Combined discrete network--Continuous control modelling of operator behavior

A speed tracking control method for induction machine is shown in this paper. The method consists of outer speed control loop and inner current control loop. Model predictive current control method without the need for calculation of the weighing factors is utilized for the inner control loop, which generates a continuous set of voltage reference values that can be modulated and applied by the inverter to the induction machine. Interesting parallels are drawn between the developed method and state feedback principles that helped with the analysis of the stability and controllability. Simple speed and rotor flux estimator is implemented that helps achieve sensorless control. Simulation is conducted and the method shows great performance for speed tracking in a steady state, and during transients as well. Additionally, compared to the finite control set predictive current control, it shows less harmonic content in the generated torque on the rotor shaft.

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A robust composite wide area control of a DFIG wind energy system for damping inter-area oscillations

Protection and Control of Modern Power Systems ◽

10.1186/s41601-020-00170-y ◽

2020 ◽

Vol 5 (1) ◽

Author(s):

Ayyarao S. L. V. Tummala

Keyword(s):

Wind Energy ◽

Sliding Mode ◽

Energy System ◽

Wide Area ◽

Continuous Control ◽

Energy Integration ◽

Wind Energy System ◽

Wind Energy Integration ◽

Twisting Algorithm ◽

Wide Area Control

AbstractThis paper presents a novel composite wide area control of a DFIG wind energy system which combines the Robust Exact Differentiator (RED) and Discontinuous Integral (DI) control to damp out inter-area oscillations. RED generates the real-time differentiation of a relative speed signal in a noisy environment while DI control, an extension to a twisting algorithm and PID control, develops a continuous control signal and hence reduces chattering. The proposed control is robust to disturbances and can enhance the overall stability of the system. The proposed composite sliding mode control is evaluated using a modified benchmark two-area power system model with wind energy integration. Simulation results under various operating scenarios show the efficacy of the proposed approach.

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Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning

IEEE Robotics and Automation Letters ◽

10.1109/lra.2021.3060403 ◽

2021 ◽

pp. 1-1

Author(s):

Sammy Christen ◽

Lukas Jendele ◽

Emre Aksan ◽

Otmar Hilliges

Keyword(s):

Path Planning ◽

Continuous Control

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Laser beam calibration for wood surface colour treatment

European Journal of Wood and Wood Products ◽

10.1007/s00107-021-01704-3 ◽

2021 ◽

Author(s):

M. Jurek ◽

R. Wagnerová

Keyword(s):

Laser Beam ◽

Power Control ◽

Wood Surface ◽

Laser Power ◽

Production Quality ◽

Chemical Processes ◽

Continuous Control ◽

Calibration System ◽

Laser Engraving ◽

Wood Surfaces

AbstractLaser engraving of photographs on wood surfaces is a challenging task. To optimize the outcome and production quality it is necessary to control every aspect of the laser engraving process. Most of the production machines and technologies overall are mainly focused on laser power control. However, with other systems and deeper knowledge of the wood characteristics it is possible to achieve even better quality. This paper deals with enlarging the number of achievable shades of burned wood and its optimization. A calibration system was developed to control colour shades of engraved wood with a combination of laser power and optic focus. With this approach it is possible to widen achievable palette of engraved shades by continuous control of chemical processes of laser and wood interaction. The production is divided into wood burning and wood carbonization by variation of laser beam focus.

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The Impact of Transparency and Decision Risk on Human–Automation Teaming Outcomes

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/00187208211033445 ◽

2021 ◽

pp. 001872082110334

Author(s):

Shayne Loft ◽

Adella Bhaskara ◽

Brittany A. Lock ◽

Michael Skinner ◽

James Brooks ◽

...

Keyword(s):

Decision Aid ◽

Financial Incentive ◽

Decision Aids ◽

Rejection Rate ◽

Decision Time ◽

Correct Rejection ◽

Incentive Scheme ◽

Operator Behavior ◽

The Impact ◽

Financial Penalty

Objective Examine the effects of decision risk and automation transparency on the accuracy and timeliness of operator decisions, automation verification rates, and subjective workload. Background Decision aids typically benefit performance, but can provide incorrect advice due to contextual factors, creating the potential for automation disuse or misuse. Decision aids can reduce an operator’s manual problem evaluation, and it can also be strategic for operators to minimize verifying automated advice in order to manage workload. Method Participants assigned the optimal unmanned vehicle to complete missions. A decision aid provided advice but was not always reliable. Two levels of decision aid transparency were manipulated between participants. The risk associated with each decision was manipulated using a financial incentive scheme. Participants could use a calculator to verify automated advice; however, this resulted in a financial penalty. Results For high- compared with low-risk decisions, participants were more likely to reject incorrect automated advice and were more likely to verify automation and reported higher workload. Increased transparency did not lead to more accurate decisions and did not impact workload but decreased automation verification and eliminated the increased decision time associated with high decision risk. Conclusion Increased automation transparency was beneficial in that it decreased automation verification and decreased decision time. The increased workload and automation verification for high-risk missions is not necessarily problematic given the improved automation correct rejection rate. Application The findings have potential application to the design of interfaces to improve human–automation teaming, and for anticipating the impact of decision risk on operator behavior.

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Fully distributed actor-critic architecture for multitask deep reinforcement learning

The Knowledge Engineering Review ◽

10.1017/s0269888921000023 ◽

2021 ◽

Vol 36 ◽

Author(s):

Sergio Valcarcel Macua ◽

Ian Davies ◽

Aleksi Tukiainen ◽

Enrique Munoz de Cote

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Duality Theory ◽

Deep Neural Network ◽

Original Problem ◽

Almost Sure Convergence ◽

Continuous Control ◽

Access Data ◽

Central Station ◽

Common Policy

Abstract We propose a fully distributed actor-critic architecture, named diffusion-distributed-actor-critic Diff-DAC, with application to multitask reinforcement learning (MRL). During the learning process, agents communicate their value and policy parameters to their neighbours, diffusing the information across a network of agents with no need for a central station. Each agent can only access data from its local task, but aims to learn a common policy that performs well for the whole set of tasks. The architecture is scalable, since the computational and communication cost per agent depends on the number of neighbours rather than the overall number of agents. We derive Diff-DAC from duality theory and provide novel insights into the actor-critic framework, showing that it is actually an instance of the dual-ascent method. We prove almost sure convergence of Diff-DAC to a common policy under general assumptions that hold even for deep neural network approximations. For more restrictive assumptions, we also prove that this common policy is a stationary point of an approximation of the original problem. Numerical results on multitask extensions of common continuous control benchmarks demonstrate that Diff-DAC stabilises learning and has a regularising effect that induces higher performance and better generalisation properties than previous architectures.

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