Top PDF Robust LQR Controller Design for Stabilizing and Trajectory Tracking of Inverted Pendulum

Robust LQR Controller Design for Stabilizing and Trajectory Tracking of Inverted Pendulum

Robust LQR Controller Design for Stabilizing and Trajectory Tracking of Inverted Pendulum

This paper describes the method for stabilizing and trajectory tracking of Self Erecting Single Inverted Pendulum (SESIP) using Linear Quadratic Regulator (LQR). A robust LQR is proposed in this paper not only to stabilize the pendulum in upright position but also to make the cart system to track the given reference signal even in the presence of disturbance. The control scheme of pendulum system consists of two controllers such as swing up controller and stabilizing controller. The main focus of this work is on the design of stabilizing controller which can accommodate the disturbance present in the system in the form of wind force. An optimal LQR controller with well tuned weighting matrices is implemented to stabilize the pendulum in the vertical position. The steady state and dynamic characteristics of the proposed controller are investigated by conducting experiments on benchmark linear inverted pendulum system. Experimental results prove that the proposed LQR controller can guarantee the inverted pendulum a faster and smoother stabilizing process with less oscillation and better robustness than a Full State Feedback (FSF) controller by pole placement approach.
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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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Real Time Optimal Implementation of Stabilizing Controller for Inverted Pendulum

Real Time Optimal Implementation of Stabilizing Controller for Inverted Pendulum

This paper present real time control of an inverted pendulum. The system is inherently unstable and multivariable. It is mostly used in laboratories to study, verify and validate new control ideas. The dynamic model of the system was derived based on Lagrange approach and it was linearized. Linear Quadratic Regulator (LQR) controller was designed to stabilize the system in an upright position. The robustness of the control algorithm was tested based on disturbance rejection. Simulation and experimental results showed a good performance was achieved and the controller is robust to external disturbances.
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Optimal Controller Design for Inverted Pendulum System: An Experimental Study

Optimal Controller Design for Inverted Pendulum System: An Experimental Study

This thesis begins with the explanation of CIPS together with the hardware setup used for research, its state space dynamics and transfer function models after linearizing it. Since, Inverted Pendulum is inherently unstable i.e. if it is left without a stabilizing controller it will not be able to remain in an upright position when disturbed. So, a systematic iterative method for the state feedback design by choosing weighting matrices key to Linear Quadratic Regulator (LQR) design is presented assuming all the states to be available at the output. After that, Kalman Filter, which is an optimal Observer has been designed to estimate all the four states considering process and measurement noises in the system.
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Linear quadratic regulator (LQR) controller design for inverted pendulum

Linear quadratic regulator (LQR) controller design for inverted pendulum

Based on the pole placement design technique, a full state feedback controller using separation factor is proposed to stabilize a real single inverted pendulum [11]. The strategy is to start with selection two dominant poles that achieve a certain desired performance, using a separation factor between the selected dominant poles and the other poles to eliminate their effect on the system performance, and finally Ackermann’s formula can be used to calculate the feedback gain matrix to place the system poles at the desired locations. Simulation and experimental results demonstrate the effectiveness of the proposed controller, which offers an excellent stabilizing and also ability to overcome the external resistance acting on the pendulum system.
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Alternative controller design for rotary inverted pendulum

Alternative controller design for rotary inverted pendulum

Jadlovska and Sarnovsky [8] have revealed an approach for the control of the rotary single inverted pendulum system. State feedback control techniques are used as pole placement and the LQR optimal control. Mathew et al. [9] performed a study on swing up and stabilization control of a RIP system. Two control schemes are performed for stabilization as LQR and SMC. Chen and Huang [10] have proposed an adaptive controller for RIP with time-varying uncertainties to bring the pendulum close to the upright position regardless of the various uncertainties and disturbances. Its underactuated dynamics was first decoupled by Olfati’s transformation into a cascade form. Oltean [11] has proposed solution for swing up and stabilization of RIP using PD and fuzzy PD controllers. The models are performed in MATLAB/ Simulink environment. Ding and Li [12] have proposed a cascade fuzzy controller based on Mamdani for the outer loop and Sugeno for the inner loop. The simulation graphs are performed with Simulink. Dang et al. [13] have designed a robust Takagi-Sugeno (T-S) fuzzy descriptor approach for a stabilizing controller for the RIP with real-time implementation. Chandran et al. [14] derived the nonlinear dynamics of the RIP. Artificial neural network (ANN) is applied to identify the model.
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Intelligent controllers for velocity tracking of two wheeled inverted pendulum mobile robot

Intelligent controllers for velocity tracking of two wheeled inverted pendulum mobile robot

Based on the review stated above, velocity tracking has been one of the major objectives of controlling the TWIP. Many works in the past years have been published in the area. Model based controllers like LQR [32, 34], and Pole placement controller [27], which are designed based on the linearized model of the robot making the model uncertainties to affect the controllers gain. Nonlinear controllers like partial feedback linearization [11, 19, 33] and SMC [1, 12, 22] performs well in rejecting modelling inaccuracies, parameter variations and disturbances, but SMC has the problem of chattering. Intelligent controllers were used by researchers to track a desired speed of the TWIP, in [6] a direct adaptive model reference control scheme was used, fuzzy logic which is a non-model based controller, was used in [15, 20]. Adaptive neural SMC method for trajectory tracking was shown in [35]. In this paper, three
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Complex Trajectory Tracking of Inverted Pendulum Using PID and LQR

Complex Trajectory Tracking of Inverted Pendulum Using PID and LQR

It is a well-established benchmark problem which provides many challenging problems on the design of control design. The system is nonlinear, unstable, no minimum phase and under actuated. Because of their nonlinear nature pendulums have maintained their usefulness and they are now used to illustrate many of the ideas emerging in the field of nonlinear control [1]. The challenges of control made the inverted pendulum systems become a classic tool in control laboratories.

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Modelling and simulation of rotary inverted pendulum design

Modelling and simulation of rotary inverted pendulum design

Pendulum can be described as a system in a control field theory. An inverted pendulum includes a pendulum rod in vertical, a horizontal pendulum arm, a motor and an encoder. This system was developed by Stephensen about 100 years ago that the controller is needed in order to achieve a stabilization for inverted pendulum in remain upright. The elasticity of the inverted pendulum was studied by Chao under the expectation of beam. This system is widely used in this control engineering that was also applied in many industries such as balancing a broom with only one hand, by launching the rocket from the ground and to stable the arm of robot. Thus, there were many studies on doing a research of the inverted pendulum system that are still in progress of carrying it all over the world. (Tang Jiali Ren Gexue, 2009)
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Design of Intelligent Controllers for Double Inverted Pendulum

Design of Intelligent Controllers for Double Inverted Pendulum

the system for angles up-to maximum degrees, it is also seen that it does not perform well in terms of speed. In order to solve this problem, we have used an supervisory controller. A non- Fuzzy supervisor (linearization feedback) is designed for the Fuzzy controller such that, if the inverted pendulum’s angle exceeds a limit (23 degrees); supervisor controller takes action and reduces the angle immediately. In normal conditions (angles below 23 degrees) the fuzzy controller has to control the system. Thus not only the system becomes stable for a larger initial angle but also a simpler fuzzy controller (with less rules and inputs) is used, less calculation is required and the speed becomes higher.
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Conceptual Design and Simulation of An Inverted Pendulum Wheelchair

Conceptual Design and Simulation of An Inverted Pendulum Wheelchair

widely used as a teaching aid and in research experiments around the world. As a teaching aid, the inverted pendulum used because it’s an imaginable unstable nonlinear dynamics problem commonly discussed in control engineering (Hauser et al., 2005), and various control algorithms, ranging from conventional through to intelligent control algorithms, has been applied and evaluated (Jung and Kim, 2008). There are a number of associated control problems that can be derived from the inverted pendulum models such as rocket control, the dynamic balance of skiing, bicycle/motorcycle dynamics (Hauser et al., 2005). Recently, a mobile inverted pendulum model with two wheels has been applied to various robotic problems such as designing walking or legged humanoid robot, robotic wheelchairs and personal transport systems (Kim et al., 2006).
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Stabilization Control Of Rotary Inverter Pendulum System With Double-PID And LQR Controller

Stabilization Control Of Rotary Inverter Pendulum System With Double-PID And LQR Controller

Figure 2.2 shows a block diagram of a Full State Feedback (FSF) controller in the RIP system. The RIP system is was designed in state space model. Poles of the closed loop system may be placed at any desired locations by means of state feedback through an appropriate state feedback gain matrix K. There are a few approach to tune FSF controller, one of them is by pole placement method. According to M. Akhtaruzzaman [5], he designed FSF controller by placing stable poles of the RIP system, then used Ackermann’s formula and Integral of Time-weighted Absolute Error (ITAE) table, state feedback control gain matrix, K which is a 4 × 1 matrix was obtained. FSF controller was considered relatively ease of design and effective procedure to obtain the gain matrix K. There are some drawbacks of designing FSF controller. It requires successful measurement of all state variables or a state observer in the system, where it needs control system design in state space model. It also requires experienced researcher to determine the desired closed loop poles of the system, especially when the system has a higher order system than second or third order.
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Control Of A Real Inverted Pendulum Systems Using Statefeedback Controller

Control Of A Real Inverted Pendulum Systems Using Statefeedback Controller

3.1 Inverted Pendulum on cart (Modeled by K.Ogata, 1978) 30 3.2 Inverted Pendulum plant in MATLAB’s Simulink 35 4.1 Basic structure of state feedback controller 37 4.2 Block diagram of a plant with a state feedback controller 37 4.3 State feedback block diagram in the Simulink 41 4.4 Gain of the state feedback controller in Simulink 42 4.5 State feedback controls with steady state (SS) gain 43 4.6 State feedback with integral block diagram at Simulink 44 5.1 The block diagram representing the interface process 45
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Design And Implementation Of Controllers In An Inverted Pendulum

Design And Implementation Of Controllers In An Inverted Pendulum

simulated inverted pendulum system and these K values were implemented in the physical laboratory inverted pendulum. The non – minimum phase behaviour of both simulated and laboratory inverted pendulum systems were noticed. Although, the results obtained show no direct correlation between time - taken to swing the pendulum to its upright position for the two systems. These variations may be due to high K values which make the system approach the upright position with too high velocity. The physical inverted pendulum shows high robustness for any external disturbance as the cart is homing back and front during the measurement as stability attained.
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Linear Quadratic Gaussian (LQG) controller for inverted pendulum

Linear Quadratic Gaussian (LQG) controller for inverted pendulum

Synopsis: Purpose of this paper is to design and build a two wheeled upright robot. The robot was embedded with sensors, in order to the robot interact with surrounding the distance sensor in combination with temperature sensor was implemented on it. The robot also has a bowl on top for carrying load purpose. The controller that used to balance the robot is PID controller and linear quadratic Gaussian (LQG) controller. For testing the sensor fusion between the accelerometer and gyro, the Kalman filter and complementary filter was used. As result, Kalman filter was chosen because Kalman filter sufficient for both linear movement and noise of the accelerometer as well as estimate and compensate for the gyros drift and bias. In this project, the microcontroller has been used is Arduino Mega35. Unfortunately, the LQG controller cannot be implementing in hardware this is because Arduino is not fast enough to do calculation for the controller in the required loop time. As result, PID controller was used to balance the robot and it was successful.
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State space control using LQR method for a cart-inverted pendulum
linearised model

State space control using LQR method for a cart-inverted pendulum linearised model

C ONTROLLING a Cart-Inverted Pendulum System (CIPS) is a challenging problem which is widely used as benchmark for testing control algorithms such as PID controllers [1], neural networks [2], [3], fuzzy control [4], genetic algorithms [5]. The CIPS features as higher order, nonlinear, strong coupling and multivariate system, which has been studied by many researchers. It is used to model the field of robotics and aerospace field, and so has important significance both in the field of the theoretical study research and practice. It has good practical applications right from mis- sile launchers to segways, human walking, earthquake resistant building design etc. The CIPS dynamics resembles the missile or rocket launcher dynamics as its center of gravity is located behind the centre of drag causing aerodynamic instability. The CIPS has two equilibrium points [6], one of them is stable while the other is unstable. The stable equilibrium corresponds to a state in which the pendulum is pointing downwards. The control challange is to maintain the pendulum at the unstable equilibrium point where the pendulum upwards, with minimum control energy. In recent times optimal control provides the best possible solution to process control problems for a given set of performance objectives. Detail review on optimal control has been presented in [7]. Survey on optimal control approaches and their applications have been conducted, for instance, an optimal control approach for inventory systems [8] and energy optimisation [9].
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Control of Non Linear Inverted Pendulum using Fuzzy Logic Controller

Control of Non Linear Inverted Pendulum using Fuzzy Logic Controller

being an inherently unstable system, is a common control problem, and so being one of the most significant classical problems, the control of inverted pendulum has been a research interest in the field of control engineering. Due to its importance this is a choice of dynamic system to analyze its dynamic model. The aim of this case study is to stabilize the Inverted Pendulum (IP) such that the position of the cart on the track is controlled rapidly and accurately so that the pendulum is always in erected in its inverted position during such movements even the system is affected with disturbance input such as wind force or due to others cause. Realistically, this mechanical system is representative of a class of altitude control problems whose aim is to maintain the desired vertically oriented position at all times [6-9].
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LQG Control Design for Balancing an Inverted Pendulum Mobile Robot

LQG Control Design for Balancing an Inverted Pendulum Mobile Robot

of the obstacles by using the PID and PD controllers are that they alone cannot effectively control all of the pendulum state variables (modes) since they are of lower order than the pendulum itself. Hence, they are usually replaced by a full-order controller [3]. A linear state feedback controller based on the linearized inverted pendulum model can instead be used, and may also be extended with a disturbance observer (Kalman filter) to improve the disturbance rejection performance.

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PSO based Optimum Design of PID Controller for Mobile Robot Trajectory Tracking

PSO based Optimum Design of PID Controller for Mobile Robot Trajectory Tracking

The PID controller has been used to control about 90% of industrial processes worldwide [3]. The main problem of that simple controller is the correct choice of the PID gains and the fact that by using fixed gains, the controller may not provide the required control performance, when there are variations in the plant parameters and operating conditions. Therefore, a tuning process must be performed to insure that the controller can deal with the variations in the plant [4]. To tune the PID controller, there are numbers of strategies, the most famous, which is frequently used in industrial applications, is the Ziegler-Nichols method [3] , genetic algorithm GA, etc. Moreover, PSO was another method for tuning procedure. PSO first introduced by Kennedy and Eberhart is one of the modern heuristic algorithms, it has been motivated by the behavior of organisms, such as fish schooling and bird flocking [5]. Generally, PSO is characterized as a simple concept, easy to implement, and computationally efficient. Unlike the other heuristic techniques, PSO has a flexible and well-balanced mechanism to enhance the global and local exploration abilities [6]. In this paper, a novel PSO-based approach to optimally design a PID controller for a mobile robot trajectory tracking is proposed. This paper has been organized as follows: in section 2 both kinematics and dynamic models of mobile robot are described. In section 3, the particle swarm optimization method is reviewed. Section 4, describes how PSO is used to design t`he PID controller optimally for mobile robot to control the velocity and
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Robust Sliding Mode Controller for Trajectory Tracking and Attitude Control of a Nonholonomic Spherical Mobile Robot

Robust Sliding Mode Controller for Trajectory Tracking and Attitude Control of a Nonholonomic Spherical Mobile Robot

Although, some investigations have been developed on spherical robots recently, motion control of these robots is still one of the major problem in robotic researches. Since the spherical shell is assumed to roll over the ground surface without any slipping, its motion is subjected to two nonholonomic constraints. On the other hand, according to Brocket’s theorem, the stabilization of the equilibrium points of the nonholonomic systems through time invariant state-feedback is not possible [9]. By the way, for the control of the spherical robot, Zhao et all have derived the dynamical model of the spherical robot merely for straight line motions and a PID controller has been proposed for the robot’s motion on straight line trajectories [10]. The control of the spherical robot by uses of a pendulum as a control actuator has been developed by feedback linearization method in straight line trajectories [11]. Furthermore, trajectory tracking control of the spherical robot on straight paths has been investigated using SMC method [12], adaptive hierarchical sliding mode approach [13]; and also using combined adaptive neuro-fuzzy and SMC method [14].
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