On combined plant and control optimization (original) (raw)
Related papers
2003
The plant and control optimization problems are coupled in the sense that solving them sequentially does not guarantee system optimality. This paper extends previous studies of this coupling by relaxing their assumption of full state measurement availability. An original derivation of first-order necessary conditions for plant, observer, controller, and combined optimality furnishes coupling terms quantifying the underlying trilateral coupling. Special scenarios where the problems decouple are pinpointed, and a nested optimization strategy that guarantees system optimality is adopted otherwise. Applying these results to combined passive/active car suspension optimization produces a suspension design outperforming its passive, active, and sequentially optimized passive/active counterparts.
2003
This paper examines the plant and controller optimization problems. One can solve these problems sequentially, iteratively, using a nested (or bi-level) strategy, or simultaneously. Unlike the nested and simultaneous strategies, the sequential and iterative strategies fail to guarantee system-level optimality. This is because the plant and controller optimization problems are coupled. This coupling is introduced using a simple experiment. To prove it theoretically, the necessary conditions for combined plant and controller optimality are derived. These combined optimality conditions differ from the individual sets of necessary conditions for plant and controller optimality by a coupling term that reflects the plant design's influence on the plant dynamics and control input constraints. d e h, g
Simultaneous Optimization of Structure and Control for Vibration Suppression
Journal of Vibration and Acoustics, 1999
A method of simultaneous optimization of structure and control using mixed H2 and H" norms of the transfer function as the objective function is proposed and the modeling and formulation of simultaneous optimization problems associated with this approach are discussed in this paper. Simultaneous optimization is realized by iteratively executing structural optimization and controller optimization. Both serial and parallel approaches to combine structural optimization and controller optimization are investigated. They are applied to the simultaneous optimization of the crosssectional parameters of a spring-supported beam and the parameters of the controller used to actively suppress the vibration of the beam. The performance of both displacement output and control input is improved significantly after simultaneous optimization. The simulation results show the great potential advantages of simultaneous optimization over traditional design methods and the effectiveness of the proposed approach.
Simultaneous optimization of controlled structures
Computational Mechanics, 1988
A formulation is presented for finding the combined optimal design of a structural system and its control by defining a composite objective function as a linear combination of two components; a structural objective and a control objective. When the structural objective is a function of the structural design variables only, and when the control objective is represented by the quadratic functional of the response and control energy, it is possible to analytically express the optimal control in terms of any set of “admissible” structural design variables. Such expression for the optimal control is used recursively in an iterative Newton-Raphson search scheme, the goal of which is to determine the corresponding optimal set of structural design variables that minimize the combined objective function. A numerical example is given to illustrate the computational procedure. The results indicate that significant improvement of the combined optimal design can be achieved over the traditional separate optimization.
On the coupling between the plant and controller optimization problems
… 2001. Proceedings of …, 2001
This paper examines the plant and controller optimization problems. One can solve these problems sequentially, iteratively, using a nested (or bi-level) strategy, or simultaneously. Unlike the nested and simultaneous strategies, the sequential and iterative strategies fail to guarantee system-level optimality. This is because the plant and controller optimization problems are coupled. This coupling is introduced using a simple experiment. To prove it theoretically, the necessary conditions for combined plant and controller optimality are derived. These combined optimality conditions differ from the individual sets of necessary conditions for plant and controller optimality by a coupling term that reflects the plant design's influence on the plant dynamics and control input constraints.
2014 5th International Conference on Intelligent Systems, Modelling and Simulation, 2014
This paper demonstrates optimization of collocated sensor-actuator location and the controller gains of active vibration control system. Ant colony optimization algorithm is employed for this purpose. Instead of using equation-based modeling for the system, the plant is a finite element model developed in COMSOL Multiphysics software, which later interfaced with the MATLAB-coded optimization algorithm using the Livelink for MATLAB feature. The benchmark model is a simply supported thin plate excited and attenuated by two piezoelectric patches. The optimization is based on the average energy reduction across a frequency range between 11 Hz to 50 Hz, which covers the first three modes. It is found that the maximum attenuation achieved is 68.31% using optimal values of sensor-actuator location and controller gains.
Integrated optimal structural and vibration control design
Structural Optimization
An integrated design procedure which is composed of structural design, control design, and actuator locations design is proposed in this paper. First, a composite objective function, formed by a structural and a control objective, is optimized in steady state through the homogenization design method. Then an independent modal space control algorithm (IMSC) is performed on this optimal structure to reduce the dynamic response. Finally, to minimize the control force while still obtaining the same modal response for the controlled modes, the optimal choice for actuator locations is discussed.
A comparative study of optimal linear controllers for vibration suppression
Journal of The Franklin Institute-engineering and Applied Mathematics, 2002
The performance enhancement of dynamic systems is accomplished by the application of active control components. Control strategies are derived to accomplish the task. Among the various control strategies, those that are designed to minimize a specified cost function while satisfying the necessary system constraints are referred to as optimal controllers. An important constraint is the amount of power and energy consumed by the control device. In this paper, the effect of optimal control strategies on the power and energy requirement of control devices is investigated.
Nested optimization of an elevator and its gain-scheduled lqg controller
2002
ABSTRACT This paper studies the combined optimization of an elevator's design (plant) and LQG controller for ride comfort. Elevator dynamics and primary vibration sources (drive motor torque ripple and guide rail irregularity) are modeled using an object-oriented language. The resulting model is nonlinear. Elevator vibrations are minimized with respect to both the design and the LQG controller. LQG gains are scheduled versus cab mass and height for robustness.