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...

The paper presents a novel Modulated Power Filter and Compensator (MPFC) scheme for voltage stability, energy conservation, loss reduction, power factor correction, and power quality enhancement for electric distribution systems based on Multi-Objective Particle Swarm Optimisation (MOPSO). The MPFC scheme was developed by the first author to vary the shunt power filter equivalent admittance, mo...

A novel multi-objective evolutionary algorithm (MOEA) is developed based on Imperialist Competitive Algorithm (ICA), a newly introduced evolutionary algorithm (EA). Fast non-dominated sorting and the Sigma method are employed for ranking the solutions. The algorithm is tested on six well-known test functions each of them incorporate a particular feature that may cause difficulty to MOEAs. The n...

This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the mult...

This paper presents an application of an evolutionary optimization algorithm for multiobjective analysis for reservoir operations and planning. A multiobjective particle swarm optimization (MOPSO) algorithm is used to find nondominated solutions with four objectives: (i) maximize annual firm water supply; (ii) maximize annual firm energy production; (iii) minimize flood risk; and (iv) maximize ...

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.