Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems

Most existing adaptive control designs for nonlinear pure-feedback systems have been derived based on backstepping or dynamic surface control (DSC) methods, requiring full system states to be measurable. The neural networks (NNs) or fuzzy logic systems (FLSs) used to accommodate uncertainties also impose demanding computational cost and sluggish convergence. To address these issues, this paper proposes a new output-feedback control for uncertain pure-feedback systems without using backstepping and function approximator. A coordinate transform is first used to represent the pure-feedback system in a canonical form to evade using the backstepping or DSC scheme. Then the Levant's differentiator is used to reconstruct the unknown states of the derived canonical system. Finally, a new unknown system dynamics estimator with only one tuning parameter is developed to compensate for the lumped unknown dynamics in the feedback control. This leads to an alternative, simple approximation-free control method for pure-feedback systems, where only the system output needs to be measured. The stability of the closed-loop control system, including the unknown dynamics estimator and the feedback control is proved. Comparative simulations and experiments based on a PMSM test-rig are carried out to test and validate the effectiveness of the proposed method

Neural networks-based command filtering control for a table-mount experimental helicopter

This paper presents neural networks based on command filtering control method for a table-mount experimental helicopter which has three rotational degrees-of-freedom. First, the controller is designed based on backstepping technique, and further command filtering technique is used to solve the derivative of the virtual control, thereby avoiding the effects of signal noise. Secondly, the model uncertainty of the table-mount experimental helicopter's system is estimated by using neural networks. And then, Lyapunov stabilization analysis proves the stability of the table-mount experimental helicopter closedloop attitude tracking system. Finally, the experiment is carried out to clarify the effectiveness of the proposed method. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved

Decentralized adaptive neural network control of interconnected nonlinear dynamical systems with application to power system

Traditional nonlinear techniques cannot be directly applicable to control large scale interconnected nonlinear dynamic systems due their sheer size and unavailability of system dynamics. Therefore, in this dissertation, the decentralized adaptive neural network (NN) control of a class of nonlinear interconnected dynamic systems is introduced and its application to power systems is presented in the form of six papers. In the first paper, a new nonlinear dynamical representation in the form of a large scale interconnected system for a power network free of algebraic equations with multiple UPFCs as nonlinear controllers is presented. Then, oscillation damping for UPFCs using adaptive NN control is discussed by assuming that the system dynamics are known. Subsequently, the dynamic surface control (DSC) framework is proposed in continuous-time not only to overcome the need for the subsystem dynamics and interconnection terms, but also to relax the explosion of complexity problem normally observed in traditional backstepping. The application of DSC-based decentralized control of power system with excitation control is shown in the third paper. On the other hand, a novel adaptive NN-based decentralized controller for a class of interconnected discrete-time systems with unknown subsystem and interconnection dynamics is introduced since discrete-time is preferred for implementation. The application of the decentralized controller is shown on a power network. Next, a near optimal decentralized discrete-time controller is introduced in the fifth paper for such systems in affine form whereas the sixth paper proposes a method for obtaining the L2-gain near optimal control while keeping a tradeoff between accuracy and computational complexity. Lyapunov theory is employed to assess the stability of the controllers --Abstract, page iv

Adaptive NN State-Feedback Control for Stochastic High-Order Nonlinear Systems with Time-Varying Control Direction and Delays

Nussbaum-type gain function and neural network (NN) approximation approaches are extended to investigate the adaptive statefeedback stabilization problem for a class of stochastic high-order nonlinear time-delay systems. The distinct features of this paper are listed as follows. Firstly, the power order condition is completely removed; the restrictions on system nonlinearities and time-varying control direction are greatly weakened. Then, based on Lyapunov-Krasovskii function and dynamic surface control technique, an adaptive NN controller is constructed to render the closed-loop system semiglobally uniformly ultimately bounded (SGUUB). Finally, a simulation example is shown to demonstrate the effectiveness of the proposed control scheme

Adaptive command-filtered finite-time consensus tracking control for single-link flexible-joint robotic multi-agent systems

This article presents a command-filtered finite-time consensus tracking control strategy for the considered single-link flexible-joint robotic multi-agent systems. First, each agent system considered in this article is a nonlinear nonstrict-feedback system with unknown nonlinearities, so the traditional backstepping method cannot be directly applied to the design controller. However, by applying the unique structure of the Gaussian function in radial basis function neural networks, the challenges in controller design caused by the aforementioned nonstrict-feedback system have been overcome. Second, the problem of unknown nonlinearities in the system is solved by the approximation property of radial basis function neural network technology. In addition, the traditional backstepping approach often leads to an “explosion of complexity” resulting from repeated derivation of virtual control signals. Our design addresses this issue by employing command filtering technology, which simplifies the controller design process. Meanwhile, new compensation signals are designed, which successfully eliminate the error influence posed by the filters. It is seen that the control strategy presented in this article can guarantee the tracking errors converge to a small neighborhood of origin in a finite time, and all signals in the closed-loop systems remain bounded. Eventually, the simulation results show the validity of the acquired control scheme

Reference Governors: From Theory to Practice

Control systems that are subject to constraints due to physical limitations, hardware protection, or safety considerations have led to challenging control problems that have piqued the interest of control practitioners and theoreticians for many decades. In general, the design of constraint management schemes must meet several stringent requirements, for example: low computational burden, performance, recovery mechanisms from infeasibility conditions, robustness, and formulation simplicity. These requirements have been particularly difficult to meet for the following three classes of systems: stochastic systems, linear systems driven by unmodeled disturbances, and nonlinear systems. Hence, in this work, we develop three constraint management schemes, based on Reference Governor (RG), for these classes of systems. The first scheme, which is referred to as Stochastic RG, leverages the ideas of chance constraints to construct a Stochastic Robustly Invariant Maximal Output Admissible set (SR-MAS) in order to enforce constraints on stochastic systems. The second scheme, which is called Recovery RG (RRG), addresses the problem of recovery from infeasibility conditions by implementing a disturbance observer to update the MAS, and hence recover from constraint violations due to unmodeled disturbances. The third method addresses the problem of constraint satisfaction on nonlinear systems by decomposing the design of the constraint management strategy into two parts: enforcement at steady-state, and during transient. The former is achieved by using the forward and inverse steady-state characterization of the nonlinear system. The latter is achieved by implementing an RG-based approach, which employs a novel Robust Output Admissible Set (ROAS) that is computed using data obtained from the nonlinear system. Added to this, this dissertation includes a detailed literature review of existing constraint management schemes to compare and highlight advantages and disadvantages between them. Finally, all this study is supported by a systematic analysis, as well as numerical and experimental validation of the closed-loop systems performance on vehicle roll-over avoidance, turbocharged engine control, and inverted pendulum control problems

Adaptive neural control of nonlinear systems with hysteresis

Ph.DDOCTOR OF PHILOSOPH

Passivity - Based Control and Stability Analysis for Hydro-Solar Power Systems

Los sistemas de energía modernos se están transformando debido a la inclusión de renovables no convencionales fuentes de energía como la generación eólica y fotovoltaica. A pesar de que estas fuentes de energía son buenas alternativas para el aprovechamiento sostenible de la energía, afectan el funcionamiento y la estabilidad del sistema de energía, debido a su naturaleza inherentemente estocástica y dependencia de las condiciones climáticas. Además, los parques solares y eólicos tienen una capacidad de inercia reducida que debe ser compensada por grandes generadores síncronos en sistemas hidro térmicos convencionales, o por almacenamiento de energía dispositivos. En este contexto, la interacción dinámica entre fuentes convencionales y renovables debe ser estudiado en detalle. Para 2030, el Gobierno de Colombia proyecta que el poder colombiano El sistema integrará en su matriz energética al menos 1,2 GW de generación solar fotovoltaica. Por esta razón, es necesario diseñar controladores robustos que mejoren la estabilidad en los sistemas de energía. Con alta penetración de generación fotovoltaica e hidroeléctrica. Esta disertación estudia nuevas alternativas para mejorar el sistema de potencia de respuesta dinámica durante y después de grandes perturbaciones usando pasividad control basado. Esto se debe a que los componentes del sistema de alimentación son inherentemente pasivos y permiten formulaciones hamiltonianas, explotando así las propiedades de pasividad de sistemas eléctricos. Las principales contribuciones de esta disertación son: una pasividad descentralizada basada control de los sistemas de control de turbinas hidráulicas para sistemas de energía de múltiples máquinas para estabilizar el rotor acelerar y regular el voltaje terminal de cada sistema de control de turbinas hidráulicas en el sistema como, así como un control basado en PI pasividad para las plantas solares fotovoltaicas

The 5th Annual NASA Spacecraft Control Laboratory Experiment (SCOLE) Workshop, part 2

A collection of papers from the workshop are presented. The topics addressed include: the modeling, systems identification, and control synthesis for the Spacecraft Control Laboratory Experiment (SCOLE) configuration