Adaptive control allocation for constrained systems

Tohidi, Seyed Shahabaldin; Yıldız, Yıldıray; Kolmanovsky, Ilya

doi:10.1016/j.automatica.2020.109161

Cited by 46 publications

(46 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bold numbers remark the results of the best performance. It can be noticed that the novel adaptive controller (38), (39), (43), and (44) is the controller with the lower position error RMS value. Therefore, the advantage of the non-linear compensation is clear, as can be seen in the performance of the modelbased controller.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

Montoya–Cháirez¹,

Moreno–Valenzuela²,

Santibáñez

et al. 2021

IET Control Theory & Appl

View full text Add to dashboard Cite

By relying on the input-output feedback linearization approach, a novel adaptive controller for flexible joint robots is proposed in this work. First, a model-based controller is developed to get a structure that is useful in the development of the adaptive controller. The adaptive version is developed by using two techniques. To stabilize the output function, an adaptive neural network controller is used, which approximates the non-linear function that contains the uncertainties. The desired rotor position required by the input-output feedback linearization controller is defined with the structure of a link dynamics adaptive regressor-based controller. The main reason to adopt the mentioned structure in the definition of the desired rotor link position is to guarantee its differentiability. Real-time experiment comparisons among the model-based controller, a model-based controller with desired compensation, an adaptive controller based on joint torque feedback, and an adaptive neural network-based controller are carried out. Experimental results support the theory reported in this document and the accuracy of the proposed approach.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Finally, we can conclude that the proposed adaptive controller presents better tracking accuracy. 39), (43), and (44)…”

Section: Resultsmentioning

confidence: 99%

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

Montoya–Cháirez¹,

Moreno–Valenzuela²,

Santibáñez

et al. 2021

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…Proof: See [30], [31] and [32] Theorem 2: The closed loop system, consisting of the plant (44), and the control input, v, whose elements are given in ( 26) - (31), is stable.…”

Section: Control Allocationmentioning

confidence: 99%

“…This model is also validated against a multi-body dynamics based commercial software [29]. (2) Inspired from [30], [31] and [32], a controller framework involving a fault-tolerant control allocation algorithm is developed, which is capable of controlling the longitudinal, lateral and vertical vehicle dynamics in an integrated manner. To achieve this, the control allocation presented in [30] and [31] had to be extended to handle the class of systems that have time-varying control input matrix dynamics.…”

Section: Introductionmentioning

confidence: 99%

A Fault Tolerant Vehicle Stability Control Using Adaptive Control Allocation

Temiz

Çakmakçı

Yıldız

2018

Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In order to enable synchronisation of chaotic systems for encryption schemes and secure communication devices, many useful control techniques have been devised in the literature such as adaptive control [22], [23], active control [24], [25], fuzzy control [26], [27], backstepping control [28], [29], and sliding mode control [30], [31]. Sliding mode control (SMC) is a special robust approach for variable structure control (VSC) knowledge.…”

Section: Introductionmentioning

confidence: 99%

Rikitake dynamo system, its circuit simulation and chaotic synchronization via quasi-sliding mode control

et al. 2021

View full text Add to dashboard Cite

Rikitake dynamo system (1958) is a famous two-disk dynamo model that is capable of executing nonlinear chaotic oscillations similar to the chaotic oscillations as revealed by palaeomagnetic study. First, we detail the Rikitake dynamo system, its signal plots and important dynamic properties. Then a circuit design using Multisim is carried out for the Rikitake dynamo system. New synchronous quasi-sliding mode control (QSMC) for Rikitake chaotic system is studied in this paper. Furthermore, the selection on switching surface and the existence of QSMC scheme is also designed in this paper. The efficiency of the QSMC scheme is illustrated with MATLAB plots.

show abstract

Adaptive control allocation for constrained systems

Cited by 46 publications

References 53 publications

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

A Fault Tolerant Vehicle Stability Control Using Adaptive Control Allocation

Rikitake dynamo system, its circuit simulation and chaotic synchronization via quasi-sliding mode control

Contact Info

Product

Resources

About