Category Archives: Control Engineering

A good review of allostasis and control theory applied to physiology

November 21, 2024 12:33 ,

Eli Sennesh, Jordan Theriault, Dana Brooks, Jan-Willem van de Meent, Lisa Feldman Barrett, Karen S. Quigley, Interoception as modeling, allostasis as control, Biological Psychology, Volume 167, 2022 DOI: 10.1016/j.biopsycho.2021.108242.

The brain regulates the body by anticipating its needs and attempting to meet them before they arise – a process called allostasis. Allostasis requires a model of the changing sensory conditions within the body, a process called interoception. In this paper, we examine how interoception may provide performance feedback for allostasis. We suggest studying allostasis in terms of control theory, reviewing control theory’s applications to related issues in physiology, motor control, and decision making. We synthesize these by relating them to the important properties of allostatic regulation as a control problem. We then sketch a novel formalism for how the brain might perform allostatic control of the viscera by analogy to skeletomotor control, including a mathematical view on how interoception acts as performance feedback for allostasis. Finally, we suggest ways to test implications of our hypotheses.

Posted in: Control Engineering, Psycho-physiological bases of engineering , Tagged:

Very interesting seminal work on the analysis and synthesis of embodied agents as coupled dynamical systems composed of both agent and environment

September 19, 2024 12:27 ,

Randall D. Beer, A dynamical systems perspective on agent-environment interaction, Artificial Intelligence 72 (1995) 173-215 DOI: 10.1016/0004-3702(94)00005-L.

Using the language of dynamical systems theory, a general theoretical framework for the synthesis and analysis of autonomous agents is sketched. In this framework, an agent and its environment are modeled as two coupled dynamical systems whose mutual interaction is in general jointly responsible for the agent’s behavior. In addition, the adaptive fit between an agent and its environment is characterized in terms of the satisfaction of a given constraint on the trajectories of the coupled agent-environment system. The utility of this framework is demonstrated by using it to first synthesize and then analyze a walking behavior for a legged agent.

Posted in: Control Engineering , Tagged: ,

Integrating the physical model of a Model Predictive Controller into an Actor-Critic RL framework to improve safety and flexibility at the same time

September 12, 2024 14:51 ,

Angel Romero, Yunlong Song, Davide Scaramuzza, Actor-Critic Model Predictive Control, IEEE International Conference on Robotics and Automation, Yokohama, 2024 arXiv:2306.09852 [cs.RO].

An open research question in robotics is how
to combine the benefits of model-free reinforcement learning
(RL)—known for its strong task performance and flexibility in
optimizing general reward formulations—with the robustness
and online replanning capabilities of model predictive control
(MPC). This paper provides an answer by introducing a new
framework called Actor-Critic Model Predictive Control. The
key idea is to embed a differentiable MPC within an actor-
critic RL framework. The proposed approach leverages the
short-term predictive optimization capabilities of MPC with
the exploratory and end-to-end training properties of RL. The
resulting policy effectively manages both short-term decisions
through the MPC-based actor and long-term prediction via
the critic network, unifying the benefits of both model-based
control and end-to-end learning. We validate our method in
both simulation and the real world with a quadcopter platform
across various high-level tasks. We show that the proposed
architecture can achieve real-time control performance, learn
complex behaviors via trial and error, and retain the predictive
properties of the MPC to better handle out of distribution
behaviour.

Posted in: Applications of reinforcement learning to control engineering , Tagged: ,

A remote Matlab laboratory for LTI system identification

November 3, 2023 09:32 ,

Z. Lei, H. Zhou, W. Hu and G. -P. Liu, Teaching and Comprehensive Learning With Remote Laboratories and MATLAB for an Undergraduate System Identification Course, EEE Transactions on Education, vol. 65, no. 3, pp. 402-408, Aug. 2022 DOI: 10.1109/TE.2021.3123302.

Contribution: This article introduces the teaching and learning with remote laboratories and MATLAB for an undergraduate system identification (SI) course, which can be employed for students at the advanced level with a control background. Background: SI has been widely used in all engineering fields; thus, the SI course that includes complex theories, concepts, and formulas is crucial for engineering education. Constraints, such as time, space, cost, and maintenance work, pose limitations for conventional laboratories, and current remote laboratories may not offer experiences to enhance control-oriented practical skills. Intended Outcomes: The proposed teaching and learning using remote laboratories is intended to facilitate the understanding of theories and concepts, and enhance the ability of design and implementation of control algorithms, the conducting of experiments, data collecting, data analysis, and the conducting of SI with MATLAB. Application Design: In the classroom teaching, theoretical lectures regarding SI are delivered to students by the teacher, along with the classroom demonstration with the networked control system laboratory for online experimentation. Then, the laboratory work is required to be completed by the students using the remote laboratory. A tailored laboratory report is supposed to be handed in by each student after the experimentation. Findings: The effectiveness of the proposed method was evaluated through the analysis of student performance and student responses to surveys. The student performance analysis indicates that the application of the remote laboratories is effective, and the feedback from students shows that they can benefit from the application of remote laboratories, and they would like the remote laboratories to be expanded to other courses.

Posted in: Control Engineering , Tagged: , , ,

Abstraction of continuous control problems considered as MDPs

October 20, 2023 09:03 ,

H. G. Tanner and A. Stager, Data-Driven Abstractions for Robots With Stochastic Dynamics, IEEE Transactions on Robotics, vol. 38, no. 3, pp. 1686-1702, June 2022 DOI: 10.1109/TRO.2021.3119209.

This article describes the construction of stochastic, data-based discrete abstractions for uncertain random processes continuous in time and space. Motivated by the fact that modeling processes often introduce errors which interfere with the implementation of control strategies, here the abstraction process proceeds in reverse: the methodology does not abstract models; rather it models abstractions. Specifically, it first formalizes a template for a family of stochastic abstractions, and then fits the parameters of that template to match the dynamics of the underlying process and ground the abstraction. The article also shows how the parameter-fitting approach can be implemented based on a probabilistic model validation approach which draws from randomized algorithms, and results in a discrete abstract model which is approximately simulated by the actual process physics, at a desired confidence level. In this way, the models afford the implementation of symbolic control plans with probabilistic guarantees at a desired level of fidelity.

Posted in: Control Engineering , Tagged: , ,

RL in manufacturing control

October 13, 2023 09:49 ,

Vladimir Samsonov, Karim Ben Hicham, Tobias Meisen, Reinforcement Learning in Manufacturing Control: Baselines, challenges and ways forward, Engineering Applications of Artificial Intelligence, Volume 112, 2022 DOI: 10.1016/j.engappai.2022.104868.

The field of Neural Combinatorial Optimization (NCO) offers multiple learning-based approaches to solve well-known combinatorial optimization tasks such as Traveling Salesman or Knapsack problem capable of competing with classical optimization approaches in terms of both solution quality and speed. This brought the attention of the research community to the tasks of Manufacturing Control (MC) with combinatorial nature. In this paper we outline the main components of MC tasks, select the most promising application fields and analyze dedicated learning-based solutions available in the literature. We draw multiple parallels to the current state of the art in the NCO field and allocate the main research gaps and directions on the perception, cognition and interaction levels. Using a set of practical examples we implement and benchmark common design patterns for single-agent Reinforcement Learning (RL) solutions. Along with testing existing solutions, we build on the ranked reward idea (Laterre et al., 2018) and offer a novel Multi-Instance Ranked Reward (m-R2) approach tailored to MC optimization tasks. It minimizes the reward shaping effort and defines a suitable training curriculum for more stable learning by separately tracking the agent\u2019s performance on every scheduling task and rewarding only policies contributing towards better scheduling solutions. We implement all solution design patterns as a set of interchangeable modules with a shared API, unified in a benchmarking framework with the focus on standardization of training and evaluation processes, reproducibility and simplified experiment lifecycle management. In addition to the framework, we make available our discrete-event simulation of a job shop production.

Also:

Zhihao Liu, Quan Liu, Wenjun Xu, Lihui Wang, Zude Zhou,
Robot learning towards smart robotic manufacturing: A review,
Robotics and Computer-Integrated Manufacturing,
Volume 77,
2022,
102360,
ISSN 0736-5845,
https://doi.org/10.1016/j.rcim.2022.102360.

Posted in: Applications of reinforcement learning to control engineering , Tagged:

Shorter exploration stage in RL through the use of expert (a PID) that sets the expectation of the explored action

October 13, 2023 08:50 ,

J. Enrique Sierra-Garcia, Matilde Santos, Ravi Pandit, Wind turbine pitch reinforcement learning control improved by PID regulator and learning observer, Engineering Applications of Artificial Intelligence, Volume 111, 2022 DOI: 10.1016/j.engappai.2022.104769.

Wind turbine (WT) pitch control is a challenging issue due to the non-linearities of the wind device and its complex dynamics, the coupling of the variables and the uncertainty of the environment. Reinforcement learning (RL) based control arises as a promising technique to address these problems. However, its applicability is still limited due to the slowness of the learning process. To help alleviate this drawback, in this work we present a hybrid RL-based control that combines a RL-based controller with a proportional\u2013integral\u2013derivative (PID) regulator, and a learning observer. The PID is beneficial during the first training episodes as the RL based control does not have any experience to learn from. The learning observer oversees the learning process by adjusting the exploration rate and the exploration window in order to reduce the oscillations during the training and improve convergence. Simulation experiments on a small real WT show how the learning significantly improves with this control architecture, speeding up the learning convergence up to 37%, and increasing the efficiency of the intelligent control strategy. The best hybrid controller reduces the error of the output power by around 41% regarding a PID regulator. Moreover, the proposed intelligent hybrid control configuration has proved more efficient than a fuzzy controller and a neuro-control strategy.

Posted in: Applications of reinforcement learning to control engineering , Tagged: ,

A practical setup for control engineering courses

September 25, 2023 11:24 ,

A. Chevalier, K. Dekemele, J. Juchem and M. Loccufier, Student Feedback on Educational Innovation in Control Engineering: Active Learning in Practice, IEEE Transactions on Education, vol. 64, no. 4, pp. 432-437, Nov. 2021, DOI: 10.1109/TE.2021.3077278.

Contribution: An education innovation in control engineering using practical setups and its evaluation based on a three-year student feedback study and examination grades. Background: Based on extensive research, education\u2019s transition toward active learning and more practical experience has been shown to increase learning outcomes. Contrary to virtual and remote labs, a practical session with an individual setup for each student provides the most practical experience. Intended Outcomes: To show a positive effect on learning performance by integrating practical sessions in basic control engineering. Application Design: Presenting low cost setups that can be mass produced and adapt to the course\u2019s growing complexity. These setups are evaluated during a three-year feedback study. Findings: The developed setups increased understanding of theoretical concepts. The new methodology significantly improved students\u2019 average grades. The students\u2019 interest in control theory is triggered. This case study could guide other institutions toward successfully implementing highly individual practical sessions for large groups.

Posted in: Control Engineering , Tagged: ,

Using a physical simulator for sampled rollouts in stochastic optimal control

September 15, 2023 09:47 ,

Carius J, Ranftl R, Farshidian F, Hutter M. Constrained stochastic optimal control with learned importance sampling: A path integral approach, The International Journal of Robotics Research. 2022;41(2):189-209, DOI: 10.1177/02783649211047890.

Modern robotic systems are expected to operate robustly in partially unknown environments. This article proposes an algorithm capable of controlling a wide range of high-dimensional robotic systems in such challenging scenarios. Our method is based on the path integral formulation of stochastic optimal control, which we extend with constraint-handling capabilities. Under our control law, the optimal input is inferred from a set of stochastic rollouts of the system dynamics. These rollouts are simulated by a physics engine, placing minimal restrictions on the types of systems and environments that can be modeled. Although sampling-based algorithms are typically not suitable for online control, we demonstrate in this work how importance sampling and constraints can be used to effectively curb the sampling complexity and enable real-time control applications. Furthermore, the path integral framework provides a natural way of incorporating existing control architectures as ancillary controllers for shaping the sampling distribution. Our results reveal that even in cases where the ancillary controller would fail, our stochastic control algorithm provides an additional safety and robustness layer. Moreover, in the absence of an existing ancillary controller, our method can be used to train a parametrized importance sampling policy using data from the stochastic rollouts. The algorithm may thereby bootstrap itself by learning an importance sampling policy offline and then refining it to unseen environments during online control. We validate our results on three robotic systems, including hardware experiments on a quadrupedal robot.

Posted in: Applications of reinforcement learning to control engineering , Tagged: , ,

Identifying state-space-models of systems with autoencoders

July 11, 2023 09:31 ,

Daniele Masti, Alberto Bemporad, Learning nonlinear state–space models using autoencoders, . Automatica, Volume 129, 2021 DOI: 10.1016/j.automatica.2021.109666.

We propose a methodology for the identification of nonlinear state–space models from input/output data using machine-learning techniques based on autoencoders and neural networks. Our framework simultaneously identifies the nonlinear output and state-update maps of the model. After formulating the approach and providing guidelines for tuning the related hyper-parameters (including the model order), we show its capability in fitting nonlinear models on different nonlinear system identification benchmarks. Performance is assessed in terms of open-loop prediction on test data and of controlling the system via nonlinear model predictive control (MPC) based on the identified nonlinear state–space model.

Posted in: Control Engineering , Tagged: ,