29,717 research outputs found
Adaptive set-point regulation of discrete-time nonlinear systems
In this paper, adaptive set-point regulation controllers for discrete-time
nonlinear systems are constructed. The system to be controlled is assumed to
have a parametric uncertainty, and an excitation signal is used in order to
obtain the parameter estimate. The proposed controller belongs to the category
of indirect adaptive controllers, and its construction is based on the policy
of calculating the control input rather than that of obtaining a control law.
The proposed method solves the adaptive set-point regulation problem under the
(possibly minimal) assumption that the target state is reachable provided that
the parameter is known. Additional feature of the proposed method is that
Lyapunov-like functions have not been used in the construction of the
controllers.Comment: This work was supported by the Japan Society for the Promotion of
Science under Grant-in-Aid for Scientific Research (C) 23560535. This
manuscript is a former version of the manuscript the author has submitted to
International Journal of Adaptive Control and Signal Processin
Data-based approximate policy iteration for nonlinear continuous-time optimal control design
This paper addresses the model-free nonlinear optimal problem with
generalized cost functional, and a data-based reinforcement learning technique
is developed. It is known that the nonlinear optimal control problem relies on
the solution of the Hamilton-Jacobi-Bellman (HJB) equation, which is a
nonlinear partial differential equation that is generally impossible to be
solved analytically. Even worse, most of practical systems are too complicated
to establish their accurate mathematical model. To overcome these difficulties,
we propose a data-based approximate policy iteration (API) method by using real
system data rather than system model. Firstly, a model-free policy iteration
algorithm is derived for constrained optimal control problem and its
convergence is proved, which can learn the solution of HJB equation and optimal
control policy without requiring any knowledge of system mathematical model.
The implementation of the algorithm is based on the thought of actor-critic
structure, where actor and critic neural networks (NNs) are employed to
approximate the control policy and cost function, respectively. To update the
weights of actor and critic NNs, a least-square approach is developed based on
the method of weighted residuals. The whole data-based API method includes two
parts, where the first part is implemented online to collect real system
information, and the second part is conducting offline policy iteration to
learn the solution of HJB equation and the control policy. Then, the data-based
API algorithm is simplified for solving unconstrained optimal control problem
of nonlinear and linear systems. Finally, we test the efficiency of the
data-based API control design method on a simple nonlinear system, and further
apply it to a rotational/translational actuator system. The simulation results
demonstrate the effectiveness of the proposed method.Comment: 22 pages, 21 figures, submitted for Peer Revie
Full-Form Model-Free Adaptive Control for a Family of Multivariable System
This correspondence proposes a kind of model-free adaptive control (MFAC) on
the basis of full-form equivalent-dynamic-linearization model (EDLM) for the
multivariable nonlinear system. Compared with the current MFAC, i) this control
law does not have denominator, which is stemmed from the norm of the inverse
matrix and it inevitably misses the coupling relationships among the inputs and
outputs (I/O) of systems. ii) the current restrictive assumption of a
diagonally dominant matrix is reduced to extend its application. iii) the MFAC
based on full-form EDLM is more general than the current MFAC based on
partial-form and compact-form EDLM. At last, the convergence of tracking error
and the BIBO stability of controlled system have been proved, which is one of
the open questions in MFAC
Secondary Voltage Control of Microgrids Using Nonlinear Multiple Models Adaptive Control
This paper proposes a novel model-free secondary voltage control (SVC) for
microgrids using nonlinear multiple models adaptive control. The proposed
method is comprised of two components. Firstly, a linear robust adaptive
controller is designed to guarantee the voltage stability in the
bounded-input-bounded-output (BIBO) manner, which is more consistent with the
operation requirements of microgrids. Secondly, a nonlinear adaptive controller
is developed to improve the voltage tracking performance with the help of
artificial neural networks (ANNs). A switching mechanism is proposed to
coordinate such two controllers for guaranteeing the closed-loop stability
while achieving accurate voltage tracking. Given our method leverages a
data-driven real-time identification, it only relies on the input and output
data of microgrids without resorting to any prior information of primary
control and grid models, thus exhibiting good robustness, ease of deployment
and disturbance rejection
Continuous-Time Robust Dynamic Programming
This paper presents a new theory, known as robust dynamic pro- gramming, for
a class of continuous-time dynamical systems. Different from traditional
dynamic programming (DP) methods, this new theory serves as a fundamental tool
to analyze the robustness of DP algorithms, and in par- ticular, to develop
novel adaptive optimal control and reinforcement learning methods. In order to
demonstrate the potential of this new framework, four illustrative applications
in the fields of stochastic optimal control and adaptive DP are presented.
Three numerical examples arising from both finance and engineering industries
are also given, along with several possible extensions of the proposed
framework
Robust Policy Iteration for Continuous-time Linear Quadratic Regulation
This paper studies the robustness of policy iteration in the context of
continuous-time infinite-horizon linear quadratic regulation (LQR) problem. It
is shown that Kleinman's policy iteration algorithm is inherently robust to
small disturbances and enjoys local input-to-state stability in the sense of
Sontag. More precisely, whenever the disturbance-induced input term in each
iteration is bounded and small, the solutions of the policy iteration algorithm
are also bounded and enter a small neighborhood of the optimal solution of the
LQR problem. Based on this result, an off-policy data-driven policy iteration
algorithm for the LQR problem is shown to be robust when the system dynamics
are subjected to small additive unknown bounded disturbances. The theoretical
results are validated by a numerical example
A Separation-based Approach to Data-based Control for Large-Scale Partially Observed Systems
This paper studies the partially observed stochastic optimal control problem
for systems with state dynamics governed by partial differential equations
(PDEs) that leads to an extremely large problem. First, an open-loop
deterministic trajectory optimization problem is solved using a black-box
simulation model of the dynamical system. Next, a Linear Quadratic Gaussian
(LQG) controller is designed for the nominal trajectory-dependent linearized
system which is identified using input-output experimental data consisting of
the impulse responses of the optimized nominal system. A computational
nonlinear heat example is used to illustrate the performance of the proposed
approach.Comment: arXiv admin note: text overlap with arXiv:1705.09761,
arXiv:1707.0309
Verification for Machine Learning, Autonomy, and Neural Networks Survey
This survey presents an overview of verification techniques for autonomous
systems, with a focus on safety-critical autonomous cyber-physical systems
(CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances
in artificial intelligence (AI) and machine learning (ML) through approaches
such as deep neural networks (DNNs), embedded in so-called learning enabled
components (LECs) that accomplish tasks from classification to control.
Recently, the formal methods and formal verification community has developed
methods to characterize behaviors in these LECs with eventual goals of formally
verifying specifications for LECs, and this article presents a survey of many
of these recent approaches
Value and Policy Iteration in Optimal Control and Adaptive Dynamic Programming
In this paper, we consider discrete-time infinite horizon problems of optimal
control to a terminal set of states. These are the problems that are often
taken as the starting point for adaptive dynamic programming. Under very
general assumptions, we establish the uniqueness of solution of Bellman's
equation, and we provide convergence results for value and policy iteration
A Decoupled Data Based Approach to Stochastic Optimal Control Problems
This paper studies the stochastic optimal control problem for systems with
unknown dynamics. A novel decoupled data based control (D2C) approach is
proposed, which solves the problem in a decoupled "open loop-closed loop"
fashion that is shown to be near-optimal. First, an open-loop deterministic
trajectory optimization problem is solved using a black-box simulation model of
the dynamical system using a standard nonlinear programming (NLP) solver. Then
a Linear Quadratic Regulator (LQR) controller is designed for the nominal
trajectory-dependent linearized system which is learned using input-output
experimental data. Computational examples are used to illustrate the
performance of the proposed approach with three benchmark problems.Comment: arXiv admin note: substantial text overlap with arXiv:1711.01167,
arXiv:1705.0976
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