Predictive reinforcement learning based adaptive PID controller

Chaoqun Ma; Zhiyong Zhang

REVIEW 4 major objections 4 minor 31 references

Predictive reinforcement learning based adaptive PID controller

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that an adaptive PID controller trained with predicted future rewards stabilizes open-loop unstable, nonlinear, and coupled systems more accurately than comparable RL-tuned PID controllers.

desk verdict A sensible engineering recipe for RL-tuned PID with a model-based lookahead reward, but the experiments hand the method the exact plant model and compare it to model-free baselines, so the headline gains are partly an oracle effect. read the letter →

arxiv 2506.08509 v1 pith:RU4UIM6R submitted 2025-06-10 eess.SY cs.SY

Chaoqun Ma , Zhiyong Zhang This is my paper

classification eess.SYcs.SY

keywords predictivereinforcementlearningadaptivePIDcontrollerrewardforecastactionsmoothstrategyhierarchicalPPOunstablenonlinearsystemsquadrotorattitudecontrol

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes an adaptive PID controller whose three gains are tuned online by a reinforcement-learning agent, and argues that tuning is more efficient and more stable when the agent is allowed to look ahead. The look-ahead is a reward forecast strategy: using a known model of the plant, the controller simulates the next $N$ steps from the current state with the current PID gains, computes the average reward over that horizon, and trains its policy on the predicted reward instead of the single-step reward. Two supporting mechanisms stabilize the learned behavior: a hierarchical reward function that penalizes error growth and rewards fine steady-state accuracy, and an action smooth strategy that executes a weighted average of recent actions rather than the raw output of the policy. Across linear unstable, time-varying, nonlinear two-tank, and quadrotor-attitude simulations, the paper reports that PRL-PID tracks setpoints with smaller overshoot, shorter settling time, and stronger disturbance rejection than PPO-, TD3-, and SAC-tuned PID baselines. The stated value is a dual-driven design that keeps the flexibility of data-driven RL while using model priors to make learning converge faster and more reliably.

What carries the argument

The load-bearing mechanism is the reward forecast strategy. For a discretized plant model $x(k+1)=f(x(k),u(k))$, the agent predicts $N$ future states from the current state using the current PID gains, computes the reward $r_{k+i}$ along that predicted trajectory with the hierarchical reward function, and uses the averaged value $r_k^{\mathrm{pred}}$ in place of the single-step reward inside the PPO advantage estimate. This is what makes the controller dual-driven: the policy network still learns from interaction data, but every update is informed by model-based foresight. Two auxiliary mechanisms carry the robustness claims: the action smooth strategy executes a weighted average of the last $M$ raw actions, $\hat{a}(t)=\sum_{i=t-M+1}^{t}w_{t,i}a(i)$, which suppresses overshoot and oscillation in systems with delay and inertia, and the hierarchical reward function, whose steady-state accuracy term $r_{\mathrm{add}}=0.1/(\|e\|+0.05)$ activates when $\|e\|<0.05$, provides stage-appropriate learning signal. In the UAV experiment the same idea is extended to a cross-axis coupled PID, where the coupling coefficients between roll, pitch, and yaw error channels are themselves tuned online by the RL agent.

What would settle it

Train PRL-PID and a plain PPO-PID baseline on a plant whose true dynamics differ from the prediction model — for example, add 20% parameter drift or an unmodeled friction term to the two-tank equations — and compare tracking error and settling time. If PRL-PID no longer consistently beats the baseline under that mismatch, the benefit depends on perfect model knowledge rather than on the forecasting mechanism itself.

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Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that replacing the immediate reward in PPO-based PID tuning with an $N$-step-ahead predicted reward improves both training and control: under the same number of training iterations, the agent's reward grows faster and reaches higher levels, and the resulting controller produces smaller overshoot and shorter settling time, with $N=3$ the best horizon among the values tested. The prediction is computed by iterating a discretized plant model $\hat{x}(k+i+1)=f(\hat{x}(k+i),\hat{u}(k+i))$ while holding the current PID gains fixed over the horizon, then averaging the rewards along that rollout, $r_k^{\mathrm{pred}}=\frac{1}{N}\sum_{i=0}^{N-1}r_{k+i}$. The paper also finds that executing a weighted blend of the last $M$ actions, rather than the immediate policy output, suppresses overshoot and oscillation, and that a hierarchical reward $-\mathrm{ReLU}(\Delta V_k)-\alpha\,\mathrm{clip}(V_k)-\beta\,\mathrm{clip}(|e_k|)+r_{\mathrm{add}}$ with a steady-state accuracy bonus $r_{\mathrm{add}}=0.1/(\|e\|+0.05)$ accelerates convergence and improves robustness. Taken together, the mechanisms allow an adaptive PID to stabilize first-order and second-order open-loop unstable plants, a time-varying plant, a nonlinear two-tank process, and a coupled quadrotor attitude model.

Load-bearing premise

The reward forecast strategy assumes an accurate plant model $f(x,u)$, and in every experiment the same discretized simulation model is used both as the plant and as the predictor, so the claimed advantage over model-free RL is untested when the model is inaccurate.

Editorial extensions

If this is right

Combining reward forecast with action smooth lets the controller track a time-varying first-order unstable plant accurately even for reference values beyond the training range, so the method generalizes outside its training distribution.
In the two-tank nonlinear system, adding reward forecast alone cuts ISE and IAE relative to plain PPO-PID, and adding action smooth on top cuts them further, indicating the two mechanisms contribute additively.
On the quadrotor attitude model, a cross-axis coupled PID whose coupling coefficients are tuned by the same RL agent tracks roll, pitch, and yaw setpoints, so the approach extends to MIMO control with coupled channels.
The horizon study showing $N=3$ as the best prediction depth implies there is a sweet spot for foresight length that practitioners should tune per plant.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because every experiment uses the exact discretized simulation model as the predictor, PRL-PID is best read as model-based RL with a PID policy parameterization; a natural testable extension is replacing $f$ with a learned dynamics model and measuring how much of the advantage survives model error.
The reward forecast is essentially a short-horizon MPC-style rollout injected into a policy-gradient update, so the same trick could give MPC-like foresight to other RL controllers without solving an online optimization problem.
The action smooth strategy is a low-pass filter on the policy output, which suggests it could transfer to any RL controller acting on physical plants with delay, not only PID-tuned ones.
Under large model mismatch the forecasted rewards would be miscalibrated and the claimed advantage could shrink or reverse; the paper's experiments do not currently test that regime.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick read for you on arXiv:2506.08509. It's a simulation study combining PPO-based PID tuning with a model-based reward forecast and action smoothing. The headline: the paper shows that adding a multi-step lookahead reward computed from the plant model improves tracking and reduces overshoot relative to vanilla PPO-PID, and the combo of forecast plus smoothing is consistently the best. That result is plausible and reasonably demonstrated.

What's actually new: the specific recipe – using the known discretized dynamics to compute an N-step average reward inside PPO, plus a weighted moving average on the output PID gains. None of these parts are new in isolation, but the particular combination and the experiments on unstable, time-varying, and coupled nonlinear plants are a legitimate extension. The ablations separating RF and AS are the most useful part of the paper; they show each component earns its keep.

The main weakness is the oracle-model setup. Every experiment gives PRL-PID the exact same discretized model used as the plant. The baselines (SAC-PID, TD3-PID, PPO-PID) are model-free and never see this privileged information, so the headline comparisons in Section 4.1 and Table 2 are not apples-to-apples. The paper's 'robustness and adaptability' claims are only tested within that one perfect model; there is no model-mismatch experiment, so we don't know how the method degrades when the forecast model is wrong. This is a real gap, but it does not sink the paper: the ablation PPO vs PPO+RF shows the forecast itself helps even within the same model, so the core idea stands. The other soft spots are standard for this genre – no seeds, no error bars, no PPO hyperparameters, and the figures are hard to read in the text I have. Reproducibility is weak.

There is also a minor typo in Eq. (20) denominator limit, and some grammatical slips in the prose. Not important.

Who is it for: people working on RL-based PID auto-tuning in industrial process control might find the recipe useful. It does not change the theoretical landscape. It deserves peer review as a modest engineering contribution, but the authors need to add model-mismatch tests, report variance over seeds, and soften the 'outperforming' language given the unfair baselines. I'd send it to review with a request for major revision.

Referee Report

4 major / 4 minor

Summary. The paper proposes PRL-PID, an adaptive PID controller whose PID gains are tuned online by a modified PPO agent. The modification consists of three ingredients: a hierarchical reward function (Eq. 10), a reward forecast strategy that uses a known discretized plant model to predict N-step-ahead rewards (Eqs. 12-14), and an action-smoothing strategy that mixes historical actions (Eq. 15). The proposed controller is tested in simulation on a first-order unstable system, a second-order unstable system, a time-varying first-order system, a two-tank nonlinear process, and a simplified quadrotor attitude model, with comparisons against PPO-, SAC-, and TD3-tuned PID controllers. The paper reports that PRL-PID reduces overshoot, shortens settling time, improves ISE/IAE, and generalizes beyond the training range.

Significance. The core idea--combining a model-based predictive rollout with RL-based PID tuning--is reasonable and practically motivated, and the action-smoothing and hierarchical reward components are plausible engineering contributions. If the claimed advantages were rigorously demonstrated, the paper could be of interest to the process-control and RL-for-control communities. However, the current experimental evidence is not sufficient to support the broad robustness and superiority claims, because the reward forecast uses the exact plant model as an oracle and because the results are reported without statistical rigor or visible figures. The study is best viewed as a promising design study that needs substantially stronger validation before its central claims can be accepted.

major comments (4)

[Section 3.2 and Section 4] The reward forecast in Eq. (12) uses the same discretized model that serves as the plant in all simulations: Section 4 states 'the known dynamic properties of each system were discretized' and the predictor in Eq. (12) is exactly f(x,u). PRL-PID therefore receives perfect model knowledge, while the PPO-PID, SAC-PID, and TD3-PID baselines are model-free. The consistently lower ISE/IAE in Table 2 and the claims of 'excellent robustness and adaptability across diverse operating conditions' (Abstract, Section 5) may be artifacts of this privileged oracle rather than of the predictive-learning framework. To support the central claim, the authors should add experiments with a deliberately mismatched or identified prediction model, or compare against a model-based controller (e.g., an MPC or a model-based RL method) that receives the same accurate model information.
[Section 4 (all experiments)] No statistical rigor is reported: there are no random seeds, no number of independent runs, no standard deviations, no error bars, and no confidence intervals for any metric. PPO is stochastic, so a single training run cannot establish that PRL-PID outperforms the baselines. The authors should report means and standard deviations over at least 5-10 seeds for ISE/IAE, overshoot, settling time, and training reward, and should state the PPO hyperparameters (learning rate, clip ratio, GAE lambda, discount factor, minibatch size) and the exact state vector used in each experiment.
[Figures 3, 5, 6, 7, 9, and 10] The figures that support the main performance claims--such as smaller overshoot, shorter settling time, and lower oscillations--are referenced in the text but do not appear in the submitted manuscript; only their captions remain. Without the actual response curves, the qualitative claims in Sections 4.1-4.6 cannot be verified. The figures must be included and, if possible, accompanied by numeric summaries of overshoot and settling time.
[Eqs. (13)-(14)] The forecasted reward r_k^pred is computed by rolling out the model with a fixed set of PID parameters over the N-step horizon (Eq. 13), whereas the executed policy updates the PID parameters at every step. This is a policy mismatch between the forecast and the actual closed-loop behavior; the paper does not justify why the fixed-gain rollout remains a useful learning signal, nor does it analyze the effect of N beyond a qualitative statement that N=3 is best (Section 4.2). A quantitative ablation of N with training curves and variance would be needed to substantiate the reward-forecast contribution.

minor comments (4)

[Eq. (10)] The variable r_k is used both as the reward (e.g., in Eq. 4) and as the reference signal in e_k = r_k - y_k, which is confusing. Please use a distinct symbol for the reference, such as y_ref or r_sp. Also, the clip(·) function is never defined; please specify the clipping bounds used for V_k and |e_k|.
[Eq. (15)] The indexing in the action-smoothing formula is unclear: the sum runs over i = t-M+1 to t, but the text says a(i) is the immediate action generated at time t-i. Please rewrite the sum with a consistent time index so that the past M actions are clearly defined.
[Section 4.3, Eqs. (19)-(21)] The text refers to 'EMA 5' and 'LWMA 5' but Eq. (21) defines an exponential recursive average with alpha=0.3 and no window length, while Eq. (20) defines LWMA without specifying M=5 in the equation. Please make the naming and parameter values consistent between the text and equations.
[Section 4.4] The time-varying experiment trains on references in [0,2] and validates on [-0.3,2.5], but the state space is never defined, so it is unclear how the policy is expected to generalize. Please specify the state vector and, in particular, whether the reference signal is an input feature.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reward forecast is a model-based rollout, not a fitted quantity, and no load-bearing self-citation chain appears.

full rationale

The PRL-PID derivation is self-contained against its own stated inputs. The reward forecast strategy in Eqs. (12)-(14) recursively applies the known discretized plant model f(x,u) to generate predicted states and an average predicted reward; this is a model-based rollout (an MPC-style prior), not a fitted parameter subsequently renamed as a prediction. The policy is updated by the standard PPO objective (Eqs. 5-8) using that forecasted reward, so the central chain does not reduce to its own conclusion. The main weakness is experimental rather than circular: Section 4 states that the known dynamic properties of each system were discretized, and the same discretized model serves as both plant and prediction model, giving PRL-PID oracle model access that the model-free baselines lack; robustness to model mismatch is therefore untested. This is a benchmark validity concern, not a definitional equivalence. Similarly, selecting N=3 and smoothing parameters from the test systems is hyperparameter selection, not a fitted input called a prediction. The reference list contains no load-bearing self-citations, and no uniqueness theorem is imported from the authors' prior work. Accordingly, no circular step meets the quote-and-reduction bar.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The key external input is the known plant model used for forecasting; the paper tunes several hyperparameters (N, smoothing weights, reward coefficients) without reporting them or testing sensitivity.

free parameters (6)

alpha (reward weight for -clip(V_k)) = not reported
Appears in Eq (10); no value given in the text, yet it scales the Lyapunov-style penalty term.
beta (reward weight for -clip(|e_k|)) = not reported
Appears in Eq (10); no value given.
N (reward forecast horizon) = 3 (chosen as best)
Section 4.2 states N=3 offered the best performance, i.e. selected from data on the test system.
Threshold and coefficients in r_add = 0.05 threshold, 0.1/0.05 coefficients
Eq (11) fixes these constants without justification or sensitivity analysis.
Action smooth weights (M, alpha) = EMA with alpha=0.3, window 5
Eq (21) uses alpha=0.3; Section 4.3 compares SMA/LWMA/ERA and selects ERA as best, another data-driven selection.
PPO hyperparameters (learning rate, clip, gamma, lambda) = not reported
Table 1 only gives network layers; PPO settings are absent, so training cannot be replicated.

assumptions (3)

domain assumption The plant dynamics are exactly described by the discretized model f with sampling time T_s=0.1 s.
Section 4 states the known dynamic properties were discretized; the same model is used for the reward forecast, so any discretization error is ignored.
standard math The Markov property holds and PPO converges to a good policy with the chosen reward.
Section 2.1 relies on the MDP formulation; no convergence guarantees are provided.
ad hoc to paper Future control inputs during the N-step forecast are generated by fixed PID parameters (Eq 13).
Section 3.2 assumes the action stays constant over the horizon, a simplification that may bias the forecasted reward.

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Cite this review

Pith. "Pith review of Predictive reinforcement learning based adaptive PID controller." pith.science (2026). https://pith.science/paper/RU4UIM6R

@misc{pith2026250608509,
  author       = {Pith},
  title        = {Pith review of: Predictive reinforcement learning based adaptive PID controller},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RU4UIM6R}},
  note         = {Machine review of arXiv:2506.08509}
}

read the original abstract

Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines the advantages of both data-driven and model-driven approaches. Design/methodology/approach: A predictive reinforcement learning framework is introduced, incorporating action smooth strategy to suppress overshoot and oscillations, and a hierarchical reward function to support training. Findings: Experimental results show that the PRL-PID controller achieves superior stability and tracking accuracy in nonlinear, unstable, and strongly coupled systems, consistently outperforming existing RL-tuned PID methods while maintaining excellent robustness and adaptability across diverse operating conditions. Originality/Value: By adopting predictive learning, the proposed PRL-PID integrates system model priors into data-driven control, enhancing both the control framework's training efficiency and the controller's stability. As a result, PRL-PID provides a balanced blend of model-based and data-driven approaches, delivering robust, high-performance control.

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Reference graph

Works this paper leans on

31 extracted references · 29 canonical work pages

[1]

I., Vandika, A

Sappaile, B. I., Vandika, A. Y., Deiniatur, M., Nuridayanti, N., & Arifudin, O. (2024). The Role of Artificial Intelligence in the Development of Digital Era Educational Progress. Journal of Artificial Intelligence and Development, 3(1), 1-8

work page 2024
[2]

J., & Chiang, L

Qin, S. J., & Chiang, L. H. (2019). Advances and opportunities in machine learning for process data analytics. Computers & Chemical Engineering, 126, 465-473

work page 2019
[3]

(2022, June)

Tang, W., & Daoutidis, P. (2022, June). Data-driven control: Overview and perspectives. In 2022 American Control Conference (ACC) (pp. 1048-1064). IEEE

work page 2022
[4]

Jianhong, W., & Xiaoyong, G. (2023). Iterative learning data driven strategy for aircraft control system. Aircraft Engineering and Aerospace Technology, 95(10), 1588-1595

work page 2023
[5]

A., Tharayil, M., & Alleyne, A

Bristow, D. A., Tharayil, M., & Alleyne, A. G. (2006). A survey of iterative learning control. IEEE control systems magazine, 26(3), 96-114

work page 2006
[6]

K., & Srinivasan, K

Shuprajhaa, T., Sujit, S. K., & Srinivasan, K. (2022). Reinforcement learning based adaptive PID controller design for control of linear/nonlinear unstable processes. Applied Soft Computing, 128, 109450

work page 2022
[7]

& Lei, Y

Shi, H., Li, R., Bai, X., Zhang, Y., Min, L., Wang, D., ... & Lei, Y. (2023). A review for control theory and condition monitoring on construction robots. Journal of Field Robotics, 40(4), 934-954

work page 2023
[8]

Recht, B. (2019). A tour of reinforcement learning: The view from continuous control. Annual Review of Control, Robotics, and Autonomous Systems, 2(1), 253-279

work page 2019

Show all 31 references

[9]

Woo, J., Yu, C., & Kim, N. (2019). Deep reinforcement learning-based controller for path following of an unmanned surface vehicle. Ocean Engineering, 183, 155-166

2019
[10]

P., Nageshrao, S

Pane, Y. P., Nageshrao, S. P., Kober, J., & Babuška, R. (2019). Reinforcement learning based compensation methods for robot manipulators. Engineering Applications of Artificial Intelligence, 78, 236-247

2019
[11]

Annaswamy, A. M. (2023). Adaptive control and intersections with reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 6(1), 65-93

2023
[12]

Buşoniu, L., De Bruin, T., Tolić, D., Kober, J., & Palunko, I. (2018). Reinforcement learning for control: Performance, stability, and deep approximators. Annual Reviews in Control, 46, 8-28

2018
[13]

J., & Hä gglund, T

Åströ m, K. J., & Hä gglund, T. (2001). The future of PID control. Control engineering practice, 9(11), 1163-1175

2001
[14]

Astrom, K. J. (1995). PID controllers: theory, design, and tuning. The international society of measurement and control. 22

1995
[15]

Samad, T. (2017). A survey on industry impact and challenges thereof [technical activities]. IEEE Control Systems Magazine, 37(1), 17-18

2017
[16]

Guan, Z., & Yamamoto, T. (2021). Design of a reinforcement learning PID controller. IEEJ transactions on electrical and electronic engineering, 16(10), 1354- 1360

2021
[17]

S., Tan, S

Hwang, K. S., Tan, S. W., & Tsai, M. C. (2003). Reinforcement learning to adaptive control of nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 33(3), 514-521

2003
[18]

(2024, August)

Daoudi, P., Mavkov, B., Robu, B., Prieur, C., Witrant, E., Barlier, M., & Dos Santos, L. (2024, August). Improving a proportional integral controller with reinforcement learning on a throttle valve benchmark. In 2024 IEEE Conference on Control Technology and Applications (CCTA...

2024
[19]

P., Forbes, M

Lawrence, N. P., Forbes, M. G., Loewen, P. D., McClement, D. G., Backströ m, J. U., & Gopaluni, R. B. (2022). Deep reinforcement learning with shallow controllers: An experimental application to PID tuning. Control Engineering Practice, 121, 105046

2022
[20]

Muduli, R., Jena, D., & Moger, T. (2024). Application of reinforcement learning-based adaptive PID controller for automatic generation control of multi-area power system. IEEE Transactions on Automation Science and Engineering, 22, 1057-1068

2024
[21]

S., Huang, B.,

Dogru, O., Velswamy, K., Ibrahim, F., Wu, Y., Sundaramoorthy, A. S., Huang, B., ... & Bell, N. (2022). Reinforcement learning approach to autonomous PID tuning. Computers & Chemical Engineering, 161, 107760

2022
[22]

M., Xu, T., Lai, H., Chen, X

Luo, F. M., Xu, T., Lai, H., Chen, X. H., Zhang, W., & Yu, Y. (2024). A survey on model-based reinforcement learning. Science China Information Sciences, 67(2), 121101

2024
[23]

Mayne, D. Q. (1999). Model predictive control theory and design. Nob Hill Pub, Llc

1999
[24]

Grü ne, L., Pannek, J., Grü ne, L., & Pannek, J. (2017). Nonlinear model predictive control (pp. 45-69). Springer International Publishing

2017
[25]

Borrelli, F., Bemporad, A., & Morari, M. (2017). Predictive control for linear and hybrid systems. Cambridge University Press

2017
[26]

L., Novak, M.,

Harbi, I., Rodriguez, J., Liegmann, E., Makhamreh, H., Heldwein, M. L., Novak, M., ... & Kennel, R. (2023). Model-predictive control of multilevel inverters: Challenges, recent advances, and trends. IEEE Transactions on Power Electronics, 38(9), 10845-10868

2023
[27]

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. 23

2017 arXiv
[28]

(2018, July)

Fujimoto, S., Hoof, H., & Meger, D. (2018, July). Addressing function approximation error in actor-critic methods. In International conference on machine learning (pp. 1587- 1596). PMLR

2018
[29]

& Levine, S

Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., ... & Levine, S. (2018). Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905

2018 arXiv
[30]

Chidambaram, M. (1988). Periodic operation of isothermal plug-flow reactors for autocatalytic reactions. Chemical engineering communications, 69(1), 215-224

1988
[31]

Cai, Y., Yang, Y., Huang, T., & Li, B. (2025). Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network. Advanced Intelligent Systems, 2400427

2025

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

I., Vandika, A

Sappaile, B. I., Vandika, A. Y., Deiniatur, M., Nuridayanti, N., & Arifudin, O. (2024). The Role of Artificial Intelligence in the Development of Digital Era Educational Progress. Journal of Artificial Intelligence and Development, 3(1), 1-8

work page 2024

[2] [2]

J., & Chiang, L

Qin, S. J., & Chiang, L. H. (2019). Advances and opportunities in machine learning for process data analytics. Computers & Chemical Engineering, 126, 465-473

work page 2019

[3] [3]

(2022, June)

Tang, W., & Daoutidis, P. (2022, June). Data-driven control: Overview and perspectives. In 2022 American Control Conference (ACC) (pp. 1048-1064). IEEE

work page 2022

[4] [4]

Jianhong, W., & Xiaoyong, G. (2023). Iterative learning data driven strategy for aircraft control system. Aircraft Engineering and Aerospace Technology, 95(10), 1588-1595

work page 2023

[5] [5]

A., Tharayil, M., & Alleyne, A

Bristow, D. A., Tharayil, M., & Alleyne, A. G. (2006). A survey of iterative learning control. IEEE control systems magazine, 26(3), 96-114

work page 2006

[6] [6]

K., & Srinivasan, K

Shuprajhaa, T., Sujit, S. K., & Srinivasan, K. (2022). Reinforcement learning based adaptive PID controller design for control of linear/nonlinear unstable processes. Applied Soft Computing, 128, 109450

work page 2022

[7] [7]

& Lei, Y

Shi, H., Li, R., Bai, X., Zhang, Y., Min, L., Wang, D., ... & Lei, Y. (2023). A review for control theory and condition monitoring on construction robots. Journal of Field Robotics, 40(4), 934-954

work page 2023

[8] [8]

Recht, B. (2019). A tour of reinforcement learning: The view from continuous control. Annual Review of Control, Robotics, and Autonomous Systems, 2(1), 253-279

work page 2019