435 research outputs found
Anti-Disturbance Compensation-Based Nonlinear Control for a Class of MIMO Uncertain Nonlinear Systems
Multi-Inputs-Multi-Outputs (MIMO) systems are recognized mainly in industrial applications with both input and state couplings, and uncertainties. The essential principle to deal with such difficulties is to eliminate the input couplings, then estimate the remaining issues in real-time, followed by an elimination process from the input channels. These difficulties are resolved in this research paper, where a decentralized control scheme is suggested using an Improved Active Disturbance Rejection Control (IADRC) configuration. A theoretical analysis using a state-space eigenvalue test followed by numerical simulations on a general uncertain nonlinear highly coupled MIMO system validated the effectiveness of the proposed control scheme in controlling such MIMO systems. Time-domain comparisons with the Conventional Active Disturbance Rejection Control (CADRC)-based decentralizing control scheme are also included
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Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure
This paper presents adaptive neural tracking control for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine purefeedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem (MVT) is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularityfree adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SGUUB). Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this work
Control optimization, stabilization and computer algorithms for aircraft applications
The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications. Despite
their low number of open parameters, tuning multiple, coupled PID controllers
can become tedious in practice. In this paper, we extend PILCO, a model-based
policy search framework, to automatically tune multivariate PID controllers
purely based on data observed on an otherwise unknown system. The system's
state is extended appropriately to frame the PID policy as a static state
feedback policy. This renders PID tuning possible as the solution of a finite
horizon optimal control problem without further a priori knowledge. The
framework is applied to the task of balancing an inverted pendulum on a seven
degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
and data-efficient policy learning, even on complex real world problems.Comment: Accepted final version to appear in 2017 IEEE International
Conference on Robotics and Automation (ICRA
Optimal Universal Controllers for Roll Stabilization
Roll stabilization is an important problem of ship motion control. This
problem becomes especially difficult if the same set of actuators (e.g. a
single rudder) has to be used for roll stabilization and heading control of the
vessel, so that the roll stabilizing system interferes with the ship autopilot.
Finding the "trade-off" between the concurrent goals of accurate vessel
steering and roll stabilization usually reduces to an optimization problem,
which has to be solved in presence of an unknown wave disturbance. Standard
approaches to this problem (loop-shaping, LQG, -control etc.)
require to know the spectral density of the disturbance, considered to be a
\colored noise". In this paper, we propose a novel approach to optimal roll
stabilization, approximating the disturbance by a polyharmonic signal with
known frequencies yet uncertain amplitudes and phase shifts. Linear quadratic
optimization problems in presence of polyharmonic disturbances can be solved by
means of the theory of universal controllers developed by V.A. Yakubovich. An
optimal universal controller delivers the optimal solution for any uncertain
amplitudes and phases. Using Marine Systems Simulator (MSS) Toolbox that
provides a realistic vessel's model, we compare our design method with
classical approaches to optimal roll stabilization. Among three controllers
providing the same quality of yaw steering, OUC stabilizes the roll motion most
efficiently
Adaptive control and neural network control of nonlinear discrete-time systems
Ph.DDOCTOR OF PHILOSOPH
A Survey of the Probability Density Function Control for Stochastic Dynamic Systems
Probability density function (PDF) control strategy investigates the controller design approaches in order to to realise a desirable distributions shape control of the random variables for the stochastic processes. Different from the existing stochastic optimisation and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. This paper summarises the recent research results of the PDF control while the controller design approaches can be categorised into three groups: 1) system model-based direct evolution PDF control; 2) model-based distribution-transformation PDF control methods and 3) databased PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense
Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges
In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed
Model-free robust decoupling control of nonlinear nonaffine dynamic systems
This study presents a model-free robust input-output decoupling control with Nonlinear-Dynamic-Coupling Inversion/Inverter (NDCI) in a U-control framework. Regarding the decoupling, an input/output (I/O) coupling matrix function is proposed to derive two decouplers (U-decoupler/functional inversion and D-decoupler/static matrix inversion). A general existing theorem is proved for model-free sliding mode control (MFSMC) to lay the foundation for the NDCI, which takes the Lyapunov differential inequality for its derivative rather than the semi-define Lyapunov derivative. Accordingly, a multi-input and multi-output (MIMO) model-free decoupling U-control (MFDUC) platform is established to integrate the functionalities into a double closed-loop system framework. To validate the functionalities and configurations, this study presents transparent and comparative simulated bench tests, which also could be treated as user guidance for further study and ad hoc applications
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