Gain-scheduling through continuation of observer-based realizations-applications to H∞ and μ controllers

The dynamic behavior of gain scheduled controllers is highly depending on the state-space representations adopted for the family of lienar controllers designed on a set of operating conditions. In this paper, a technique for determining a set of consistent and physically motivated linear state-space transformations to be applied to the original set of linear controllers is proposed. After transformation, these controllers exhibits an-observer-based structure are therefore easily interpolted and implemented

Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces

© 2007 EUCA.A novel online-computation approach to optimal control of nonlinear, noise-affected systems with continuous state and control spaces is presented. In the proposed algorithm, system noise is explicitly incorporated into the control decision. This leads to superior results compared to state-of-the-art nonlinear controllers that neglect this influence. The solution of an optimal nonlinear controller for a corresponding deterministic system is employed to find a meaningful state space restriction. This restriction is obtained by means of approximate state prediction using the noisy system equation. Within this constrained state space, an optimal closed-loop solution for a finite decision-making horizon (prediction horizon) is determined within an adaptively restricted optimization space. Interleaving stochastic dynamic programming and value function approximation yields a solution to the considered optimal control problem. The enhanced performance of the proposed discrete-time controller is illustrated by means of a scalar example system. Nonlinear model predictive control is applied to address approximate treatment of infinite-horizon problems by the finite-horizon controller

Design of Predictive Controllers by Dynamic Programming and Neural Networks

This paper proposes a method for the design of predictive controllers for nonlinear systems. The method consists of two phases, a solution phase and a learning phase. In the solution phase, dynamic programming is applied to obtain a closed-loop control law. In the learning phase, neural networks are used to simulate the control law. This phase overcomes the curse of dimensionality problem that has often hindered the implementation of control laws generated by dynamic programming. Experimental results demonstrate the effectiveness of the metho

Trajectory-scheduling control systems and their multi-objective design automation

This thesis encompasses the analysis of TSN systems and their multi-objective design methods. TSN nodes are networked through interpolation and activation, similar to a gain-scheduling or local model/controller network. However, to achieve accuracy and ease of commissioning without requiring a large number of nodes, an algorithm has been developed first to identify optimum transition nodes within the entire operating envelope. Then the TSN approaches a nonlinear plant globally, not just locally, without requiring linearization. If desired or necessary, global optimisation provides an enhancement in the design process for TSNs. Since optimising only one aspect (a single objective) of performance while compromising others is undesirable, multi-objective designs have been developed concurrently to deliver or improve multiple aspects of performance. Following the development of a TSN, it is applied to nonlinear system modelling, and this TSN is termed a Trajectory-Scheduling Model (TSM). A TSM possesses the same properties and design features as the TSN generic framework. A nonlinear system, a coupled liquid-tank, is used to examine this modelling technique. Results verify the feasibility and effectiveness of the methods developed and validates the TSM. Further, the TSN technique is applied to nonlinear controller design, by way of a Trajectory-Scheduling Controller (TSC) network. It is illustrated through the design of a networked, easy-to-understand and easy-to-use PID control system for the coupled liquid-tank. Results show that the methods developed offer a high-performance linear control system with nonlinear capabilities to handle practical systems operating in a broad range and to cope with conflict between setpoint following at transient and disturbance rejection at steady state. This method is then applied to the PID network design problems for two nonlinear chemical processes

Trajectory-scheduling control systems and their multi-objective design automation

This thesis encompasses the analysis of TSN systems and their multi-objective design methods. TSN nodes are networked through interpolation and activation, similar to a gain-scheduling or local model/controller network. However, to achieve accuracy and ease of commissioning without requiring a large number of nodes, an algorithm has been developed first to identify optimum transition nodes within the entire operating envelope. Then the TSN approaches a nonlinear plant globally, not just locally, without requiring linearization. If desired or necessary, global optimisation provides an enhancement in the design process for TSNs. Since optimising only one aspect (a single objective) of performance while compromising others is undesirable, multi-objective designs have been developed concurrently to deliver or improve multiple aspects of performance. Following the development of a TSN, it is applied to nonlinear system modelling, and this TSN is termed a Trajectory-Scheduling Model (TSM). A TSM possesses the same properties and design features as the TSN generic framework. A nonlinear system, a coupled liquid-tank, is used to examine this modelling technique. Results verify the feasibility and effectiveness of the methods developed and validates the TSM. Further, the TSN technique is applied to nonlinear controller design, by way of a Trajectory-Scheduling Controller (TSC) network. It is illustrated through the design of a networked, easy-to-understand and easy-to-use PID control system for the coupled liquid-tank. Results show that the methods developed offer a high-performance linear control system with nonlinear capabilities to handle practical systems operating in a broad range and to cope with conflict between setpoint following at transient and disturbance rejection at steady state. This method is then applied to the PID network design problems for two nonlinear chemical processes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Application of variable-gain output feedback for high-alpha control

A variable-gain, optimal, discrete, output feedback design approach that is applied to a nonlinear flight regime is described. The flight regime covers a wide angle-of-attack range that includes stall and post stall. The paper includes brief descriptions of the variable-gain formulation, the discrete-control structure and flight equations used to apply the design approach, and the high performance airplane model used in the application. Both linear and nonlinear analysis are shown for a longitudinal four-model design case with angles of attack of 5, 15, 35, and 60 deg. Linear and nonlinear simulations are compared for a single-point longitudinal design at 60 deg angle of attack. Nonlinear simulations for the four-model, multi-mode, variable-gain design include a longitudinal pitch-up and pitch-down maneuver and high angle-of-attack regulation during a lateral maneuver

Bilinear modeling and nonlinear estimation

New methods are illustrated for online nonlinear estimation applied to the lateral deflection of an elastic beam on board measurements of angular rates and angular accelerations. The development of the filter equations, together with practical issues of their numerical solution as developed from global linearization by nonlinear output injection are contrasted with the usual method of the extended Kalman filter (EKF). It is shown how nonlinear estimation due to gyroscopic coupling can be implemented as an adaptive covariance filter using off-the-shelf Kalman filter algorithms. The effect of the global linearization by nonlinear output injection is to introduce a change of coordinates in which only the process noise covariance is to be updated in online implementation. This is in contrast to the computational approach which arises in EKF methods arising by local linearization with respect to the current conditional mean. Processing refinements for nonlinear estimation based on optimal, nonlinear interpolation between observations are also highlighted. In these methods the extrapolation of the process dynamics between measurement updates is obtained by replacing a transition matrix with an operator spline that is optimized off-line from responses to selected test inputs