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Abstract

This paper deals with two control algorithms which utilize learning of their models’ parameters. An adaptive and artificial neural network control techniques are described and compared. Both control algorithms are implemented in MATLAB and Simulink environment, and they are used in the simulation of a postion control of the LWR 4+ manipulator subjected to unknown disturbances. The results, showing the better performance of the artificial neural network controller, are shown. Advantages and disadvantages of both controllers are discussed. The usefulness of the learning algorithms for the control of LWR 4+ robots is discussed. Preliminary experiments dealing with dynamic properties of the two LWR 4+ robots are reported.

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Bibliography

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Authors and Affiliations

Łukasz Woliński
1

  1. Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology, Poland.
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Abstract

Passive noise reduction methods require thick and heavy barriers to be effective for low frequencies and those clasical ones are thus not suitable for reduction of low frequency noise generated by devices. Active noise-cancelling casings, where casing walls vibrations are actively controlled, are an interesting alternative that can provide much higher low-frequency noise reduction. Such systems, compared to classical ANC systems, can provide not only local, but also global noise reduction, which is highly expected for most applications. For effective control of casing vibrations a large number of actuators is required. Additionally, a high number of error sensors, usually microphones that measure noise emission from the device, is also required. All actuators have an effect on all error sensors, and the control system must take into account all paths, from each actuator to each error sensor. The Multiple Error FXLMS has very high computational requirements. To reduce it a Switched-Error FXLMS, where only one error signal is used at the given time, have been proposed. This, however, significantly reduces convergence rate. In this paper an algorithm that uses multiple errors at once, but not all, is proposed. The performance of various algorithm variants is compared using simulations with the models obtained from real active-noise cancelling casing.

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Authors and Affiliations

Krzysztof Mazur
Stanislaw Wrona
Anna Chraponska
Jaroslaw Rzepecki
Marek Pawelczyk
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Abstract

Noise control has gained a lot of attention recently. However, presence of nonlinearities in signal paths for some applications can cause significant difficulties in the operation of control algorithms. In particular, this problem is common in structural noise control, which uses a piezoelectric shunt circuit. Not only vibrating structures may exhibit nonlinear characteristics, but also piezoelectric actuators. In this paper, active device casing is addressed. The objective is to minimize the noise coming out of the casing, by controlling vibration of its walls. The shunt technology is applied. The proposed control algorithm is based on algorithms from a group of soft computing. It is verified by means of simulations using data acquired from a real object.

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Authors and Affiliations

Sebastian Kurczyk
Marek Pawełczyk
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Abstract

This work describes a new study to achieve a combination of modified function projective synchronization between three different chaotic systems through adaptive control. Using the Lyapunov function theory, the asymptotic stability of the error dynamics is obtained and discussed. Further, we set some appropriate initial conditions for the state variables and assigning specific values to the parameters and obtain the graphical results, which shows the efficiencies of the new method. Finally, we summarized our work with conclusion and references.

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Authors and Affiliations

N.A. Almohammadi
E.O. Alzahrani
M.M. El-Dessoky
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Abstract

In this paper, the issue related to control of the plant with nonconstant parameters is addressed. In order to assure the unchanged response of the system, an adaptive state feedback speed controller for permanent magnet synchronous motor is proposed. The model-reference adaptive system is applied while the Widrow-Hoff rule is used as adjustment mechanism of controller’s coefficients. Necessary modifications related to construction of the cost function and formulas responsible for adjustment of state feedback speed controller’s coefficients are depicted. The impact of adaptation gain, which is the only parameter in proposed adjustment mechanism, on system behaviour is experimentally examined. The discussion about computational resources consumption of the proposed adaptation algorithm and implementation issues is included. The proposed approach is utilized in numerous experimental tests on modern SiC based drive with nonconstant moment of inertia. Comparison between adaptive and nonadaptive control schemes is also shown.

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Authors and Affiliations

R. Szczepanski
T. Tarczewski
L.M. Grzesiak
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Abstract

The paper presents a method for designing a neural speed controller with use of Reinforcement Learning method. The controlled object is an electric drive with a synchronous motor with permanent magnets, having a complex mechanical structure and changeable parameters. Several research cases of the control system with a neural controller are presented, focusing on the change of object parameters. Also, the influence of the system critic behaviour is researched, where the critic is a function of control error and energy cost. It ensures long term performance stability without the need of switching off the adaptation algorithm. Numerous simulation tests were carried out and confirmed on a real stand.

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Authors and Affiliations

T. Pajchrowski
P. Siwek
A. Wójcik
ORCID: ORCID
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Abstract

The paper describes a nonlinear controller design technique applied to a servo drive in the presence of hard state constraints. The approach presented is based on nonlinear state-space transformation and adaptive backstepping. It allows us to impose hard constraints on the state variables directly and to achieve asymptotic tracking of any reference trajectory inside the constraints, despite unknown plant parameters. Two control schemes (with and without integral action) are derived, investigated and then compared. Several examples demonstrate the main features of the design procedure and prove that it may be applied in case of motion control problems in electric drive automation.

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Authors and Affiliations

J. Kabziński
P. Mosiołek
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Abstract

The paper introduces Extended Identification-Based Predictive Control (EIPC), which is a novel control method developed for the problem of adaptive impact mitigation. The model-based approach utilizing the paradigm of Model Predictive Control is combined with sequential identification of selected system parameters and process disturbances. The elaborated method is implemented in the shock-absorber control system and tested under impact loading conditions. The presented numerical study proves the successful and efficient adaptation of the absorber to unknown excitation conditions as well as to unknown force and leakage disturbances appearing during the process. The EIPC is used for both semi-active and active control of the impact mitigation process, which are compared in detail. In addition, the influence of selected control parameters and disturbance identification on the efficiency of the impact absorption process is assessed. As a result, it can be concluded that an efficient and robust control method was developed and successfully applied to the problem of adaptive impact mitigation.
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Authors and Affiliations

Cezary Graczykowski
1
ORCID: ORCID
Rami Faraj
1
ORCID: ORCID

  1. Institute of Fundamental Technological Research PAS, Pawi´nskiego 5B, 02-106 Warszawa, Poland
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Abstract

A novel 4-D chaotic hyperjerk system with four quadratic nonlinearities is presented in this work. It is interesting that the hyperjerk system has no equilibrium. A chaotic attractor is said to be a hidden attractor when its basin of attraction has no intersection with small neighborhoods of equilibrium points of the system. Thus, our new non-equilibrium hyperjerk system possesses a hidden attractor. Chaos in the system has been observed in phase portraits and verified by positive Lyapunov exponents. Adaptive backstepping controller is designed for the global chaos control of the non-equilibrium hyperjerk system with a hidden attractor. An electronic circuit for realizing the non-equilibrium hyperjerk system is also introduced, which validates the theoretical chaotic model of the hyperjerk system with a hidden chaotic attractor.
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Authors and Affiliations

Sundarapandian Vaidyanathan
Sajad Jafar
Viet-Thanh Pham
Ahmad Taher Azar
Fawaz E. Alsaadi
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Abstract

For many adaptive noise control systems the Filtered-Reference LMS, known as the FXLMS algorithm is used to update parameters of the control filter. Appropriate adjustment of the step size is then important to guarantee convergence of the algorithm, obtain small excess mean square error, and react with required rate to variation of plant properties or noise nonstationarity. There are several recipes presented in the literature, theoretically derived or of heuristic origin.

This paper focuses on a modification of the FXLMS algorithm, were convergence is guaranteed by changing sign of the algorithm steps size, instead of using a model of the secondary path. A TakagiSugeno-Kang fuzzy inference system is proposed to evaluate both the sign and the magnitude of the step size. Simulation experiments are presented to validate the algorithm and compare it to the classical FXLMS algorithm in terms of convergence and noise reduction.

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Authors and Affiliations

Sebastian Kurczyk
Marek Pawelczyk
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Abstract

Vibrating plates have been recently used for a number of active noise control applications. They are resistant to difficult environmental conditions including dust, humidity, and even precipitation. However, their properties significantly depend on temperature. The plate temperature changes, caused by ambient temperature changes or plate heating due to internal friction, result in varying response of the plate, and may make it significantly different than response of a fixed model. Such mismatch may deteriorate performance of an active noise control system or even lead to divergence of a model-based adaptation algorithm.

In this paper effects of vibrating plate temperature variation on a feedforward adaptive active noise reduction system with the multichannel Filtered-reference LMS algorithm are examined. For that purpose, a thin aluminum plate is excited with multiple Macro-Fiber Composite actuators. The plate temperature is forced by a set of Peltier cells, what allows for both cooling and heating the plate. The noise is generated at one side of the plate, and a major part of it is transmitted through the plate. The goal of the control system is to reduce sound pressure level at a specified area on the other side of the plate.

To guarantee successful operation of the control system in face of plate temperature variation, a gain-scheduling scheme is proposed to support the Filtered-reference LMS algorithm.

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Authors and Affiliations

Krzysztof Mazur
Marek Pawełczyk
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Abstract

In this paper, the adaptive control based on symbolic solution of Diophantine equation is used to suppress circular plate vibrations. It is assumed that the system to be regulated is unknown. The plate is excited by a uniform force over the bottom surface generated by a loudspeaker. The axially-symmetrical vibrations of the plate are measured by the application of the strain sensors located along the plate radius, and two centrally placed piezoceramic discs are used to cancel the plate vibrations. The adaptive control scheme presented in this work has the ability to calculate the error sensor signals, to compute the control effort and to apply it to the actuator within one sampling period. For precise identification of system model the regularized RLS algorithm has been applied. Self-tuning controller of RST type, derived for the assumed system model of the 4th order is used to suppress the plate vibration. Some numerical examples illustrating the improvement gained by incorporating adaptive control are demonstrated.

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Authors and Affiliations

Lucyna Leniowska
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Abstract

Vibrating plates can be used in Active Noise Control (ANC) applications as active barriers or as secondary sources replacing classical loudspeakers. The system with vibrating plates, especially when nonlinear MFC actuators are used, is nonlinear. The nonlinearity in the system reduces performance of classical feedforward ANC with linear control filters systems, because they cannot cope with harmonics generated by the nonlinearity. The performance of the ANC system can be improved by using nonlinear control filters, such as Artificial Neural Networks or Volterra filters. However, when multiple actuators are mounted on a single plate, which is a common practice to provide effective control of more vibration modes, each actuator should be driven by a dedicated nonlinear control filter. This significantly increases computational complexity of the control algorithm, because adaptation of nonlinear control filters is much more computationally demanding than adaptation of linear FIR filters. This paper presents an ANC system with multiple actuators, which are driven with a single nonlinear filter. To avoid destructive interference of vibrations generated by different actuators the control signal is filtered by appropriate separate linear filters. The control system is experimentally verified and obtained results are reported.
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Authors and Affiliations

Krzysztof Mazur
Marek Pawełczyk

Abstract

We study an elegant snap system with only one nonlinear term, which is a quadratic nonlinearity. The snap systemdisplays chaotic attractors,which are controlled easily by changing a system parameter. By using analysis, simulations and a real circuit, the dynamics of such a snap system has been investigated. We also investigate backstepping based adaptive control schemes for the new snap system with unknown parameters.

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Abstract

DC motors have wide acceptance in industries due to their high efficiency, low costs, and flexibility. The paper presents the unique design concept of a multi-objective optimized proportional-integral-derivative (PID) controller and Model Reference Adaptive Control (MRAC) based controllers for effective speed control of the DC motor system. The study aims to optimize PID parameters for speed control of a DC motor, emphasizing minimizing both settling time (Ts ) and % overshoot (% OS) of the closed-loop response. The PID controller is designed using the Ziegler Nichols (ZN) method primarily subjected to Taguchi-grey relational analysis to handle multiple quality characteristics. Here, the Taguchi L9 orthogonal array is defined to find the process parameters that affect Ts and %OS. The analysis of variance shows that the most significant factor affecting Ts and %OS is the derivative gain term. The result also demonstrates that the proposed Taguchi-GRA optimized controller reduces Ts and %OS drastically compared to the ZN-tuned PID controller. This study also uses MRAC schemes using the MIT rule, Lyapunov rule, and a modified MIT rule. The DC motor speed tracking performance is analyzed by varying the adaptation gain and reference signal amplitude. The results also revealed that the proposed MRAC schemes provide desired closed-loop performance in real-time in the presence of disturbance and varying plant parameters. The study provides additional insights into using a modified MIT rule and the Lyapunov rule in protecting the response from signal amplitude dependence and the assurance of a stable adaptive controller, respectively.
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Authors and Affiliations

Mary Ann George
1
ORCID: ORCID
Dattaguru V. Kamat
1
ORCID: ORCID

  1. Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal – 576104, Udupi District, Karnataka State, India
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Abstract

The selection of a reference model (RM) for a Model-Reference Adaptive Control is one of the most important aspects of the synthesis process of the adaptive control system. In this paper, the four different implementations of RM are developed and investigated in an adaptive PMSM drive with variable moment of inertia. Adaptation mechanisms are based on the Widrow-Hoff rule (W-H) and the Adaptation Procedure for Optimization Algorithms (APOA). Inadequate order or inaccurate approximation of RM for the W-H rule may provide poor behavior and oscillations. The results prove that APOA is robust against an improper selection of RM and provides high-performance PMSM drive operation.
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Authors and Affiliations

Rafał Szczepański
Tomasz Tarczewski
Lech Grzesiak
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Abstract

The genesis of both coherent structures and reactive flow control strategies is explored. Futuristic control systems that utilize mi-crosensors and microactuators together with artificial intelligence to target specific coherent structures in a transitional or turbulent flow are considered. Of possible interest to the readers of this journal is the concept of smart wings, to be briefly discussed early in the article.

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Authors and Affiliations

M. Gad-El-Hak

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