator and the turbine governor of a generator. A third COT ANN is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT ANN controllers can control turbogenerators under steady state as well as transient conditions and thus allow turbogenerators to operate more closely to their steady state stability limits
FPGA design methodology for industrial control systems—a review
This paper reviews the state of the art of fieldprogrammable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic
PID control system analysis, design, and technology
Designing and tuning a proportional-integral-derivative
(PID) controller appears to be conceptually intuitive, but can
be hard in practice, if multiple (and often conflicting) objectives
such as short transient and high stability are to be achieved.
Usually, initial designs obtained by all means need to be adjusted
repeatedly through computer simulations until the closed-loop
system performs or compromises as desired. This stimulates
the development of "intelligent" tools that can assist engineers
to achieve the best overall PID control for the entire operating
envelope. This development has further led to the incorporation
of some advanced tuning algorithms into PID hardware modules.
Corresponding to these developments, this paper presents a
modern overview of functionalities and tuning methods in patents,
software packages and commercial hardware modules. It is seen
that many PID variants have been developed in order to improve
transient performance, but standardising and modularising PID
control are desired, although challenging. The inclusion of system
identification and "intelligent" techniques in software based PID
systems helps automate the entire design and tuning process to
a useful degree. This should also assist future development of
"plug-and-play" PID controllers that are widely applicable and
can be set up easily and operate optimally for enhanced productivity,
improved quality and reduced maintenance requirements
- …