Browsing by Subject "Adaptive control"

Now showing 1 - 20 of 29

Open Access
A robust human-autonomy collaboration framework with experimental validation
(Institute of Electrical and Electronics Engineers, 2024-09-24) Uzun, M. Yusuf; İnanç, Emirhan; Yıldız, Yıldıray
In this letter, we introduce a robust human-autonomy collaboration framework focusing on flight control applications. The objective is to optimize performance by always keeping the human operator in control of the vehicle while compensating for human limitations. A significant aspect of this framework is its robustness to human intent estimation errors. This is achieved by precisely modulating the automation assistance to prevent undesired interference with the human operator. We provide human-in-the-loop experimental results, demonstrating significant performance improvements when intent estimation is accurate. Experiments also validate that the pilots maintain vehicle control even when the estimation is faulty.
Open Access
Adaptive control allocation for over-actuated systems with actuator saturation
(Elsevier B.V., 2017) Tohidi, Seyed Shahabaldin; Yıldız, Yıldıray; Kolmanovsky, Ilya
This paper proposes an adaptive control allocation approach for over-actuated systems with actuator saturation. The methodology can tolerate actuator loss of effectiveness without utilizing the control input matrix estimation, eliminating the need for persistence of excitation. Closed loop reference model adaptive controller is used for identifying adaptive parameters, which provides improved performance without introducing undesired oscillations. The modular design of the proposed control allocation method improves the flexibility to develop the outer loop controller and the control allocation strategy separately. The ADMIRE model is used as an over-actuated system, to demonstrate the effectiveness of the proposed method using simulation results.
Open Access
Adaptive control of cyberphysical human systems
(2021-08) Tohidi, Seyed Shahabaldin
This dissertation focuses on the control of cyberphysical human systems in the presence of actuators’ redundancy and constraints. A novel adaptive control tech-nique is proposed to allocate control signals among redundant actuators in the presence of uncertainty and actuator saturation. The proposed method does not require any uncertainty identiﬁcation or persistency of excitation assumption. The stability of the proposed method is guaranteed using Lyapunov stability analysis. In addition, a modiﬁed projection operator that can be implemented to the adaptive control allocation is proposed. This operator enables the allo-cator to handle both magnitude and rate limits of actuators. A novel sliding mode controller with time-varying sliding surface is designed to complement the adaptive allocator and guarantee stability and reference tracking in the presence of uncertainty and actuator saturation. This controller is robust to both adap-tive control allocation error and external disturbance. Furthermore, an adaptive human model is proposed to mimic the human control response in the presence of uncertainty. The proposed structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov-Krasovskii stabil-ity criteria. To validate this model, an experimental setup is employed to collect data and a statistical analysis is conducted to measure the predictive power of the pilot model. Finally, the stability limits of a human-in-the-loop closed loop control system, where the plant to be controlled has redundant actuators with uncertain dynamics, are demonstrated. Various human models with and without time delays are investigated. Simulation results are provided to demonstrate the eﬀectiveness of the proposed methods in each chapter.
Open Access
Adaptive control with LSTM augmentation: theory and human-in-the-loop validation
(2024-08) İnanç, Emirhan
This thesis presents a novel adaptive control architecture that provides dramatically better transient response performance compared to conventional adaptive control methods. This is accomplished by the synergistic employment of a traditional Adaptive Neural Network (ANN) controller and a Long Short-Term Memory (LSTM) network. LSTM structures can take advantage of the dependencies in an input sequence, which helps predict uncertainty. We introduce a training approach through which the LSTM network learns to compensate for the deficiencies of the ANN controller in a closed-loop setting. This improves the system’s transient response and allows the controller to react to unexpected events quickly. This study also investigates the human-in-the-loop performance of the proposed control framework. Although the LSTM-augmented control method drastically improves the transient response, especially in the presence of significant and rapid uncertainty changes, its interactions with a human operator must be analyzed to ensure safe operation. First, a human pilot model is used to investigate the overall system’s behavior and explore the controller’s performance for a reference tracking task. Then, human-in-the-loop experiments are conducted to analyze how the system responds in the presence of an actual human operator in the loop. Through careful simulation studies, we demonstrate that this architecture improves the estimation accuracy on a diverse set of uncertainties. The overall system’s stability is analyzed via a rigorous Lyapunov analysis, and the proposed method is shown to be highly effective, as demonstrated through numerical simulations and human-in-the-loop experiments.
Open Access
Adaptive decentralized control of interconnected systems
(Watam Press, 2004) Sezer, M. E.; Altunel, H.
This paper presents a decentralized adaptive stabilization scheme for a class of interconnected systems using high-gain adaptive controllers. The nominal subsystems are assumed to satisfy some mild conditions required by standard adaptive control schemes, and the interconnections certain structural conditions. The decentralized controllers are high-gain dynamic systems operating on local outputs to generate local control inputs. Both continuous-time and sampled-data controllers are considered. The idea behind the design of continuous-time controllers is the small-gain theorem. The sampled-data controllers are discrete versions of the continuous-time controllers, where local sampling frequencies of the controllers also serve as their gains. The controllers are synchronized by a careful choice of their sampling frequencies. In order to guarantee closed-loop stability when the interconnection bounds are unknown, controller gains are increased, using simple centralized adaptation rule, to sufficiently high values as required by the strength of interconnections. The results are illustrated by a practical example.
Open Access
Adaptive friction compensations for mechanical systems with measurement delay
(SAGE Publications, 2021) Odabaş, Caner; Morgül, Ömer
Application performance of mechanical positioning systems might not coincide with the theory, mainly due to nonlinearities or imperfections of system models. Although it is sometimes possible to ignore these mismatches, systems generally suffer from performance degradation or even instability eventually. Especially, friction force and time delay are two major factors of these undesired effects. Hence, in this paper, Smith predictor-based controllers and an adaptive Coulomb friction observer are designed to enhance position tracking performance of a mechanical system including time delay. In fact, implemented hierarchical control scheme provides two-degree of freedom to control both velocity and position separately. The proposed observer structure is mainly motivated by the Friedland-Park observer but could be considered as an extension of it which characterizes a general class of nonlinear functions for friction estimation. To assure its functionality with delayed measurements, different velocity predictor schemes are designed and their performances are compared. As a guideline for observer design, some conditions for exponential stability and robustness analysis are presented. Simulation results demonstrate that the proposed control system enhances the tracking performance even when the actual friction is a compound of various static and dynamic terms.
Open Access
An adaptive human pilot model for adaptively controlled systems
(IEEE, 2021-12-17) Habboush, Abdullah; Yıldız, Yıldıray
Despite their success in handling uncertain dynamical systems that are prone to failure, adaptive controllers are observed to have unfavorable interactions with human pilots in certain applications. To alleviate this problem, we need to evaluate the safety and performance of adaptive controllers in the simulation environment using realistic pilot models before conducting flight tests. While many useful human pilot models exist in the literature, models that are adequate for the prediction of adaptive human-adaptive controller interactions are yet to be available. In this letter, we fill this gap by proposing an adaptive human pilot model suited for the prediction of human behavior in the loop with an adaptive controller. The model can serve as a valuable tool guiding the design of adaptive controllers so as to ensure smooth pilot-controller interactions.
Open Access
Adaptive observer designs for friction estimation in position control of simple mechanical systems with time delay
(2021-09) Odabaş, Caner
Friction force/torque is a well known natural eﬀect that can cause performance degradation or even instability in mechanical systems, although it sometimes can be disregarded in closed loop feedback design phase. Hence, friction modeling and cancellation methods can be vital to achieve desired robustness and performance criteria in position control problems. Basically, the topic of friction cancellation is divided into two main categories named model based and non-model based methods. Friction modeling is a broad area of research and there are lots of diﬀerent modeling approaches in various complexities. Among these approaches, Coulomb Model is one the simplest yet fundamental models. Nevertheless, in some cases, being a classical static model, it is inadequate to exhibit the dominant friction components occurring at diﬀerent motion stages such as break-away force, stick-slip motion, pre-sliding behavior or friction lag. Generally, dynamical models, i.e. LuGre Model, are more advanced as a result, they are better to describe such friction eﬀects. Unfortunately, for these cases, the number of friction parameters are increased. In fact, there is a trade-oﬀ between model complexity and parameter identiﬁcation. A desired system response may not be achieved when model parameters do not coincide with the existing friction coeﬃcients. In this manner, precise identiﬁcation of each parameter can be challenging when there are many of them. Besides, some of these parameters might be time varying due to environment, temperature, material properties, position, etc. Therefore, non-model based adaptive schemes are prevalent in the literature since these methods do not require any parameter identiﬁcation. In this study, we focus on adaptive observer based friction compensation techniques and provide some stability conditions. First, we consider simple second order mechanical systems with or without time delay under Coulomb friction. To estimate the Coulomb friction, we ﬁrst consider Friedland-Park observer. Then, some necessary conditions are stated to extend the estimation function in the observer structure to a larger class of functions. Especially measurement delay can be signiﬁcant since observers estimate friction based on the velocity measurements. Therefore, it is proposed to employ a velocity predictor either based on numerical diﬀerential equation solvers or inverse Pade approximant when the existing time delay is large. What is more, a new observer design that considers friction and velocity error dynamics together is proposed as a novel contribution. Extensive MATLAB simulations are conducted to investigate the performances of proposed observers in a closed loop position control system with and without delay. To this end, Smith predictor and ITAE index-based designs are considered to utilize a position controller. In some of these simulations, LuGre model is preferred to mimic the actual friction instead of Coulomb friction in order to observe the eﬀects of dynamic parameters. Moreover, some experiments are performed on DC motor platform driven by Arduino Uno microcontroller. Under the light of acquired results, observer based friction compensation improves the system performance even existing friction cannot be conﬁned to Coulomb coeﬃcient, especially when the implemented controller has low bandwidth. Also, in terms of practicability, it is an advantage that these observer structures do not require any parameter identiﬁcation.
Open Access
Air fuel ratio control using delay resistant closed loop reference model adaptive control
(Hezarfen Havacılık ve Uzay Teknolojileri Enstitüsü, 2018) Yıldız, Yıldıray
The focus of this paper is the air fuel ratio control of spark ignited engines. It is known that for an efficient removal of the pollutants from the exhaust gases, air fuel ratio has to be kept within a narrow band around the stoicometric ratio, which represents the condition where the air amount is perfectly matched with the fuel amount for a complete burn. The main challenges for this control problem are the inherent time delay of the system and uncertain dynamics. In this paper, employment of a high performance adaptive controller, which explicitly compensates for the delays without causing excessive oscillations, is proposed. The performance of this controller is demonstrated via simulation studies. To emphasize the importance of delay compensation, the controller is compared with the closed loop model reference adaptive controller, which do not have explicit delay compensation.
Open Access
Attention-enabled memory for concurrent learning adaptive control
(Institute of Electrical and Electronics Engineers, 2022-12-16) Habboush, Abdullah; Yıldız, Yıldıray
Transient tracking error dynamics are inevitable in any practical closed-loop control system. While numerous works are devoted to improving these dynamics, in this letter, we focus on taking advantage of it first, in the context of adaptive control. We propose a memory architecture that can make use of stored significant data about the transients of previously experienced anomalies to aid in obtaining a resilient system against uncertainties. The proposed architecture consists of 1) a memory containing data about a variety of uncertainties, 2) a short-term memory that aids in handling new uncertainties, and 3) an attention-based reading mechanism that enables the controller to retrieve only relevant data from the memory. The effectiveness of the architecture is validated through numerical simulations, and a rigorous Lyapunov stability analysis is provided.
Open Access
Computable delay margins for adaptive systems with state variables accessible
(Institute of Electrical and Electronics Engineers Inc., 2017) Hussain, H. S.; Yildiz, Y.; Matsutani, M.; Annaswamy, A. M.; Lavretsky, E.
Robust adaptive control of plants whose state variables are accessible in the presence of an input time delay is established in this paper. It is shown that a standard model reference adaptive controller modified with projection ensures global boundedness of the overall adaptive system for a range of nonzero delays. The upper bound of such delays, that is, the delay margin, is explicitly defined and can be computed a priori. © 1963-2012 IEEE.
Open Access
Control of uncertain sampled-data systems: An Adaptive posicast control approach
(Institute of Electrical and Electronics Engineers Inc., 2017) Abidi K.; Yildiz, Y.; Annaswamy A.
This technical note proposes a discrete-time adaptive controller for the control of sampled-data systems. The design is inspired from the Adaptive Posicast Controller (APC) which was designed for time-delay systems in continuous time. Due to the performance degradation caused by digital approximation of continuous laws, together with the problem of assuming time-delays as integer multiples of sampling intervals, the benefits of APC could not be fully realized. In this technical note, these approximations/assumptions are eliminated. In addition, a disturbance observer is incorporated into the controller design which minimizes the effect of disturbances on the system. Extension to the case of uncertain input time-delay is also presented. The proposed approach is verified in simulation studies. © 1963-2012 IEEE.
Open Access
A control theoretical adaptive human pilot model: theory and experimental validation
(Institute of Electrical and Electronics Engineers Inc., 2022-04-19) Yildiz, Yildiray; Tohidi, S.S
This article proposes an adaptive human pilot model that is able to mimic the crossover model in the presence of uncertainties. The proposed structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov–Krasovskii stability criteria. The model can be employed for human-in-the-loop stability and performance analyses incorporating different types of controllers and plant types. For validation purposes, an experimental setup is employed to collect data and a statistical analysis is conducted to measure the predictive power of the pilot model.
Open Access
Enhancing human operator performance with long short-term memory networks in adaptively controlled systems
(Institute of Electrical and Electronics Engineers, 2023-11-20) Uzun, M. Yusuf; İnanç, Emirhan; Yıldız, Yıldıray
The focus of this letter is developing a Long Short-Term Memory (LSTM) network-based control framework that works in collaboration with the human operator to enhance the overall closed-loop system performance in adaptively controlled systems. The domain of investigation is chosen to be flight control, although the proposed approach can be generalized for other domains such as automotive control. In accordance with this choice, an adaptive human pilot model is used as the mathematical representation of the pilot during the technical development of the method. An LSTM network is designed in such a way that it predicts and compensates for the inadequacies of the human operator's decisions while they fly an aircraft that has an adaptive inner loop controller. The simulation results demonstrate that the tracking performance is improved, and the pilot workload is reduced.
Open Access
Explicit adaptive time-delay compensation for bilateral teleoperation
(IEEE, 2015) Abidi, K.; Yıldız, Yıldıray; Körpe, Bekir Emre
This paper proposes a control framework that addresses the destabilizing effect of communication time-delays and system uncertainties in telerobotics, in the presence of force-feedback. Force feedback is necessary to obtain transparency, which is providing the human operator as close a feel as possible of the environment where the slave robot is operating. Achieving stability and providing transparency are conflicting goals. This is the major reason why currently a very few, if at all, fully operational force feedback teleoperation devices exist except for research environments. The proposed framework handles system uncertainty with adaptation and communication time delays with explicit delay compensation. The technology that allows this explicit adaptive time-delay compensation is inspired by MIT's Adaptive Posicast Controller.
Open Access
Fault tolerant control for over-actuated systems: an adaptive correction approach
(IEEE, 2016) Tohidi, Seyed Shahabaldin; Yıldız, Yıldıray; Kolmanovsky, İ.
This paper proposes an adaptive fault tolerant control allocation approach for over-actuated systems. The methodology does not utilize the control input matrix estimation to tolerate actuator faults and, therefore, the proposed control allocation method does not require persistence of excitation. Adaptive control approach with a closed loop reference model is used for identifying control allocation parameters, which provides improved performance without introducing undesired oscillations. Furthermore, a sliding mode controller is used to guarantee the outer loop asymptotic stability. Simulation results are provided, where the ADMIRE model is used as an over-actuated system, to demonstrate the effectiveness of the proposed method.
Open Access
A fault tolerant vehicle stability control using adaptive control allocation
(ASME, 2018) Temiz, Ozan; Çakmakçı, Melih; Yıldız, Yıldıray
This paper presents an integrated fault-tolerant adaptive control allocation strategy for four wheel frive - four wheel steering ground vehicles to increase yaw stability. Conventionally, control of brakes, motors and steering angles are handled separately. In this study, these actuators are controlled simultaneously using an adaptive control allocation strategy. The overall structure consists of two steps: At the first level, virtual control input consisting of the desired traction force, the desired moment correction and the required lateral force correction to maintain driver’s intention are calculated based on the driver’s steering and throttle input and vehicle’s side slip angle. Then, the allocation module determines the traction forces at each wheel, front steering angle correction and rear steering wheel angle, based on the virtual control input. Proposed strategy is validated using a non-linear three degree of freedom reduced two-track vehicle model and results demonstrate that the vehicle can successfully follow the reference motion while protecting yaw stability, even in the cases of device failure and changed road conditions.
Open Access
Gas generator pressure control in throttleable ducted rockets: a classical and adaptive control approach
(American Institute of Aeronautics and Astronautics (AIAA), 2015) Alan, Anıl; Yıldız, Yıldıray; Poyraz, Ü.; Olgun, U.
This paper describes the control of gas generator pressure in throttleable ducted rockets using nonlinear adaptive control as well as classical control approaches. Simulation results using the full nonlinear and time-varying dynamics of the gas generator are reported in both classical controller and adaptive controller cases. "Closed-loop Reference Model" structure is used together with the adaptive controller to improve transient response. Controllers are simulated to test their robustness by introducing uncertinities, disturbances and noise to the system. Cold Air Test Plant (CATP) is used as the test facility to compare the controllers and validate the results from simulations. Moreover, damping effect of CRM to oscillations in adaptive controller case is observed in CATP tests.
Open Access
Identification and adaptive control of bipedal robot motion with artificial neural networks
(2024-07) Çatalbaş, Burak
Artificial Neural Networks (ANNs) is one of the most popular fields of machine learning thanks to the critical improvements in the last decade, including their applications in the field of robotics and control. The important usage of neural networks in robotics makes it possible for robots to act and interact similar to humans. In this manner, legged robots are important platforms to mimick human locomotion. However, there are significant difficulties to apply system identification and control schemes for these hybrid dynamical structures. With this purpose, this thesis focuses on using artificial neural network-based novel techniques on these problems, for reaching to an efficient walking ability for bipedal robot systems like their counterparts in the nature. In this thesis, our work to find and apply our novel techniques is mainly divided into two parts. In the first part, inspired by a class of activation functions frequently used in deep learning literature, we propose a novel activation function and investigate its performance in various segmentation and classification tasks by using different well-known datasets. In the second part of the thesis, biped robot locomotion is chosen as the main topic. Separate datasets are created for three experiment configurations. For 2D and 3D simulations, locomotion control, system identification and adaptive control are applied with neural networks for successful periodical walking with low errors, having approximations of robot models and preparing for the adaptive learning using both control and identification blocks, respectively. For 2D physical robot system, system identification is completed for a walking dataset generated with varying speed levels. For all cases, proposed novel activation function DELU (ExtendeD Exponential Linear Unit) and its tuned functions are tried together with other activation functions in comparison, to reach better performances.
Open Access
Long short-term memory for improved transients in neural network adaptive control
(IEEE, 2023-07-03) İnanç, Emirhan; Gürses, Yiğit; Habboush, Abdullah; Yıldız, Yıldıray
In this study, we propose a novel adaptive control architecture, which provides dramatically better performance compared to conventional methods. What makes this architecture unique is the synergistic employment of a traditional, Adaptive Neural Network (ANN) controller and a Long Short-Term Memory (LSTM) network. LSTM structures, unlike the standard feed-forward neural networks, take advantage of the dependencies in an input sequence, which helps predict the evolution of an uncertainty. Through a training method we introduced, the LSTM network learns to compensate for the deficiencies of the ANN controller. This substantially improves the transient response by allowing the controller to quickly react to unexpected events. Through careful simulation studies, we demonstrate that this architecture can improve the estimation accuracy on a diverse set of unseen uncertainties. We also provide an analysis of the contributions of the ANN controller and LSTM network, identifying their individual roles in compensating low and high frequency error dynamics. This analysis provides insight into why and how the LSTM augmentation improves the system’s transient response.