Top PDF Performance Comparisan Between Sliding Mode Control (Smc) And Pd-Pid Controllers For A Nonlinear Inverted Pendulum System

Performance Comparisan Between Sliding Mode Control (Smc) And Pd-Pid Controllers For A Nonlinear Inverted Pendulum System

Performance Comparisan Between Sliding Mode Control (Smc) And Pd-Pid Controllers For A Nonlinear Inverted Pendulum System

SMC is a method in modern control theory that uses state- space approach to analyze such a system. Using state-space methods it is relatively simple to work with a multi-output system [8].The typical structure of a sliding mode controller (SMC) is composed of a nominal part and additional terms to deal with model uncertainty. The way SMC deals with uncertainty is to drive the plants state trajectory onto a sliding surface and maintain the error trajectory on this surface for all subsequent times. The advantage of SMC is that the controlled system becomes insensitive to system disturbances. The sliding surface is defined such that the state tracking error converges to zero with input reference. With the perspective to achieve zero steady state error, Cao and Xu (2001) and Sam et al. (2002) have proposed the proportional integral sliding mode control (PISMC) in their studies [9]. The proportional factor in this controller gives more freedom in selecting some parameters matrices that will make the output response faster and the stability condition to be more easily satisfied. The proportional integral sliding surface equation can be represented as (14).
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Performance Comparison between Sliding Mode Control with PID Sliding Surface and PID Controller for an Electro-hydraulic Positioning System

Performance Comparison between Sliding Mode Control with PID Sliding Surface and PID Controller for an Electro-hydraulic Positioning System

In this study, the performance of SMC, P, PD and PID controllers are evaluated for position tracking control. The Lyapunov approach is used in theoretical analysis in developing the SMC with PID scheme and to ensure that the system is under stable condition. The numerical simulation study shows that the proposed controller provides better performance in tracking accuracy and time response. The control effort produced from SMC without the chattering effect also is practical to be used in real application. In conclusion, a simple control method without much control effort and better performance can be made with the SMC design based on PID sliding surface instead of using conventional PID controller.
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Sliding Mode Control of Inverted Pendulum with Decoupling Algorithm

Sliding Mode Control of Inverted Pendulum with Decoupling Algorithm

This paper presents a decoupling algorithm of sliding mode control on inverted pendulum. The decoupled method provides a simple way to achieve asymptotic stability for a nth -order nonlinear systems. The system dynamics of SMC and inverted pendulum systems are encapsulated in the algorithm form and analysed by MATLAB Simulations. The convergence of the proposed sliding mode control is verified by Lyapunov function to prove the stability of system. Numerical simulations of designed SMC control strategy for inverted pendulum demonstrate faster convergence, reduced disturbance in control input and overall robust performance.
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Self-elected inverted pendulum by high performance control technique

Self-elected inverted pendulum by high performance control technique

The improvement of fuzzy logic controller control technique are continuously be studied by researchers. (Chun-e et al. 2011) also introduced fuzzy logic controller in the system as the second controller. In this paper research, the researchers implement fuzzy logic controller as a second controller since the PID controller are less effective toward the performance and robustness. The two fuzzy logic controllers give the angle and position of the inverted pendulum completed control in seconds. (Yadav et al. 2011) proposed a design of a fuzzy logic as another controller to make the system more stable and robust. The developing several combination of fuzzy logic PD and PID give the better result in performance and robust of inverted pendulum than the two fuzzy logic controller. The result is considered in term of maximum overshoot, settling time and steady state error.
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Sliding-Mode Control for Transformation to an Inverted Pendulum Mode of a Mobile Robot With Wheel-Arms

Sliding-Mode Control for Transformation to an Inverted Pendulum Mode of a Mobile Robot With Wheel-Arms

Control system design for wheeled inverted pendulums has been intensively studied in the literature. Many of early studies adopt linear controllers [11]–[16] based on a linear approximation of a nonlinear model around an equilibrium point. A limitation of linear controllers is that they are not necessarily effective in cases where the system state is far from the equilibrium point, such as in our transformation problem. More recent studies have proposed various nonlinear control methods for wheeled inverted pendulums [17]–[37]. They can be classified by how to deal with the underactuation, which occurs mainly because both the velocity (or position) of the vehicle and the pitch angle of the body need to be controlled by a single actuator.
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Emotional Fuzzy Sliding-Mode Control for Unknown Nonlinear Systems

Emotional Fuzzy Sliding-Mode Control for Unknown Nonlinear Systems

Abstract The brain emotional learning model can be implemented with a simple hardware and processor; how- ever, the learning model cannot model the qualitative aspects of human knowledge. To solve this problem, a fuzzy-based emotional learning model (FELM) with structure and parameter learning is proposed. The mem- bership functions and fuzzy rules can be learned through the derived learning scheme. Further, an emotional fuzzy sliding-mode control (EFSMC) system, which does not need the plant model, is proposed for unknown nonlinear systems. The EFSMC system is applied to an inverted pendulum and a chaotic synchronization. The simulation results with the use of EFSMC system demonstrate the feasibility of FELM learning procedure. The main contri- butions of this paper are (1) the FELM varies its structure dynamically with a simple computation; (2) the parameter learning imitates the role of emotions in mammalians brain; (3) by combining the advantage of nonsingular ter- minal sliding-mode control, the EFSMC system provides very high precision and finite-time control performance; (4) the system analysis is given in the sense of the gradient descent method.
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Dual-mode model predictive control of an omnidirectional wheeled inverted pendulum

Dual-mode model predictive control of an omnidirectional wheeled inverted pendulum

The two-wheeled inverted pendulum, however, is well studied, and possesses similar dynamics to this platform. A number of review articles compare performance between different classical control techniques applied to the TWIP such as PID, LQR, and pole placement techniques. These typi- cally find minimal performance difference between approaches given comparable parameter tuning, and consistently poor performance in the presence of constraints [3], [4]. Feedback linearisation can be used to negate some of the nonlinear- ities present in the system, achieved by a suitable change of variables and control input, transforming the nonlinear model into an equivalent linear one suitable for control by classical techniques. These methods have been applied to the TWIP for varying degrees of linearisation and control up to providing global position control for point to point manoeuvres [5]. Nonlinear optimal control has been implemented on a TWIP [6], using a nonlinear method similar to LQR to achieve full position control. However, none of these methods provide a systematic way of ensuring constraint satisfaction, and therefore must be provided with a suitable externally generated reference trajectory that results in the closed loop system observing the desired constraints. It must therefore be accepted that constraints may be violated during disturbance unless a suitable updated recovery trajectory can be provided sufficiently quickly as to prevent violation.
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Performance Comparisons Of Hybrid Fuzzy-LQR And Hybrid PID-LQR Controllers On Stabilizing Double Rotary Inverted Pendulum

Performance Comparisons Of Hybrid Fuzzy-LQR And Hybrid PID-LQR Controllers On Stabilizing Double Rotary Inverted Pendulum

ABSTRACT: Double Rotary Inverted Pendulum (DRIP) is a member of the mechanical under-actuated system which is unstable and nonlinear. The DRIP has been widely used for testing different control algorithms in both simulation and experiments. The DRIP control objectives include Stabilization control, Swing-up control and trajectory tracking control. In this research, we present the design of an intelligent controller called “hybrid Fuzzy-LQR controller” for the DRIP system. Fuzzy logic controller (FLC) is combined with a Linear Quadratic Regulator (LQR). The LQR is included to improve the rules. The proposed controller was compared with the Hybrid PID-LQR controller. Simulation results indicate that the proposed hybrid Fuzzy-LQR controllers demonstrate a better performance compared with the hybrid PID-LQR controller especially in the presense of disturbances.
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Performance Evaluation between PLC and PID Controllers Used For Speed Control Of Induction Motor

Performance Evaluation between PLC and PID Controllers Used For Speed Control Of Induction Motor

II. PLC (programmable logic controller): - It is used in electrical system to improve the reliability and efficiency of electrical equipment (electrical motor) in automation processes. To obtain accurate result PLC is interfaced with converter, PC (Personal computer) and other electrical equipment. In this application PLC of Allen Bradley is used to communicate with VFD and in turn it control 3 phase induction motor. A control program is developed to get required operation of motor and VFD.

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Design of power system stabilizer with pi, pd, pid and lead lag controllers

Design of power system stabilizer with pi, pd, pid and lead lag controllers

Electricity which is an increasing demand leads to the persistent demand to operation of the power system. So power system stability and the quality power to the consumers have equal importance that as the electric power demands. The size and structural components of electric power system vary even though they have some basic characteristics. For the generation of electricity synchronous machines are used. Prime mo vers convert the primary source of energy to mechanical energy which in turn converts to electrical energy by synchronous generators. Electric power generated at generating stations, through a complex network of individual components is transmitted to consumers. The individual components include transmission lines, transformers and switching devices. With a high degree of efficiency and reliability form of electrical energy can be transported and controlled. A wide variety of disturbances occurs frequently and electric system must withstand and remain intact for these disturbances.
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Nonlinear observer based PI sliding surface of adaptive sliding mode control for boost converter in PV system

Nonlinear observer based PI sliding surface of adaptive sliding mode control for boost converter in PV system

Due to nonlinear characteristics of boost converter, some researches have employed nonlinear controller such as sliding mode control. The robustness against parameter uncertainty and disturbance are the main reason why sliding mode control is utilized to control nonlinear system, including boost converter. Many sliding mode control methods [9-13] had been applied to boost converter. However, in practical, this control method requires to be fully known some variables, such as input voltage, inductor current, output voltage, and resistance load. As consequences, many sensors are needed to be installed to acquire those variables as input control. Implementing those methods causes increasing cost production and adding more space and weight in real system. Therefore, to reduce the number of sensors, nonlinear disturbance observer [13-15] is designed to estimate some variables, such as inductor current, output voltage, resistance load, and input voltage generated from solar array. The nonlinear disturbance observer accurately generates the estimated value of resistance load and input voltage such that when the variations of those variables exist, the proposed controller is still able to overcome those disturbances. In sliding mode control design, steady state error regulation needs to be considered. However, in [13], it is employed standard sliding surface and only use equivalent control signal to regulate boost converter. This can cause the output voltage response cannot track the varying desired output voltage and leads to steady state error. To enhance system performance, adaptive sliding mode control is applied to the boost converter for overcoming parameter uncertainty and disturbance [16-17]. Steady state error can be eliminated by constructing PI sliding surface, while ensuring sliding mode in finite time is employed reaching law dynamics and incorporates it to natural control signal. Therefore, nonlinear observer based adaptive sliding mode control with PI sliding surface is proposed for boost converter. The main contribution of this paper is to improve the system performance of voltage regulation boost converter using the combination of nonlinear observer and adaptive sliding mode control by modifying the conventional sliding surface into PI structure sliding surface. In addition, the stability of proposed method is proven by using direct Lyapunov method.
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Neural Network Global Sliding Mode PID Control for Robot Manipulators

Neural Network Global Sliding Mode PID Control for Robot Manipulators

In this paper, a robust neural network global sliding mode PID-controller is proposed to control a robot manipulator with parameter variations and external disturbances. The chattering phenomenon is eliminated by substituting a single-input radial-basis-function (RBF) neural network. The weights of hidden layers of the neural network are on-line updated to compensate the system uncertainties. Moreover, a theoretical proof of the stability and the convergence of the proposed scheme are provided.

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Robust Control of DC Motor Using Fuzzy Sliding Mode Control with PID Compensator

Robust Control of DC Motor Using Fuzzy Sliding Mode Control with PID Compensator

Abstract —In this paper, a robust control system with the fuzzy sliding mode controller and the additional compensator is presented. The additional compensator relaying on the sliding- mode theory is used to improve the dynamical characteristics of the drive system. Sliding mode control method is studied for controlling DC motor because of its robustness against model uncertainties and external disturbances, and also its ability in controlling nonlinear and MIMO systems. In this method, using high control gain to overcome uncertainties lead to occur chattering phenomena in control law which can excite unmodeled dynamics and maybe harm the plant. Different approaches, such as intelligent methods, are used to abate these drawbacks.. In order to enhancement the sliding mode controller performance, we have used fuzzy logic. For this purpose, we have used a PID outer loop in the control law then the gains of the sliding term and PID term are tuned on-line by a fuzzy system, so the chattering is avoided and response of the system is improved against external load torque here. Presented simulation results confirm the above claims and demonstrate the performance improvement in this case.
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LQR/PID Controller Design of PLC-based Inverted Pendulum

LQR/PID Controller Design of PLC-based Inverted Pendulum

This work has presented an LQR based PID controller to control the inverted pendulum system. To facilitate an integration with a PLC, the control design uses a control zon- ing approach where the entire pendulum is divided into two regions: a normal pendulum regions where the nonlinearities are inherent, and the inverted pendulum region where the system is approximately linear close to the upright position. The errors in position and angle are denoted control states to enable the use of the LQR architecture to obtain the optimal gains for the PID controller. An algebraic approach was also presented to allow a systematic method for selection of the Q and R matrices, which in turn yielded an optimal set of control gains K design . Experimental implementations with
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Complex Trajectory Tracking of Inverted Pendulum Using PID and LQR

Complex Trajectory Tracking of Inverted Pendulum Using PID and LQR

The Matlab-Simulink models for the simulation of modeling ,analysis, and control of nonlinear inverted pendulum cart dynamical system without and with disturbance input are developed. The typical parameters of inverted pendulum-cart system setup are selected as mass of the cart (M): 1 kg; mass of the pendulum (m):0.1 kg; length of the pendulum (l): 0.3m; length of the cart track (L): ± 0.5m; the friction coefficient of the cart and pole rotation is assumed negligible. The disturbance input parameters taken in the simulation are[3]: band limited white noise power = 0.001, sampling time = 0.01, seed = 23341.After linearization, the system matrices used to design LQR are computed as
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Optimal Controller Design for Linear Inverted Pendulum and Double Inverted Pendulum System

Optimal Controller Design for Linear Inverted Pendulum and Double Inverted Pendulum System

ABSTRACT: The inverted pendulum system findsit’s most widespread application in the study of control engineering. This paper focused on modelling and performance analysis of linear inverted pendulum and double inverted pendulum system. A comparative study of the time specification performance of both the systems are shown using Linear Quadratic Regulator (LQR) controller. Two control methods are used in this paper, Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) controller for linear inverted pendulum system. Simulation is done using Matlab software.
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Fractional PID and Sliding Mode Controller for Active Vehicle Suspension System

Fractional PID and Sliding Mode Controller for Active Vehicle Suspension System

Active suspension system shows better performance than passive and semi-active suspension systems. Active suspension systems can provide more handling capability and ride quality than passive or semi-active suspension systems. But the main problem facing by the active suspension systems are actuator faults and parameter variations. Due to these factors, its effectiveness decreases. However, suitable solutions are needed to optimize their performance and provide closed-loop stability in a full- scale car model to mitigate road disturbances such as bumps and grade changes on the passenger’s ride comfort. [7].
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Control of MacPherson active suspension system using sliding mode control with composite nonlinear feedback technique

Control of MacPherson active suspension system using sliding mode control with composite nonlinear feedback technique

Figure 1.3: Classification of modern automotive suspension in Xue et al. (2011) In addition, researches over the past five decades have shown that a linear optimal control scheme offers an effective method in designing active suspension control strategies that can improve both vehicle ride and handling performance simultaneously, Hrovat (1997). In fact, most researchers focused on active suspension system with a linear model without uncertainties. However, a modern active suspension system is characteristically nonlinear and uncertain especially for the MacPherson active suspension system.
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Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

Being an under-actuated, non-linear and unstable system, the inverted pendulum (IP) has been examined by many researchers to study the behavior and performance of different and new types of control algorithms [1], [2]. The inverted pendulum has several forms and types where each type has its own characteristics and degree of freedom. The most common types are the single IP, double IP, single rotary IP, and double rotary IP [3]. Even though these types may have different shapes and sizes, their main objective is the same, namely, to balance the whole system. Since the inverted pendulum is a basic form of any advanced balancing systems [4]-[6], its applications widely vary from simple robots like scooters and robot arms, to more sophisticated systems such as satellites and rocket launch [2], [7]-[9].
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Fuzzy Control of Inverted Pendulum Using LABVIEW

Fuzzy Control of Inverted Pendulum Using LABVIEW

ABSTRACT: Generally the inverted pendulum is unstable, because the pendulum will fall downward due to the gravitational force acting on mass of the pendulum. Keeping the pendulum at vertically inverted position, the position of DC motor shaft needs to be controlled using closed loop analysis. For closed loop analysis, feedback or input device such as encoder is used. To control and set the pendulum at desired inverted position in LABVIEW, fuzzy control logic was selected. Fuzzy control logic provides the stabilization of pendulum at vertical position without using mathematical approach such as conventional approach. Fuzzy logic control system (FLC) was selected as the control technique due to its ability to deal with nonlinear systems such as inverted pendulum. Special feature of fuzzy logic control is that it controls the physical system.
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