Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes von Wang, Kai, Forbes, Michael G., Gopaluni, Bhushan, Chen, Junghui, Song, Zhihuan

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Support vector machine approach for model-plant mismatch detection von Lu, Qiugang, Forbes, Michael G., Loewen, Philip D., Backström, Johan U., Dumont, Guy A., Gopaluni, R. Bhushan

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Machine Direction Adaptive Control on a Paper Machine von Rippon, Lee D, Lu, Qiugang, Forbes, Michael G, Gopaluni, R. Bhushan, Loewen, Philip D, Backström, Johan U

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Model-plant mismatch detection for cross-directional processes von Lu, Qiugang, Forbes, Michael G., Loewen, Philip D., Backström, Johan U., Dumont, Guy A., Gopaluni, R. Bhushan

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Deep reinforcement learning with shallow controllers: An experimental application to PID tuning von Lawrence, Nathan P., Forbes, Michael G., Loewen, Philip D., McClement, Daniel G., Backström, Johan U., Gopaluni, R. Bhushan

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Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior von Lawrence, Nathan P., Loewen, Philip D., Wang, Shuyuan, Forbes, Michael G., Gopaluni, R. Bhushan

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Meta-reinforcement learning for the tuning of PI controllers: An offline approach von McClement, Daniel G., Lawrence, Nathan P., Backström, Johan U., Loewen, Philip D., Forbes, Michael G., Gopaluni, R. Bhushan

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Controller Design for Discrete-Time Stochastic Processes With Nonquadratic Loss von Forbes, Michael G, Forbes, J. Fraser, Guay, Martin

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Identification of symmetric noncausal processes von Lu, Qiugang, Loewen, Philip D., Bhushan Gopaluni, R., Forbes, Michael G., Backström, Johan U., Dumont, Guy A., Davies, Michael S.

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Performance Assessment of Cross-Directional Control for Paper Machines von Qiugang Lu, Forbes, Michael G., Gopaluni, Ratna Bhushan, Loewen, Philip D., Backstrom, Johan U., Dumont, Guy A.

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Model Predictive Control in Industry: Challenges and Opportunities von Forbes, Michael G., Patwardhan, Rohit S., Hamadah, Hamza, Gopaluni, R. Bhushan

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Reinforcement Learning with Partial Parametric Model Knowledge von Wang, Shuyuan, Loewen, Philip D., Lawrence, Nathan P., Forbes, Michael G., Gopaluni, R. Bhushan

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A modular framework for stabilizing deep reinforcement learning control von Lawrence, Nathan P., Loewen, Philip D., Wang, Shuyuan, Forbes, Michael G., Gopaluni, R. Bhushan

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Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem von Lawrence, Nathan P., Stewart, Gregory E., Loewen, Philip D., Forbes, Michael G., Backstrom, Johan U., Gopaluni, R. Bhushan

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Deep Hankel matrices with random elements von Lawrence, Nathan P, Loewen, Philip D, Wang, Shuyuan, Forbes, Michael G, Gopaluni, R. Bhushan

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Guiding Reinforcement Learning with Incomplete System Dynamics von Wang, Shuyuan, Duan, Jingliang, Lawrence, Nathan P, Loewen, Philip D, Forbes, Michael G, Gopaluni, R. Bhushan, Zhang, Lixian

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Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior von Lawrence, Nathan P, Loewen, Philip D, Wang, Shuyuan, bes, Michael G, Gopaluni, R Bhushan

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Reinforcement Learning based Design of Linear Fixed Structure Controllers von Lawrence, Nathan P., Stewart, Gregory E., Loewen, Philip D., Forbes, Michael G., Backstrom, Johan U., Gopaluni, R. Bhushan

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A Meta-Reinforcement Learning Approach to Process Control von McClement, Daniel G., Lawrence, Nathan P., Loewen, Philip D., Forbes, Michael G., Backström, Johan U., Gopaluni, R. Bhushan

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Reinforcement Learning with Partial Parametric Model Knowledge von Wang, Shuyuan, Loewen, Philip D, Lawrence, Nathan P, bes, Michael G, Gopaluni, R Bhushan

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