CLF-Based Control Design for Unknown Multiinput Nonlinear Systems With Good Transient Performance

Kosmatopoulos, Elias B.; Piovesan, Jorge

doi:10.1109/tac.2010.2063190

Cited by 20 publications

(28 citation statements)

References 15 publications

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“…As a matter of fact, our approach is able to provide with efficient solutions even in cases where the approximation terms O(1/L) are "large". This is due to the use of the stochastic approximation (adaptive optimization) [3] nature of the PCAO algorithm which achieves to compensate for the effect of the approximation terms O(1/L) by "averaging their effect to zero" .…”

Section: A Hjb Approximationmentioning

confidence: 99%

“…To overcome all the above problems, we employ the socalled Cognitive-based Adaptive Optimization algorithm [3], [15] appropriately extended for the needs of our problem. More precisely, a revised version of the CAO algorithm (abbreviated as PCAO) is proposed and described in Table I.…”

Section: X(t)pmentioning

confidence: 99%

“…One of the key attributes of CAO is that it employs adaptive and stochastic approximation techniques for estimating/learning the objective function. As it was shown in [3], [15], the employment of these techniques results in an adaptive optimization scheme that is equivalent to an approximate gradient descent method without the need for calculating the gradient of the objective criterion or the use of any type of differentiation.…”

Section: X(t)pmentioning

confidence: 99%

“…The basic "ingredient" of the proposed ap-S. Baldi proach is the Cognitive-based Adaptive Optimization (CAO) [3]- [4]. By combining the CAO algorithm with a recently introduced approximate solution to the Hamilton-JacobiBellman equation [5], a new optimization algorithm is developed that is specifically tailored for the needs and objectives of simulation-based control design, with the capability of efficiently handling large-scale problems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

Baldi

Michailidis

Jula

et al. 2013

52nd IEEE Conference on Decision and Control

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Recently, there has been a growing interest towards simulation-based control design (co-simulation), where the controller utilizes an optimizer to minimize or maximize an objective function (system performance) whose evaluation involves simulation of the system to be controlled. However, existing simulation-based approaches are not able to handle in a computationally efficient way large-scale optimization problems involving hundreds or thousands of states and parameters. In this paper, we propose and analyze a new simulation-based control design approach, employing an adaptive optimization algorithm capable of efficiently handle large-scale control problems. The convergence properties of the proposed algorithm are established. Simulation results exhibit efficiency of the proposed approach when applied to large-scale problems. The simulation results employ two large-scale real-life systems for which conventional popular optimization techniques totally fail to provide an efficient simulation-based control design.

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Section: A Hjb Approximationmentioning

confidence: 99%

Section: X(t)pmentioning

confidence: 99%

Section: X(t)pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

Baldi

Michailidis

Jula

et al. 2013

52nd IEEE Conference on Decision and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since computing the optimal controller actions is in general an NP-complete problem and thus impossible to be implemented in real-time, there has been recently a significant effort and research activity towards designing and implementing controllers that are approximately optimal, i.e., providing a control design that is both practically feasible and approximates the performance of the optimal controller at a satisfactory level [1], [2]. Recently, an alternative method for the approximately optimal control of nonlinear system has been developed, the so called Convex Control Design (ConvCD) methodology [3], [4], [5]. Contrary to the existing approaches, the ConvCD methodology converts the problem of constructing an approximately optimal controller into a convex optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A scalable iterative convex design for nonlinear systems

Baldi

Ioannou

Kosmatopoulos

2012

2012 American Control Conference (ACC)

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Abstract-A recently developed control scheme for approximately optimal control of nonlinear systems is the so-called Convex Control Design (ConvCD) methodology, that transforms the control problem of generic nonlinear systems into a convex optimization problem. The ConvCD approach constructs a polynomial controller approximating the optimal control law: such design does not provide a scalable controller as it requires the use of a polynomial controller, which is not scalable, especially in large-scale applications. This problem is overcome in this paper by modifying the ConvCD formulation so that the optimal control law is approximated with a Multi-Controller with Mixing: that is, the polynomial controller is substituted by linear control elements plus mixing signals that are responsible for smoothly switching from one linear element to another. The stability properties of the Multi-Controller with Mixing are analyzed and an iterative approach is proposed to solve the resulting optimization problem. The resulting procedure aims at the development of a scalable and modular architecture for nonlinear systems, in order to allow for easier implementation and re-configurability. A numerical example is used to show the effectiveness of the method.

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Robust and nonlinear control literature survey (No. 22)

2011

Intl J Robust & Nonlinear

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of robust and nonlinear control six times a year. This list is compiled from a wide range of journals. Please note that although the list has been broadly categorized, these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Dr Jiqiang Wang at jiqiang.wang@nuaa.edu.cn. THEORETICAL DEVELOPMENTS IN ROBUST CONTROL

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CLF-Based Control Design for Unknown Multiinput Nonlinear Systems With Good Transient Performance

Cited by 20 publications

References 15 publications

A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

A scalable iterative convex design for nonlinear systems

Robust and nonlinear control literature survey (No. 22)

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CLF-Based Control Design for Unknown Multiinput Nonlinear Systems With Good Transient Performance

Cited by 20 publications

References 15 publications

A &#x201C;plug-n-play&#x201D; computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

A &#x201C;plug-n-play&#x201D; computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

A scalable iterative convex design for nonlinear systems

Robust and nonlinear control literature survey (No. 22)

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A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation