An actively adaptive control policy for linear models

Dust levels around the Tema industrial area of the Greater Accra Region have seen no reduction in recent years. Even though at some periods in time a natural drop in dust pollution levels is assured, the overall variation characteristics of the concentration of PM2.5, PM10, and Total Suspended Particles (TSP) have not been studied in recent years. This paper examines the levels of dust pollution across four (4) locations within the Tema metropolitan area with a specific interest in selecting locations and periods (weeks) significantly affected by dust pollution within the study area. Data collection was done over a nine-month period using the Casella 712 Microdust Pro Kit equipment. Measurements were done day and night at sampling points about 100 m apart in a given location. Monitoring was conducted once a week during the day and at night with a sampling period of 24 h per location, for thirty-six weeks. The generalized linear models were explored in selecting locations and weeks significantly affected by dust pollution. The study results showed no significant difference between pollution levels across the four selected locations. Eight, eleven, and five weeks out of the 36 weeks recorded significantly high concentrations of PM2.5, PM10, and TSP respectively. In addition, two out of the selected four areas (the oil jetty area and the VALCO hospital area) were found to have significantly high concentrations of dust pollution. The study recommends that an urgent air quality control policy intervention be put in place to control the highly alarming levels of dust pollution concentrations to guarantee and protect human health within the study area and beyond.

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Complementary Meta-Reinforcement Learning for Fault-Adaptive Control

Annual Conference of the PHM Society ◽

10.36001/phmconf.2020.v12i1.1289 ◽

2020 ◽

Vol 12 (1) ◽

pp. 8

Author(s):

Ibrahim Ahmed ◽

Marcos Quiñones-Grueiro ◽

Gautam Biswas

Keyword(s):

Adaptive Control ◽

Reinforcement Learning ◽

Fault Tolerant ◽

Control Policy ◽

Time Constraints ◽

Learning Approach ◽

Fuel Tanks ◽

Meta Learning ◽

New Policies ◽

Abrupt Faults

Faults are endemic to all systems. Adaptive fault-tolerant control accepts degraded performance under faults in exchange for continued operation. In systems with abrupt faults and strict time constraints, it is imperative for control to adapt fast to system changes. We present a meta-reinforcement learning approach that quickly adapts control policy. The approach builds upon model-agnostic meta learning (MAML). The controller maintains a complement of prior policies learned under system faults. This ``library" is evaluated on a system after a new fault to initialize the new policy. This contrasts with MAML where the controller samples new policies from a distribution of similar systems at each update step to achieve the new policy. Our approach improves sample efficiency of the reinforcement learning process. We evaluate this on a model of fuel tanks under abrupt faults.

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A Stochastic Approximation Algorithm for the Adaptive Control of Time Series Following Generalized Linear Models

Journal of Time Series Analysis ◽

10.1111/1467-9892.00138 ◽

1999 ◽

Vol 20 (3) ◽

pp. 289-308

Author(s):

Konstantinos Fokianos ◽

Benjamin Kedem

Keyword(s):

Time Series ◽

Adaptive Control ◽

Approximation Algorithm ◽

Stochastic Approximation ◽

Generalized Linear Models ◽

Linear Models ◽

Stochastic Approximation Algorithm

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Actor Critic Neural Network-Based Adaptive Control for MEMS Gyroscopes Suffering from Multiresource Disturbances

Mathematical Problems in Engineering ◽

10.1155/2021/6663946 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Chao Jing ◽

Gangzhu Qiao

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Control Method ◽

Control Policy ◽

Dynamic Surface Control ◽

Order System ◽

Dynamic Surface ◽

Control Approach ◽

Mems Gyroscopes ◽

Critic Neural Network

In this paper, an actor critic neural network-based adaptive control scheme for micro-electro-mechanical system (MEMS) gyroscopes suffering from multiresource disturbances is proposed. Faced with multiresource interferences consisting of parametric uncertainties, strong couplings between axes, Coriolis forces, and variable external disturbances, an actor critic neural network is introduced, where the actor neural network is employed to estimate the packaged disturbances and the critic neural network is utilized to supervise the system performance. Hence, strong robustness against uncertainties and better tracking properties can be derived for MEMS gyroscopes. Aiming at handling the nonlinearities inherent in gyroscopes without analytically differentiating the virtual control signals, dynamic surface control (DSC) rather than backstepping control method is employed to divide the 2nd order system into two 1st order systems and design the actual control policy. Moreover, theoretical analyses along with simulation experiments are conducted with a view to validate the effectiveness of the proposed control approach.

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On Performance Losses in Some Adaptive Control Systems: I

Journal of Basic Engineering ◽

10.1115/1.3650534 ◽

1965 ◽

Vol 87 (1) ◽

pp. 90-94 ◽

Cited By ~ 6

Author(s):

Masanao Aoki

Keyword(s):

Adaptive Control ◽

Control Systems ◽

Unknown Parameter ◽

A Priori ◽

Control Policy ◽

Optimal Estimation ◽

Perfect Information ◽

Point Of View ◽

Unknown Parameters ◽

Linear Differential

In controlling dynamic systems with unknown parameters and/or systems operating in unknown environment, the systems suffer due to the unknowness of pertinent parameter values, compared with situations with perfect information where all pertinent information is available to control systems optimally. The paper defines the concept of loss of performance to represent the loss in performance of some adaptive control situations compared with perfect information situations and defines the optimal control problems as the one where the loss of performance is minimized. This concept is illustrated for a control system governed by a scalar linear differential equation with unknown gain. The minimax control policy is defined as the control policy which minimized the maximum possible loss in performance where no a priori knowledge on the unknown parameter is available. It also discusses the optimal estimation problem of the unknown parameter from the point of view of loss of performance.

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