Earticle

본 연구에서는 자동차 엔진에 적용되는 고무마운트의 성능을 극대화하여 시동초기 진동특성를 개선하기 위해 능동형 하이브리드 마운트를 제안하고 진동절연 성능을 실험적으로 고찰하고자 한다. 제안된 하이브리드 마운트는 수동 형 고무요소와 능동형 압전작동기로 구성되었으며, 동적 특성과 제어력의 실험적 고찰을 통해 하이브리드 마운트가 제 작되었다. 수직방향의 진동을 고려하여 관성질량을 갖는 압전-고무 하이브리드 마운트의 동적 지배방정식을 수학적으 로 모델링하였으며 상태 공간 방정식으로 표현하였다. 본 연구에서는 진동을 절연하기 위해 강건한 슬라이딩 모드 제어 기가 구성되어 진동제어 실험에 적용되었다. 마지막으로 넓은 주파수 영역에서 진동제어 성능을 실험적으로 고찰하였으 며 주파수 영역에서의 전달율과 시간영역에서 진동절연 성능을 평가하였다.

A hybrid mount featuring rubber element and piezoelectric actuator is devised to reduce vibration when starting a vehicle engine. As a first step, a passive mount adopting rubber element is manufactured and its dynamic characteristics are experimentally evaluated. After evaluating dynamic characteristics of the manufactured inertial piezoelectric actuator, the proposed hybrid mount is then established by integrating the piezoelectric actuator with the rubber element for performance improvement at non-resonant high frequencies. A mathematical model of the established active vibration control system is formulated and expressed in the state space form. Subsequently, sliding mode controller (SMC) is designed to attenuate the vibration transmitted from the base excitation. Finally, control performances of the proposed hybrid mount are evaluated such as transmissibility in frequency domain and time responses.

Sliding mode control(SMC) are carried out in this literature. And to make the controllers perform better, fuzzy logic was chosen,it makes PID controller auto-tuning parameters and reduced the chattering problem of sliding mode control. Since SMC take error and derivative of error as inputs, after comparison some results are obtained.PID controller response faster yet sliding mode control is much steadier. However certain problems cannot be ignored that the chattering phenomenal cannot be reduced entirely and this motion may hurt the machine; this project only considered a simple system, there is no guarantee PID can work as well as in this case for a much more complex system. MATLAB simulink was the main approach to obtain the performance of the two controllers: to observe the control output of the two controllers, electric circuit and special controllers are designed and tested in MATLAB.

Recent development of robot technology is revolutionizing the medical field. The concept of using robot assistance in medical surgery has been receiving more and more recognition throughout the world. Robot-assisted surgery has the advantage of reducing surgeons' hand tremor, decreasing post-operative complications, reducing patients' pains, and increasing operation dexterity inside the patients' body. Robotic assistants have been broadly used in many medical fields such as orthopedics, neurology, urology and cardiology, and robot assisted surgery is keeping expanding its influences in more general medical field. Refer to this research, auxiliary sliding variable sliding mode controller is proposed for multi DOF joint with application in surgical robot manipulator. The main problem in this research is design robust chattering free sliding mode controller. The chattering phenomenon problem is reduced in certain/uncertain system by using auxiliary sliding variable. The simulation results exhibit that the sliding mode controller with auxiliary sliding variable works well in certain and uncertain condition.

In this paper, a new approach to the super-twisting sliding mode control of uncertain systems is proposed. The idea behind this control scheme is to utilize an adaptive sliding surface function, in which the slope of the surface is updated on-line using a simple single input-single output fuzzy logic inference system so that the sliding surface is rotated in such a direction that the tracking performance of the system under control is improved. Computer simulations are performed on a system with parameter uncertainties and external disturbances. The results are compared with a conventional super twisting sliding mode controller with a fixed sliding surface. The results have shown the improved performance of the proposed control approach in terms of a decrease in the reaching and settling times and robustness to parameter uncertainties and disturbances as compared to the conventional super-twisting sliding mode controller with a fixed sliding surface. Moreover, the proposed control scheme is very simple and easy to implement.

A new type controller, recurrent fuzzy neural networks-fuzzy-sliding mode controller (FRNN-FSMC), is developed for a class of large-scale systems with unknown bounds of high-order interconnections and disturbances. The main purpose is to eliminate the chattering phenomenon and to overcome the problem of the equivalent control computation. The FRNN-FSMC, which incorporates the recurrent fuzzy neural network (RFNN), fuzzy logic controller (FLC) and the SMC, can eliminate chattering using a fixed boundary layer around the switch surface. Within the boundary layer, where the FLC is applied, the chattering phenomenon, which is inherent in a SMC, is avoided by smoothing the switch signal. Moreover, to compute the equivalent controller, a feed-forward RFNN is used. The stability of the whole system is analyzed via the Lyapunov methodology. In this study, we propose an effective method to select some key controller parameters in an optimal manner by using the genetic algorithm (GA), so that a high performance of the overall system's response can be achieved. The effectiveness and efficiency of the proposed controller and optimization method were tested using highly interconnected nonlinear systems as examples.

This paper presents the design of a sliding mode control of a buck converter using the artificial intelligence techniques. According to the advantages of the averaging model derived from the generalized state-space averaging method, it was used as the objective function for the searching process. The results from the simulation and the experiment show that the controllers designed from the proposed method can provide the best output response compared with those designed from the conventional method. The co-operation between the artificial intelligence algorithm and the averaging model is suitable to design the controller of power converters with a good performance.

This paper presents a sliding mode controller based Neural Networks for energy conservation in Induction motor. A Multi layer Neural Network trained by back propagation learning algorithm is used to compensate for the system uncertainties and provide energy efficient operation for a wide range of loads. The trained Neural Network controls the stator voltage for optimal efficiency under all operating conditions. Simulations and experimental studies were performed to establish the suggestion made in this paper. The proposed algorithm was simulated using Matlab/Simulink and the results show that the performance of the control scheme is robust, the chattering problem is solved and efficiency improvement is achieved.

A fuzzy sliding mode control method is proposed to eliminate the interference that suspended floating objects suffered in the on-ground low-gravity simulation system. Due to the change of sling swing-angle that caused by the external interference is weak, the time integral of the sling swing-angle is taken as tracking errors. Meanwhile the values of angle and the angle differential should be converged to 0. A fuzzy sliding mode controller is designed according to the dynamic model. The traditional sliding mode control method and fuzzy control method are combined in this paper. The simulation results indicate that the servo platform is capable of tracking a floating object rapidly. And the servo platform is able to move in constant velocity synchronously with the floating object while the sling is kept upright. The feasibility of applying fuzzy sliding mode control method to the field of tracking suspension floating object is verified.

In industries, overhead cranes are commonly employed to lift and lower materials or to move them horizontally. A combining sliding mode control method is proposed for overhead crane systems in this paper. The ideas behind the combining sliding mode are as follows. First, an intermediate variable is introduced by dividing the system states into two groups. Then, a sliding surface is defined on basis of the intermediate variable. The control law is deduced from Lyapunonv direct method to asymptotically stabilize the sliding surface. The stability of the system states is also proven. Simulation results demonstrate the feasibility of the presented method through transport control of an overhead crane system.

In order to improve the system performance affected by the model uncertainties and loading moments, a robust tracking controller is proposed for electro-hydraulic servo system. Considering the conventional disturbance observer (DOB) lacks the enough ability in estimating the whole disturbances in electro-hydraulic servo system, one global sliding mode controller (GSMC) is proposed based on the inner-loop DOB. The GSMC not only suppresses the remainder disturbances which cannot be observed by DOB, but also realizes the position tracking of electro-hydraulic servo system. Simulation results present that, compared with other traditional control schemes, the proposed robust control scheme can ensure electro-hydraulic servo system possess higher tracking precision. Moreover, there is no obvious high-frequency chattering at control input, therefore it also possesses higher application value.

MAV (Micro Aerial Vehicle) is nonlinear plant, it is difficult to obtain stable control for MAV attitude due to uncertainties. The purpose of this paper is to propose one robust、stable control strategy for MAV to accommodate system uncertainties, variations, and external disturbances. In this paper, an interval type II fuzzy sliding-mode controller combined with PSO algorithm (ITIIFSMC-PSO) is proposed for MAV attitude control system. The proposed ITIIFSMC-PSO is a combination of the interval type II fuzzy logic control (ITIIFLC) and the sliding-mode control (SMC) which inherits the benefits of these two methods, furthermore, PSO algorithm was adopted to tune the type II fuzzy controller’s antecedent and consequent parameters for the plant control. The objective of the controller is to ensure the asymptotic stability of the closed-loop system. The Lyapunov stability method is adopted to verify the stability of the ITIIFSMC-PSO system. At last, a Quadrotor MAV with uncertainty and disturbance are adopted to illustrate the validity of the proposed method. The simulation results show that the ITIIFSMC-PSO achieves the best tracking performance in comparison with the interval type II Fuzzy logic controller (ITIIFLC), the interval type II fuzzy sliding-mode controller (ITIIFSMC).

In this paper, a model of a bearingless permanent magnet synchronous motor (BPMSM) is proposed, the expressions of radial suspension forces are derived, and an accurate model is established. Furthermore, decoupling and basic linearization are carried out for the radial suspension forces using the theory of differential geometry, and a sliding mode controller with fractional order based on a neural network is designed for the decoupled, pseudo-linear subsystem. Finally, a simulation experiment is conducted for the designed control system and the feasibility of this decoupling control approach is validated. The simulation results indicate that the application of the proposed control scheme to a bearingless permanent magnet synchronous motor modeled using differential geometry can achieve steady and independent control of radial suspension forces.

This paper focused on the design and implementation of a sliding mode controller with fuzzy logic tuning for depth control of an autonomous underwater vehicle (AUV). A fuzzy tuning approach to sliding mode control is employed for enhancing the tracking performance as well as reducing reaching time. The sliding surface can rotate or shift in the phase space in such a direction that the tracking behavior can be improved, which can be carried out using fuzzy tuning method. The reaching time and tracking error in the approaching phase can be significantly reduced. Experimental results indicate the effectiveness of the proposed controller in dealing with model uncertainties, nonlinearities of the vehicle dynamics and environmental disturbances caused by ocean currents and waves.

Uncertain or complicated systems are difficult to control. Modeling the system uncertainties is an especial topics in most of engineering field. On the other hand, since system has uncertainty, design stable and robust controller is crucial importance in control engineering. To solve this challenge nonlinear control technique is the best choice. Sliding mode control is one important type of robust control. Model imprecision may come from actual uncertainty about the plant or from a purposeful simplification of the system's dynamics. Modeling inaccuracies can cause strong adverse effects on the control design of nonlinear systems. For the class of systems to which it applies, sliding mode controller design provides a systematic approach to the problem of maintaining stability and consistent performance in the face of modeling imprecision. However, sliding mode controller is a robust and stable controller but it has an important challenge called, chattering phenomenon. This research focuses on the comparative between three methods to eliminate/reduce the chattering.

Design of a robust controller for multi input-multi output (MIMO) nonlinear uncertain dynamical system can be a challenging work. This research focuses on the design and analysis of a high performance chattering free PD plus PD partly sliding mode controller in presence of uncertainties. In this research, sliding mode controller is a robust and stable nonlinear controller, which selected to control of robot manipulator. The proposed approach effectively combines of design methods from switching sliding mode controller, and linear Proportional-Derivative (PD) control to improve the performance, stability and robustness of the sliding mode controller. To reduce the chattering with respect to stability and robustness; linear controller is added to the switching part of the sliding mode controller. The linear controller is to reduce the role of sliding surface slope and switching (sign) function. To improve the flexibility, design high speed and low cost controller, micro-electronic device (FPGA-Based) controller is introduced in this research. The proposed design is 30-bits FPGA-based controller for inputs and 35-bits for output. All joints of robot are used to test the controller in simulation environments, using VHDL code for the purpose of simulation in Xilinx. The maximum frequency in FPGA-based design is about 63.6 MHz and the delay time in this design is about 15.7 ns. It is observed that this controller is able to make as a fast response at 15.716 𝑛𝑠 clock period with 63.6 𝑀𝐻𝑍 of a maximum frequency and 4.407 𝑛𝑠 for minimum input arrival time after clock. From investigation and synthesis summary, 30.286 𝑛𝑠 for maximum input arrival time after clock with 33.018 𝑀𝐻𝑍 frequencies, this design has 15.716 𝑛𝑠 delays for each controller to 46 logic elements and the offset before CLOCK is 55.773 𝑛𝑠 for 132 logic gates.

A fuzzy integral sliding mode control method based on the finite time stable theory and integral sliding mode control theory is proposed to solve a class of complex network problems. The complex networks are expressed by T-S fuzzy models with bounded approximation errors by using the approximation capability of T-S fuzzy models. The paper proposes fuzzy dynamic integral sliding mode control scheme and the scheme is designed for the complex networks based on their T-S fuzzy approximation models. The simulation result shows that the trajectories of the closed-loop control system are kept on the sliding surface almost since the initial time. And the sliding motion of the closed-loop control system involving the original nonlinear system can be shown to be semi globally asymptotically stable. The controller can make system convergence in finite time and reduce the system buffeting effectively.

Sliding mode controller (SMC) is a highly significant nonlinear controller under condition of certain and partly uncertain dynamic parameters of system. This paper focused on design and implementation of a sliding mode controller for STATCOM. Intend for its application to wind farm connection to the grid. The proposed inverse system sliding mode controller based on nonlinear feedback linearization technology for system decoupling, which enhance the transient stability of wind farms and stabilizes the grid voltage about the given operating point when the fault occurs closer to the wind farms. The simulation results have shown the improved performance of the proposed controller has better control effectiveness and efficiency compared with the conventional controller of inverse system , the proposed controller on wind power system and which assist in improving capability of the wind farms to ride through disturbances, dynamic power flow control of the transmission lines, enhancing power oscillation damping under a sudden fault. Moreover, the proposed control scheme is very simple and easy to implement.

Most of nonlinear controllers need real time mobility operation so one of the most important devices which can be used to solve this challenge is Field Programmable Gate Array (FPGA). FPGA can be used to design a controller in a single chip Integrated Circuit (IC).Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a FPGA-based chattering free mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller is selected. Pure sliding mode controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the switching function in presence of mathematical error-based method instead of switching function method in pure sliding mode controller. The results demonstrate that the FPGA-based sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system. Pure sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied mathematical model-free tuning method to FPGA-based sliding mode controller for adjusting the sliding surface gain ( ). Since the sliding surface gain ( ) is adjusted by mathematical model free-based tuning method, it is nonlinear and continuous. In this research new is obtained by the previous multiple sliding surface slopes updating factor . FPGA-based Chattering free mathematical error-based tuning sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12). To have higher implementation speed with good performance TVSC is implemented on Spartan 3E FPGA using Xilinx software (controller computation time=30.2 ns, Max frequency=63.7 MHz and controller action frequency=33 MHZ).

Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a robust rule base fuzzy-based tuning fuzzy sliding mode controller for second order nonlinear system in presence of uncertainties. Rule base is one of the important key factors in design of fuzzy based tuning controller. In order to provide high performance nonlinear methodology, fuzzy sliding mode controller is selected. Fuzzy sliding mode controller can be used to control of partly unknown nonlinear dynamic parameters of nonlinear system. Error-based fuzzy sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied improve rule base fuzzy-based tuning method to error-based fuzzy sliding mode controller for adjusting the rule base in adaptive fuzzy-based tuning. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.4 second, steady state error = 1.3e-11 and RMS error=1.2e-11).