Multi-objective gain optimizer for a multi-input active disturbance rejection controller: Application to series elastic actuators

DeBoon, Brayden; Nokleby, Scott B.; Rossa, Carlos

doi:10.1016/j.conengprac.2021.104733

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…In the transient stage, the increase of ω o provides faster convergence of initial errors towards zero, but may result in the higher values of z z z(t) while the observer peaking due to the dependency of the first component of ( 14) on factor max{ω −2 o , ω 2 o }. A popular choice of the feedback controller, introduced within the general control law (6), takes the form of a state feedback…”

Section: Control Plant Description and Conventional Robust Control De...mentioning

confidence: 99%

“…Therefore, we describe the system by its transient state estimated by an ESO in some predefined experiment, which we call basic experiment or test experiment. In this experiment we assume that the system starts from some random initial state x x x test 0 , and it is controlled in closed loop by the ADRC based controller defined in (6), with an ESO defined in (7) with the gains set according to bandwidth parametrization (see (13)) with ω 0 = 25. Such a choice of the observer gains seems to be large enough to track the evolution of the system, and small enough to not over-amplify sensor noise, for the objects and noise parameters ranges we consider.…”

Section: Neural Network-based Performance Estimatormentioning

confidence: 99%

“…In [49] and [38], the authors presented an analytical tuning method providing the best performance of the ADRC structure expressed solely upon the control-error-dependent criteria in a noiseless environment. In [39] and [6], the authors considered also the control cost as a factor that needs to be minimized to reduce the energy consumption of the robust control process, while in [26] the observation error of the measured signals was taken into account. Tuning procedures described in [28] and [12] have utilized prior knowledge about the plant structure and some known or identified model parameters to obtain assumed control performance requirements.…”

Section: Introductionmentioning

confidence: 99%

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Tuning of extended state observer with neural network-based control performance assessment

Kicki¹,

Łakomy²,

Lee³

2021

Preprint

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The extended state observer (ESO) is an inherent element of robust observer-based control systems that allows estimating the impact of disturbance on system dynamics. Proper tuning of ESO parameters is necessary to ensure a good quality of estimated quantities and impacts the overall performance of the robust control structure. In this paper, we propose a neural network (NN) based tuning procedure that allows the user to prioritize between selected quality criteria such as the control and observation errors and the specified features of the control signal. The designed NN provides an accurate assessment of the control system performance and returns a set of ESO parameters that delivers a near-optimal solution to the user-defined cost function. The proposed tuning procedure, using an estimated state from the single closed-loop experiment produces near-optimal ESO gains within seconds.

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Section: Control Plant Description and Conventional Robust Control De...mentioning

confidence: 99%

Section: Neural Network-based Performance Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tuning of extended state observer with neural network-based control performance assessment

Kicki¹,

Łakomy²,

Lee³

2021

Preprint

View full text Add to dashboard Cite

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“…Xiong et al adopted the control method of chatter compensation for the flutter problem in the system, which reduced the oscillation caused by the friction force of the system and improved the control performance of the system [27]. Brayden DeBoon used a linear extended state observer to estimate the unmodeled interference and dynamic load, and designed a multi-input active disturbance rejection compensation controller on this basis [28]. Lu et al proposed a modified LuGre model and designed an adaptive control law combined with friction compensation to compensate for nonlinear friction effectively [29].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear electromagnetic force analysis and compensation control of electromagnetic linear actuator

Yu,

Tan,

Yan

et al. 2023

Phys. Scr.

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Nonlinear electromagnetic force is a common phenomenon in electromagnetic linear actuators (EMLA). This nonlinearity limits the application in high-precision control systems. For facilitating the controller design, the influencing factors of nonlinear electromagnetic force were analyzed, and the quadratic polynomial with unknown weights was designed to approximate the nonlinear relationship among electromagnetic force, excitation current and displacement. An adaptive integral robust control algorithm based on electromagnetic force compensation (AIRC-FC) was designed, which combined the adaptive control law of electromagnetic force nonlinear compensation, stable feedback and error signal continuous integral robust control. The tracking performance and the adaptability of the EMLA with and without compensation control were analyzed under different loads. The results show that AIRC-FC improve the tracking performance of the EMLA effectively, and maintain high control accuracy under different loads.

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“…Mahmoudabadi et al have utilized an improved particle swarm optimization (PSO) algorithm to optimize the decoupling sliding-mode control of a ball and beam system [32]. In [33], a genetic algorithm has been employed to optimize the design of a multi-input active disturbance rejection controller. In [34], a simulated annealing method has been introduced to solve the motor efficiency optimization problem of electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Development of Optimized Cooperative Control Based on Feedback Linearization and Error Port-Controlled Hamiltonian for Permanent Magnet Synchronous Motor

Zhao¹,

Wang

2021

IEEE Access

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The conflict between dynamic rapidity and steady-state accuracy is a crucial factor hindering the performance improvement of motor control system. To overcome the issue, this article proposes an optimized cooperative control combining feedback linearization (FBL) and error port-controlled Hamiltonian (EPCH) for permanent magnet synchronous motor (PMSM). First, FBL and EPCH are separately designed to obtain good dynamic and steady-state performances. Then, considering the individual advantages of FBL and EPCH, a cooperative strategy based on the real-time position error is applied to realize the smooth switching between the two methods, so that each method is utilized efficiently within the corresponding operating range. In addition, the particle swarm optimization (PSO) algorithm is introduced to properly select the controller parameters. Thus, an optimized cooperative control method, which takes into account both fast dynamic response and high steady-state precision, is developed for PMSM drives. The experimental results are finally given to illustrate the effectiveness and superiority of the proposed method.

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Multi-objective gain optimizer for a multi-input active disturbance rejection controller: Application to series elastic actuators

Cited by 12 publications

References 18 publications

Tuning of extended state observer with neural network-based control performance assessment

Tuning of extended state observer with neural network-based control performance assessment

Nonlinear electromagnetic force analysis and compensation control of electromagnetic linear actuator

Development of Optimized Cooperative Control Based on Feedback Linearization and Error Port-Controlled Hamiltonian for Permanent Magnet Synchronous Motor

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