Michael G. Forbes

818 total citations
32 papers, 585 citations indexed

About

Michael G. Forbes is a scholar working on Control and Systems Engineering, Statistics, Probability and Uncertainty and Statistical and Nonlinear Physics. According to data from OpenAlex, Michael G. Forbes has authored 32 papers receiving a total of 585 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Control and Systems Engineering, 4 papers in Statistics, Probability and Uncertainty and 3 papers in Statistical and Nonlinear Physics. Recurrent topics in Michael G. Forbes's work include Fault Detection and Control Systems (27 papers), Advanced Control Systems Optimization (25 papers) and Control Systems and Identification (19 papers). Michael G. Forbes is often cited by papers focused on Fault Detection and Control Systems (27 papers), Advanced Control Systems Optimization (25 papers) and Control Systems and Identification (19 papers). Michael G. Forbes collaborates with scholars based in Canada, United States and China. Michael G. Forbes's co-authors include R. Bhushan Gopaluni, Rohit S. Patwardhan, J. Fraser Forbes, Martin Guay, Johan U. Backström, Philip D. Loewen, Nathan P. Lawrence, Zhihuan Song, Kai Wang and Junghui Chen and has published in prestigious journals such as Automatica, IEEE Access and Industrial & Engineering Chemistry Research.

In The Last Decade

Michael G. Forbes

31 papers receiving 562 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael G. Forbes Canada	10	473	76	74	65	39	32	585
Qiugang Lu United States	15	538 1.1×	105 1.4×	69 0.9×	66 1.0×	33 0.8×	45	694
Alejandro Rodríguez-Molina Mexico	12	214 0.5×	54 0.7×	140 1.9×	64 1.0×	13 0.3×	40	431
Bijan Moaveni Iran	12	313 0.7×	119 1.6×	39 0.5×	125 1.9×	21 0.5×	65	540
Junyou Shi China	16	300 0.6×	63 0.8×	93 1.3×	226 3.5×	46 1.2×	66	560
Khandaker Noman China	16	446 0.9×	224 2.9×	81 1.1×	51 0.8×	11 0.3×	52	590
Meng Rao Canada	10	319 0.7×	251 3.3×	64 0.9×	31 0.5×	15 0.4×	24	504
Willy Wojsznis United States	13	333 0.7×	89 1.2×	40 0.5×	47 0.7×	51 1.3×	27	439
Juraj Kabzan Switzerland	2	461 1.0×	58 0.8×	114 1.5×	24 0.4×	12 0.3×	2	616

Countries citing papers authored by Michael G. Forbes

Since Specialization

Citations

This map shows the geographic impact of Michael G. Forbes's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael G. Forbes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael G. Forbes more than expected).

Fields of papers citing papers by Michael G. Forbes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael G. Forbes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael G. Forbes. The network helps show where Michael G. Forbes may publish in the future.

Co-authorship network of co-authors of Michael G. Forbes

This figure shows the co-authorship network connecting the top 25 collaborators of Michael G. Forbes. A scholar is included among the top collaborators of Michael G. Forbes based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael G. Forbes. Michael G. Forbes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Lawrence, Nathan P., Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni, & Ali Mesbah. (2025). A view on learning robust goal-conditioned value functions: Interplay between RL and MPC. Annual Reviews in Control. 60. 101027–101027.

Lawrence, Nathan P., Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, & R. Bhushan Gopaluni. (2024). Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior. Automatica. 164. 111642–111642. 2 indexed citations

Lawrence, Nathan P., Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, & R. Bhushan Gopaluni. (2023). A modular framework for stabilizing deep reinforcement learning control. IFAC-PapersOnLine. 56(2). 8006–8011. 1 indexed citations

Lawrence, Nathan P., et al.. (2022). Meta-reinforcement learning for the tuning of PI controllers: An offline approach. Journal of Process Control. 118. 139–152. 27 indexed citations

Lawrence, Nathan P., et al.. (2022). Deep reinforcement learning with shallow controllers: An experimental application to PID tuning. Control Engineering Practice. 121. 105046–105046. 73 indexed citations

Lu, Qiugang, Michael G. Forbes, Philip D. Loewen, et al.. (2021). Model-plant mismatch detection for cross-directional processes. ISA Transactions. 117. 150–159. 1 indexed citations

Lawrence, Nathan P., Philip D. Loewen, Michael G. Forbes, Johan U. Backström, & R. Bhushan Gopaluni. (2020). Almost Surely Stable Deep Dynamics. Figshare. 33. 18942–18953. 2 indexed citations

Wang, Kai, Michael G. Forbes, R. Bhushan Gopaluni, Junghui Chen, & Zhihuan Song. (2019). Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes. IEEE Access. 7. 22554–22565. 50 indexed citations

Lu, Qiugang, Michael G. Forbes, Philip D. Loewen, et al.. (2019). Support vector machine approach for model-plant mismatch detection. Computers & Chemical Engineering. 133. 106660–106660. 8 indexed citations

10.

Shi, Dawei, et al.. (2016). Robust tuning for machine-directional predictive control of MIMO paper-making processes. Control Engineering Practice. 55. 1–12. 12 indexed citations

11.

Lu, Qiugang, R. Bhushan Gopaluni, Philip D. Loewen, et al.. (2015). Detecting model-plant mismatch without external excitation. 4976–4981. 2 indexed citations

12.

Shi, Dawei, et al.. (2015). User Friendly Robust MPC Tuning of Uncertain Paper-Making Processes∗∗This work was supported by an NSERC CRD project with Honeywell Vancouver as the industrial partner.. IFAC-PapersOnLine. 48(8). 1021–1026. 8 indexed citations

13.

Lu, Qiugang, R. Bhushan Gopaluni, Michael G. Forbes, et al.. (2015). Cross-directional controller performance monitoring for paper machines. 145. 4970–4975. 4 indexed citations

14.

Forbes, Michael G., et al.. (2015). Model Predictive Control in Industry: Challenges and Opportunities. IFAC-PapersOnLine. 48(8). 531–538. 201 indexed citations

15.

Forbes, Michael G., et al.. (2015). Moving-Horizon Predictive Input Design for Closed-Loop Identification. IFAC-PapersOnLine. 48(8). 135–140. 3 indexed citations

16.

Forbes, Michael G., et al.. (2014). Sensitivity of MIMO controller performance to model-plant mismatch, with applications to paper machine control. 96. 204–209. 3 indexed citations

17.

Forbes, Michael G., Martin Guay, & J. Fraser Forbes. (2005). Probabilistic control design for continuous-time stochastic nonlinear systems: a PDF-shaping approach. 32. 132–136. 8 indexed citations

18.

Forbes, Michael G., J. Fraser Forbes, & Martin Guay. (2005). Control design to shape the stationary probability density function. Transactions of the Institute of Measurement and Control. 27(5). 331–346. 4 indexed citations

19.

Forbes, Michael G., J. Fraser Forbes, & Martin Guay. (2004). Regulatory control design for stochastic processes: shaping the probability density function. 5. 3998–4003. 24 indexed citations

20.

Forbes, Michael G., Martin Guay, & J. Fraser Forbes. (2002). PDF-SHAPING CONTROL DESIGN. IFAC Proceedings Volumes. 35(1). 349–354. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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