Robust nonlinear control of vectored thrust aircraft

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations

Intelligent control of nonlinear systems with actuator saturation using neural networks

Common actuator nonlinearities such as saturation, deadzone, backlash, and hysteresis are unavoidable in practical industrial control systems, such as computer numerical control (CNC) machines, xy-positioning tables, robot manipulators, overhead crane mechanisms, and more. When the actuator nonlinearities exist in control systems, they may exhibit relatively large steady-state tracking error or even oscillations, cause the closed-loop system instability, and degrade the overall system performance. Proportional-derivative (PD) controller has observed limit cycles if the actuator nonlinearity is not compensated well. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the non-analytic nature of the actuator nonlinear dynamics and the fact that the exact actuator nonlinear functions, namely operation uncertainty, are unknown, the saturation compensation research is a challenging and important topic with both theoretical and practical significance. Adaptive control can accommodate the system modeling, parametric, and environmental structural uncertainties. With the universal approximating property and learning capability of neural network (NN), it is appealing to develop adaptive NN-based saturation compensation scheme without explicit knowledge of actuator saturation nonlinearity. In this dissertation, intelligent anti-windup saturation compensation schemes in several scenarios of nonlinear systems are investigated. The nonlinear systems studied within this dissertation include the general nonlinear system in Brunovsky canonical form, a second order multi-input multi-output (MIMO) nonlinear system such as a robot manipulator, and an underactuated system-flexible robot system. The abovementioned methods assume the full states information is measurable and completely known. During the NN-based control law development, the imposed actuator saturation is assumed to be unknown and treated as the system input disturbance. The schemes that lead to stability, command following and disturbance rejection is rigorously proved, and verified using the nonlinear system models. On-line NN weights tuning law, the overall closed-loop performance, and the boundedness of the NN weights are rigorously derived and guaranteed based on Lyapunov approach. The NN saturation compensator is inserted into a feedforward path. The simulation conducted indicates that the proposed schemes can effectively compensate for the saturation nonlinearity in the presence of system uncertainty

Robust Adaptive Control Barrier Functions: An Adaptive & Data-Driven Approach to Safety (Extended Version)

A new framework is developed for control of constrained nonlinear systems with structured parametric uncertainties. Forward invariance of a safe set is achieved through online parameter adaptation and data-driven model estimation. The new adaptive data-driven safety paradigm is merged with a recent adaptive control algorithm for systems nominally contracting in closed-loop. This unification is more general than other safety controllers as closed-loop contraction does not require the system be invertible or in a particular form. Additionally, the approach is less expensive than nonlinear model predictive control as it does not require a full desired trajectory, but rather only a desired terminal state. The approach is illustrated on the pitch dynamics of an aircraft with uncertain nonlinear aerodynamics.Comment: Added aCBF non-Lipschitz example and discussion on approach implementatio

Asymptotic Tracking Control of Uncertain MIMO Nonlinear Systems with Less Conservative Controllability Conditions

For uncertain multiple inputs multi-outputs (MIMO) nonlinear systems, it is nontrivial to achieve asymptotic tracking, and most existing methods normally demand certain controllability conditions that are rather restrictive or even impractical if unexpected actuator faults are involved. In this note, we present a method capable of achieving zero-error steady-state tracking with less conservative (more practical) controllability condition. By incorporating a novel Nussbaum gain technique and some positive integrable function into the control design, we develop a robust adaptive asymptotic tracking control scheme for the system with time-varying control gain being unknown its magnitude and direction. By resorting to the existence of some feasible auxiliary matrix, the current state-of-art controllability condition is further relaxed, which enlarges the class of systems that can be considered in the proposed control scheme. All the closed-loop signals are ensured to be globally ultimately uniformly bounded. Moreover, such control methodology is further extended to the case involving intermittent actuator faults, with application to robotic systems. Finally, simulation studies are carried out to demonstrate the effectiveness and flexibility of this method

Adaptive Robust Control of Biomass Fuel Co-Combustion Process

The share of biomass in energy production is constantly growing. This is caused by environmental and industry standards and EU guidelines. Biomass is used in the process of co-firing in large power plants and industrial installations. In the existing power stations, biomass is milled and burned simultaneously with coal. However, low-emission combustion techniques, including biomass co-combustion, have some negative side effects that can be split into two categories. The direct effects influence the process control stability, whereas the indirect ones on combustion installations via increased corrosion or boiler slagging. The effects can be minimised using additional information about the process. The proper combustion diagnosis as well as an appropriate, robust control system ought to be applied. The chapter is devoted to the analysis of modern, robust control techniques for complex power engineering applications