377 research outputs found
Robust Control Theory Based Performance Investigation of an Inverted Pendulum System using Simulink
In this paper, the performance of inverted pendulum have been Investigated using robust control theory. The robust controllers
used in this paper are H∞ Loop Shaping Design Using Glover McFarlane Method and mixed H∞ Loop Shaping Controllers.
The mathematical model of Inverted Pendulum, a DC motor, Cart and Cart driving mechanism have been done successfully.
Comparison of an inverted pendulum with H∞ Loop Shaping Design Using Glover McFarlane Method and H∞ Loop Shaping
Controllers for a control target deviation of an angle from vertical of the inverted pendulum using two input signals (step and
impulse). The simulation result shows that the inverted pendulum with mixed H∞ Loop Shaping Controller to have a small rise
time, settling time and percentage overshoot in the step response and having a good response in the impulse response too.
Finally the inverted pendulum with mixed H∞ Loop Shaping Controller shows the best performance in the overall simulation
result
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Design and Control of a Two-Wheeled Robotic Walker
This thesis presents the design, construction, and control of a two-wheeled inverted pendulum (TWIP) robotic walker prototype for assisting mobility-impaired users with balance and fall prevention. A conceptual model of the robotic walker is developed and used to illustrate the purpose of this study. A linearized mathematical model of the two-wheeled system is derived using Newtonian mechanics. A control strategy consisting of a decoupled LQR controller and three state variable controllers is developed to stabilize the platform and regulate its behavior with robust disturbance rejection performance. Simulation results reveal that the LQR controller is capable of stabilizing the platform and rejecting external disturbances while the state variable controllers simultaneously regulate the system’s position with smooth and minimum jerk control.
A prototype for the two-wheeled system is fabricated and assembled followed by the implementation and tuning of the control algorithms responsible for stabilizing the prototype and regulating its position with optimal performance. Several experiments are conducted, confirming the ability of the decoupled LQR controller to robustly balance the platform while the state variable controllers regulate the platform’s position with smooth and minimum jerk control
Dual Mode Control of an Inverted Pendulum: Design, Analysis and Experimental Evaluation
We present an inverted pendulum design using readily available V-slot rail components and
3D printing to construct custom parts. To enable the examination of different pendulum
characteristics, we constructed three pendulum poles of different lengths. We implemented
a brake mechanism to modify sliding friction resistance and built a paddle that can be
attached to the ends of the pendulum poles. A testing rig was also developed to consistently
apply disturbances by tapping the pendulum pole, characterizing balancing performance.
We perform a comprehensive analysis of the behavior and control of the pendulum. This
begins by considering its dynamics, including the nonlinear differential equation that
describes the system, its linearization, and its representation in the s-domain. The primary
focus of this work is the development of two distinct control modes for the pendulum: a
velocity control mode, designed to balance the pendulum while the cart is in motion, and a
position control mode, aimed at maintaining the pendulum cart at a specific location. For
this, we derived two different state space models: one for implementing the velocity control
mode and another for the position control mode. In the position control mode, integral action
applied to the cart position ensures that the inverted pendulum remains balanced and
maintains its desired position on the rail. For both models, linear observer-based state
feedback controllers were implemented. The control laws are designed as linear quadratic
regulators (LQR), and the systems are simulated in MATLAB. To actuate the physical
pendulum system, a stepper motor was used, and its controller was assembled in a DIN rail
panel to simplify the integration of all necessary components. We examined how the
optimized performance, achieved with the medium-length pendulum pole, translates to poles
of other lengths. Our findings reveal distinct behavioral differences between the control
modes
Application of model reduction for robust control of self-balancing two-wheeled bicycle
In recent years, balance control of two-wheeled bicycle has received more attention of scientists. One difficulty of this problem is the control object is unstable and constantly impacted by noise. To solve this problem, the authors often use robust control algorithms. However, robust controller of self-balancing two-wheeled bicycle are often complex and higher order so affect to quality during real controlling. The article introduces the stochastic balanced truncation algorithm based on Schur analysis and applies this algorithm to reduce order higher order robust controller in control balancing two-wheeled bicycle problem. The simulation results show that the reduced 4th and 5th order controller arcoording to the stochastic balanced truncation algorithm based on Schur analysis can control the two-wheeled bicycle model. The reduced 3rd order controller cannot control the balance of the two-wheeled bicycle model. The reduced 4th and 5th order controller can replace the original controller while the performance of the control system is ensured. Using reduced 5th, 4th order controller will make the program code simpler, reducing the calculation time of the self-balancing two-wheel control system. The simulation results show the correctness of the model reduction algorithm and the robust control algorithm of two-wheeled self-balancing two-wheeled bicycle
A brief review of neural networks based learning and control and their applications for robots
As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation
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