Traditional Linear Control Research Articles

Adaptive fuzzy sliding mode control for electric power steering system

The Electric power steering (EPS) system, a typical non-linear system, is easy to be influenced by parameters perturbation and disturbance of the road. Traditional linear control method based on a simplified linear model such as PID control cannot reach good dynamic performance. To reduce the influence of parameters perturbation and disturbance of the road and enhance the robustness of the system, an Adaptive fuzzy sliding mode control (AFSMC) method is proposed in this paper. First, fuzzy sliding mode control is employed to enhance the dynamic performance of the system. Then, to improve the precision of the controller, genetic algorithm is used to optimize the control rules which are essential to fuzzy control. The experimental results on non-linear EPS model demonstrate that AFSMC is more stable than Sliding mode control (SMC) method and more efficient to the non-linear system than SFPID control method. They can also prove that AFSMC can provide a stable driving in the presence of parameters perturbation and disturbance of the road.

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

AQM scheme design for TCP network via Takagi‐Sugeno fuzzy method

This paper proposes a novel T‐S fuzzy control method instead of the traditional linear system control method to improve the TCP network performance. Thus a TCP network can be modeled as a T‐S fuzzy system, and by use of linear matrix inequality method and cone complementarity linearization algorithm, a fuzzy state feedback controller is provided while considering the problem of the asynchronous membership grades between the controller and the plant. Simulation results are presented to show that the proposed control approach can guarantee the asymptotical stability of the studied system and the desired queue size. © 2016 Wiley Periodicals, Inc. Complexity 21: 606–612, 2016

Adaptive robust disturbance compensating control for a servo system in the presence of both friction and deadzone

An adaptive robust control that does not need sophisticated plant modeling work is proposed for precise output positioning of a servo system in the presence of both friction and deadzone nonlinearities. It is difficult to achieve effective motion control by traditional linear control methodology for these types of nonlinearities, without the aid of a proper compensation scheme for nonlinearity. In this study, dynamic friction is modeled by a Tustin friction model, and inverse deadzone method is adopted to compensate deadzone effect. The adaptive laws of the unknown system dynamic parameters, friction and deadzone, are derived. Furthermore, a robust control method with funnel control is proposed to compensate for unmodeled and estimation errors. The boundedness and convergence of the closed-loop system are ensured by a Lyapunov stability analysis. The performance of the proposed control scheme is verified through experiments on the XY table servo system and the robotic manipulator.

RBF neural network-based sliding mode vibration control of a flexible cantilever plate using laser displacement measurement

This paper is concerned with active vibration control of a flexible piezoelectric cantilever plate using a nonlinear radial basis neural network sliding mode control (RBFNN-SMC) algorithm and laser displacement measurement. In order to decouple the low-frequency vibration signals of the bending and torsional modes on measurement, two laser displacement sensors are used. The decoupling method is provided. A hyperbolic tangent function is used instead of the sign function, and the chattering phenomenon is alleviated. Also, the RBFNN is utilized to adjust the switching control gain adaptively to balance the chattering phenomenon and the control effect. The controllers for bending and torsional modes are designed independently. Experimental setup of the flexible piezoelectric cantilever plate with two laser displacement sensors is constructed. Experiments on vibration measurement and control are conducted by using the decoupling method and the designed controller, compared with the classical proportional and derivative (PD) control algorithm. The experimental results demonstrate that the proposed method can decouple the low-frequency bending and torsional vibration signals on measurement. Furthermore, the designed nonlinear RBFNN-SMC can suppress both the bending and torsional vibrations more quickly than the traditional linear PD controller, especially for the small amplitude residual vibration.

Implementing Nonlinear Feedback Controllers Using DNA Strand Displacement Reactions.

We show how an important class of nonlinear feedback controllers can be designed using idealized abstract chemical reactions and implemented via DNA strand displacement (DSD) reactions. Exploiting chemical reaction networks (CRNs) as a programming language for the design of complex circuits and networks, we show how a set of unimolecular and bimolecular reactions can be used to realize input-output dynamics that produce a nonlinear quasi sliding mode (QSM) feedback controller. The kinetics of the required chemical reactions can then be implemented as enzyme-free, enthalpy/entropy driven DNA reactions using a toehold mediated strand displacement mechanism via Watson-Crick base pairing and branch migration. We demonstrate that the closed loop response of the nonlinear QSM controller outperforms a traditional linear controller by facilitating much faster tracking response dynamics without introducing overshoots in the transient response. The resulting controller is highly modular and is less affected by retroactivity effects than standard linear designs.

Lyapunov Based Control Algorithm for Seismically Excited Buildings

Several different seismic active control algorithms have been proposed in the last decades, most of these studies are based on the applications of the traditional linear quadratic regulator control (LQR). This paper develops a new control algorithm for SDOF structures based in Lyapunov method. This algorithm uses Lyapunov’s direct approach for stability analysis in design of feedback controller. The approach requires the use of Lyapunov function candidate, which must be a positive definite function of the states of the system. The controller is designed so as to make the derivative of the Lyapunov function negative semi-definite. Numerical simulations using one story frame structure modeled as shear building structure subjected to earthquake excitations have been performed to evaluate the effectiveness of the proposed algorithm.

Biomolecular implementation of a quasi sliding mode feedback controller based on DNA strand displacement reactions.

A fundamental aim of synthetic biology is to achieve the capability to design and implement robust embedded biomolecular feedback control circuits. An approach to realize this objective is to use abstract chemical reaction networks (CRNs) as a programming language for the design of complex circuits and networks. Here, we employ this approach to facilitate the implementation of a class of nonlinear feedback controllers based on sliding mode control theory. We show how a set of two-step irreversible reactions with ultrasensitive response dynamics can provide a biomolecular implementation of a nonlinear quasi sliding mode (QSM) controller. We implement our controller in closed-loop with a prototype of a biological pathway and demonstrate that the nonlinear QSM controller outperforms a traditional linear controller by facilitating faster tracking response dynamics without introducing overshoots in the transient response.

Kalman decomposition for Boolean control networks

The Kalman decomposition problem for Boolean control networks (BCNs) is solved completely. A necessary and sufficient condition, described by vertex partitions of digraphs, is proposed for the Kalman decomposition. And, an algorithm is designed to compute the logical coordinate transformation that results in the Kalman decomposition. Finally, two examples are presented. One example illustrates the procedure of realizing the Kalman decomposition. The other one reveals that not all the BCNs can be transformed into the Kalman decomposition form. This is an essential property different from that of the traditional linear control systems.

Interactive Disturbance Observer Based Filtered PID Controller Design

The paper shows Matlab based interactive tool for noise attenuation motivated filtered PD and disturbance observer based filtered PID controller performance analyses and design. The user can set several control loop parameters (plant model parameters and their uncertainty, measurement noise, controller, filter parameters) and evaluate various performance measures. The comparison with traditional linear PID controller design methods can be made.

Multi-phase robust position control scheme for synchronous reluctance motor

In the recent years the synchronous reluctance motor (SynRM) has received much attention due to its desirable controllability and simple structure. In this paper, in order to improve the position control performance of a three phase SynRM, a control scheme is proposed. In the proposed controller design, the smooth operation of the traditional linear controllers and the robustness of variable structure controllers have been considered. The proposed controller eliminates the drawbacks of traditional linear control schemes and conventional variable structure control scheme, such as high frequency chattering at the same time. Yet, their advantages such as smooth operation and robustness are preserved as a big merit. The experimental results clearly demonstrate the superiority of the proposed controller over conventional control schemes.

Multiphase Robust Position Control Scheme for Synchronous Reluctance Motor

In the recent years the synchronous reluctance motor (SynRM) has received much attention due to its desirable controllability and simple structure. In this paper, in order to improve the position control performance of a three phase SynRM, a control scheme is proposed. In the proposed controller design, the smooth operation of the traditional linear controllers and the robustness of variable structure controllers have been considered. The proposed controller eliminates the drawbacks of traditional linear control schemes and conventional variable structure control scheme, such as high frequency chattering at the same time. Yet, their advantages such as smooth operation and robustness are preserved as a big merit. The experimental results clearly demonstrate the superiority of the proposed controller over conventional control schemes.

Research of Model-Free Adaptive Control System Based on Neural Network

With the development of industry, the control system is more and more complex. For the nonlinear problems which can’t be solved by the traditional linear control system used now, it uses the model-free adaptive control system based on the neural network to effectively solve them. In this paper, it firstly makes a detailed analysis on the neural network, describing the neuron, the BP network and the training of neural network; then talks about the model-free adaptive control system, analyzing the structure, characteristics and algorithm of the system; and finally gives the core code of the model-free adaptive control system of the neural network. This paper provides positive effect to the industrial control staff and artificial intelligence researchers.

SDRE based Leader-Follower Formation Control of Multiple Mobile Robots

<span style="font-family: T3Font_6; font-size: xx-small;"><span style="font-family: T3Font_6; font-size: xx-small;"><em> <span style="font-size: small;"> </span></em><p class="MsoNormal" style="margin: 0cm 0cm 0pt; text-align: justify;"><em><em style="mso-bidi-font-style: normal;"><span style="font-size: 11pt; mso-bidi-font-size: 10.0pt; mso-ansi-language: EN-US;" lang="EN-US">Formation control of multiple mobile robots is relatively a new area of robotics and increase the control performance and advantages of multiple mobile robots systems. <a name="OLE_LINK72">In this work we present a study concerning the modeling and formation control of a robotic system composed by two mobile robots, where one robot is the leader and the other is follower</a></span></em><em style="mso-bidi-font-style: normal;"><span style="font-size: 11pt; mso-bidi-font-size: 10.0pt; mso-ansi-language: EN-US;" lang="EN-US">. The system is a nonlinear dynamical system and cannot be controlled by traditional linear control techniques. The control strategy proposed is the SDRE (State-Dependent Riccati Equation) method. Simulations results with the software Matlab show the efficiency of the control method.</span></em></em></p><span style="font-size: small;"> </span></span></span>

Multiconverter Medium Voltage DC Power Systems on Ships: Constant-Power Loads Instability Solution Using Linearization via State Feedback Control

Bus voltage stability is a key issue in future medium-voltage DC (MVDC) power systems on ships. The presence of high-bandwidth controlled load converters (Constant Power Load, CPL) may induce voltage instabilities. A control design procedure is presented which starts at the modeling level and comes to control implementation. A control method based on a Linearization via State Feedback (LSF), is proposed to face the CPL destabilizing effect and to ensure the MVDC bus voltage stability. A multiconverter shipboard DC grid is analyzed by means of a new comprehensive model, which is able to capture the overall behavior in a second-order nonlinear differential equation. Exploiting DC-DC converters that interface power sources to the bus, LSF technique is able to compensate for system nonlinearities, obtaining a linear system. Then, traditional linear control techniques can be applied to obtain a desired pole placement. With reference to system parameters mismatch, LSF control design is verified by means of a sensitivity analysis, evaluating the possibility of an over-linearization strategy. Time-domain numerical simulations are used to validate the proposed control, in presence of relevant perturbations by means of a two-way comparison (average value model and detailed switching model).

Photovoltaic Grid-Connected Micro-Inverter Based on Self-Tuning Fuzzy-PI Controller

This paper introduces a kind of grid-connected micro-inverter system. Photovoltaic micro-inverter system is a highly nonlinear and time-varying system. Compared to traditional linear control strategy, Fuzzy Controller has better robustness and stability in the changing or non-linear application. Matlab simulation model for current tracking PV Grid-connected Micro-inverter was completed based on the Fuzzy self-tuning PI controller and the control strategy was tested on the micro-inverter prototype. The resu1ts show that this control strategy can effectively reduce the tracking error of the net current, and improve the system’s dynamic response.

Bioreactor Control Using Fuzzy Logic Controllers

Bioreactors are characterized by high nonlinearities and are often subjected to parameter uncertainties and disturbances. The control of such processes is often difficult to achieve with traditional linear control techniques. In the present work, a Fuzzy logic controller is designed in two versions to a Bioreactor which exhibits input multiplicities in dilution rate on productivity. Fuzzy controller and Fuzzy tuned PI controller is designed to translate the information obtained from the operator’s experiences for designing an automatic control system The Performance of proposed Fuzzy logic controller versions and conventional PI controller have been analyzed and evaluated. The two Fuzzy controller versions provide stable and faster responses than conventional PI controller. Thus, Fuzzy control is found to overcome the control problems of PI controller due to the input multiplicities near optimal productivity. It is interesting to note that the present fuzzy logic controller is giving superior performance. The process is tested with the MATLAB/SIMULINK and Fuzzy Logic Toolbox. The simulation results were presented which illustrate the validity of the method.

LSPV+7, a branch-point-tolerant reconstructor for strong turbulence adaptive optics.

Optical wave propagation through long paths of extended turbulence presents unique challenges to adaptive optics (AO) systems. As scintillation and branch points develop in the beacon phase, challenges arise in accurately unwrapping the received wavefront and optimizing the reconstructed phase with respect to branch cut placement on a continuous facesheet deformable mirror. Several applications are currently restricted by these capability limits: laser communication, laser weapons, remote sensing, and ground-based astronomy. This paper presents a set of temporally evolving AO simulations comparing traditional least-squares reconstruction techniques to a complex-exponential reconstructor and several other reconstructors derived from the postprocessing congruence operation. The reconstructors' behavior in closed-loop operation is compared and discussed, providing several insights into the fundamental strengths and limitations of each reconstructor type. This research utilizes a self-referencing interferometer (SRI) as the high-order wavefront sensor, driving a traditional linear control law in conjunction with a cooperative point source beacon. The SRI model includes practical optical considerations and frame-by-frame fiber coupling effects to allow for realistic noise modeling. The "LSPV+7" reconstructor is shown to offer the best performance in terms of Strehl ratio and correction stability-outperforming the traditional least-squares reconstructed system by an average of 120% in the studied scenarios. Utilizing a continuous facesheet deformable mirror, these reconstructors offer significant AO performance improvements in strong turbulence applications without the need for segmented deformable mirrors.

Decoupling control and zero dynamics stabilization for shunt hybrid active power filter

An inverse system method based optimal control strategy was proposed for the shunt hybrid active power filter (SHAPF) to enhance its harmonic elimination performance. Based on the inverse system method, the d-axis and q-axis current dynamics of the SHAPF system were decoupled and linearized into two pseudolinear subsystems. Then, an optimal feedback controller was designed for the pseudolinear system, and the stability condition of the resulting zero dynamics was presented. Under the control strategy, the current dynamics can asymptotically converge to their reference states and the zero dynamics can be bounded. Simulation results show that the proposed control strategy is robust against load variations and system parameter mismatches, its steady-state performance is better than that of the traditional linear control strategy.

Novel design model for the stator currents subsystem of induction motors

The stator currents subsystem is a vital element of many high-performance induction motor control schemes. While there are several control techniques available for this subsystem, traditional linear controllers are still widely used because of its simplicity and proven effectiveness. However, the traditional simplified design-model lacks important information, necessary for the design of high-performance and robust controllers. In this article a novel design-model intended for linear controller formulation and evaluation is developed. This new mathematical representation captures several elements which are missing in the traditional representation, maintaining at the same time a similar level of simplicity. Along the derivation of this new representation several models of decreasing complexity and comprehensiveness are also presented together with a critical classification. This classification is intended to aid the designer in choosing the appropriate mathematical representation for specific purposes. Finally, the article is accompanied with experimental findings which illustrate the use of the proposed model.