Although it is well known that fuzzy learning controller is powerful for nonlinear systems, it is very difficult to apply a learning method if they are unstable. An unstable system diverges for impulse input. This divergence makes it difficult to learn the rules unless we can find the initial rules to make the system stable prior to learning. Therefore, we introduced LQR(Linear Quadratic Regula...

Linear Quadratic Regulator (LQR) is an optimal multivariable feedback control approach that minimizes the excursion in state trajectories of a system while requiring minimum controller effort. The behaviour of a LQR controller is determined by two parameters: state and control weighting matrices. These two matrices are main design parameters to be selected by designer and greatly influence the ...

In this paper, we provide the solution to the optimal Linear Quadratic Regulator (LQR) paradigm for Markovian Jump linear Systems, when the continuous state is available at the controller instantaneously, but the mode is available only after a delay of one time step. This paper is the first to investigate the LQR paradigm in the presence of such mismatch between the delay in observing the mode ...

The concluding chapter of this part of the book discusses several flight control implementation examples that integrate Matlab/Simulink within the open control platform (OCP) framework (described in Chapters 4 and 5). A public domain version of an F-16 aircraft model, available as a Simulink block with OCP distribution 1.0, is used for illustration. The first example is a linear quadratic regul...

This paper is a contribution to the theory of the infinitehorizon linear quadratic regulator (LQR) problem subject to inequality constraints on the inputs and states, extending an approach first proposed by Sznaier and Damborg [16]. A solution algorithm is presented, which requires solving a finite number of finite-dimensional positive definite quadratic programs. The constrained LQR outlined d...

Analysis of attitude stabilization of a power-aided unicycle points out that a unicycle behaves like an inverted pendulum subject to power constraint. An LQR-mapped fuzzy controller is introduced to solve this nonlinear issue by mapping LQR control reversely through least square and Sugeno-type fuzzy inference. The fuzzy rule surface after mapping remains optimal.

The instrumentation, i.e., sensors and actuators, in feedback control systems often contain nonlinearities, such as saturation, deadzone, quantization, etc. Standard synthesis techniques, however, assume that the actuators and sensors are linear. This technical note is intended to modify the LQR/LQG methodology into the so-called Instrumented LQR/LQG (referred to as ILQR/ILQG), which allows for...

We present a comparison of a LQR controller and Q-learning on a simulation of a triple inverted pendulum. While the LQR controller was able to balance the pendulum, Qlearning was unable due to memory and computing limitations. We examine the strengths and weaknesses of each method and compare their abilities.

A hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller is designed for altitude (z) attitude (roll, pitch, yaw) control of a quadrotor vehicle. The derivation mathematical model the formulated based on Newton–Euler approach. An appropriate controller’s parameter must be obtained to obtain superior performance. Therefore, we exploit advantages nature-inspired...

A framework for the design and simulation of a building envelope and an HVAC system is presented. Building models are first captured in Modelica to leverage its rich building component library and then imported into Simulink to exploit its strong control design environment that enables efficient control design and implementation. Four controllers with different computational intensity are consi...