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

A selection of PID type controller settings via LQR approach for two-wheeled balancing robot

The problem of PID type controller tuning has been addressed in this paper. In particular, a method of selection of PD settings based on the solution of linear-quadratic optimisation problem using the energy criterion has been investigated. Thus, the possibility of transforming optimal settings of the linear-quadratic regulator into the settings of the controller in the classical control system has been given. The presented methodology has been used during synthesis of control system for a two-wheeled balancing robot. Finally, the performance of the proposed control system has been validated by simulation in Matlab-Simulink environment with the use of a two-wheeled balancing robot model.Comment: Conference pape

Analysis and synthesis of SISO H[subscript infinity] controllers

Classical feedback control theories are traditionally concerned with issues like stability and performance, however, they typically fail to address issues such as robustness and plant perturbation. This thesis is concerned with the robust stability and the robust performance of single-input single-output plants. The basic issue under analysis is how to realize the benefits of the usual feedback control structure in the presence of model uncertainty. This is accomplished by seeking feedback controllers providing robust stability and performance by minimizing weighted sensitivity functions of a linear system represented by its transfer function. A characterization of models for plants with unstructured uncertainty is introduced. Specifications and measures of stability and performance for robust controllers and the necessary and sufficient conditions to test the robust stability and the robust performance conditions of a control design are explored. A parametrization of feedback controllers that guarantee closed loop stability for both stable and unstable plants is shown and a systematic procedure for synthesizing robust controllers, known in the literature as HK controllers, is presented. These systematic algorithms are based on the theory of interpolation by analytic functions and the solution to the model matching problem. A case study of the inverted pendulum positioning system is developed to illustrate the concepts of robust analysis and the design algorithms. The controller is compared to a classic state variable feedback solution

A Practical and Conceptual Framework for Learning in Control

We propose a fully Bayesian approach for efficient reinforcement learning (RL) in Markov decision processes with continuous-valued state and action spaces when no expert knowledge is available. Our framework is based on well-established ideas from statistics and machine learning and learns fast since it carefully models, quantifies, and incorporates available knowledge when making decisions. The key ingredient of our framework is a probabilistic model, which is implemented using a Gaussian process (GP), a distribution over functions. In the context of dynamic systems, the GP models the transition function. By considering all plausible transition functions simultaneously, we reduce model bias, a problem that frequently occurs when deterministic models are used. Due to its generality and efficiency, our RL framework can be considered a conceptual and practical approach to learning models and controllers whe

Online Dynamic Motion Planning and Control for Wheeled Biped Robots

Wheeled-legged robots combine the efficiency of wheeled robots when driving on suitably flat surfaces and versatility of legged robots when stepping over or around obstacles. This paper introduces a planning and control framework to realise dynamic locomotion for wheeled biped robots. We propose the Cart-Linear Inverted Pendulum Model (Cart-LIPM) as a template model for the rolling motion and the under-actuated LIPM for contact changes while walking. The generated motion is then tracked by an inverse dynamic whole-body controller which coordinates all joints, including the wheels. The framework has a hierarchical structure and is implemented in a model predictive control (MPC) fashion. To validate the proposed approach for hybrid motion generation, two scenarios involving different types of obstacles are designed in simulation. To the best of our knowledge, this is the first time that such online dynamic hybrid locomotion has been demonstrated on wheeled biped robots