Integrated Plant, Observer, and Controller Optimization With Application to Combined Passive/Active Automotive Suspensions

2003 ◽  
Author(s):  
Hosam K. Fathy ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy
Keyword(s):  
Optimization Problems ◽  
System Optimization ◽  
Optimization Strategy ◽  
Guarantee System ◽  
Control Optimization ◽  
Car Suspension ◽  
State Measurement ◽  
Full State ◽  
And Control ◽  
Original Derivation

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 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.

scholarly journals System Optimization Algorithm for 3DOF Quarter Car Active Suspension

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
M.K. Shehan ◽  
B.B. Sahari ◽  
N.A.B.A. Jalil ◽  
T.S. Hong ◽  
A.B. Asarry
Keyword(s):  
System Optimization ◽  
Suspension System ◽  
Control Force ◽  
Active System ◽  
Control Optimization ◽  
Car Suspension ◽  
Quarter Car ◽  
Three Degree Of Freedom ◽  
And Control ◽  
The Impact

This paper handles the synergy between the design and control optimization problem for an active car suspension system consisting both active and passive components. The dynamics of the suspension system are modeled utilizing a three degree of freedom (3DOF), linear with time invariant quarter car model with capability to capture the impact of the passive stiffness on suspension deflection depending up on the spectral density of road disturbances. Direct transcription, a strategy which guarantees system optimality, is presented and utilized to find the optimal design of the suspension system. The active system dynamics were analyzed with modified level of control force to examine how dynamic system should be designed accordingly when the active control force is introduced.

Enhancement of SPSA algorithm performance using reservoir quality maps: Application to coupled well placement and control optimization problems

2020 ◽  
Vol 189 ◽  
pp. 106984 ◽  
Author(s):  
Behzad Pouladi ◽  
Abdorreza Karkevandi-Talkhooncheh ◽  
Mohammad Sharifi ◽  
Shahab Gerami ◽  
Alireza Nourmohammad ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Reservoir Quality ◽  
Well Placement ◽  
Algorithm Performance ◽  
Control Optimization ◽  
And Control ◽  
Quality Maps

On a Successive Approximation Technique in Solving Some Control System Optimization Problems

1963 ◽  
Vol 85 (2) ◽  
pp. 177-180 ◽  
Author(s):  
Masanao Aoki
Keyword(s):  
Successive Approximation ◽  
Optimization Problems ◽  
Structural Information ◽  
System Optimization ◽  
Extremal Problems ◽  
Approximation Technique ◽  
Minimizing Sequences ◽  
Approximation Techniques ◽  
Control Optimization ◽  
Computational Procedures

It has been realized for some time that most realistic optimization problems defy analytical solutions in closed forms and that in most cases it is necessary to resort to judicious combinations of analytical and computational procedures to solve problems. For example, in many optimization problems, one is interested in obtaining structural information on optimal and “good” suboptimal policies. Very often, various analytical as well as computational approximation techniques need be employed to obtain clear understandings of structures of policy spaces. The paper discusses a successive approximation technique to construct minimizing sequences for functionals in extremal problems, and the techniques will be applied, to a class of control optimization problems given by: Minv  J(v)=Minv  ∫01g(u.v)dt, where du/dt = h(u, v), h(u, v) linear in u and v, and where u and v are, in general, elements of Banach spaces. In Section 2, the minimizing sequences are constructed by approximating g(u, v) by appropriate quadratic expressions with linear constraining differential equations. It is shown that under the stated conditions the functional values converge to the minimal value monotonically. In Section 3, an example is included to illustrate some of the techniques discussed in the paper.

Nested Optimization of an Elevator and Its Gain-Scheduled LQG Controller

2002 ◽  
Author(s):  
Hosam K. Fathy ◽  
Scott A. Bortoff ◽  
G. Scott Copeland ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy
Keyword(s):  
Ride Comfort ◽  
Optimization Problems ◽  
Torque Ripple ◽  
Guide Rail ◽  
Control Optimization ◽  
Drive Motor ◽  
Rail Irregularity ◽  
Optimal System Design ◽  
And Control ◽  
Gain Scheduled

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. Sequential plant/control optimization produces an optimal ride only when the torque ripple is the dominant disturbance. Otherwise, passive vibration reduction decreases the controller’s authority over the vibrations, hence coupling the plant and control optimization problems. Combined plant/controller optimization, using a nested strategy, mitigates this coupling and finds the correct optimal system design.

Multi- Versus Single-Level Dynamic Synthesis/Design and Operation/Control Optimizations of a PEMFC System

2008 ◽  
Author(s):  
Meng Wang ◽  
Kihyung Kim ◽  
Michael R. von Spakovsky ◽  
Douglas J. Nelson
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Proton Exchange ◽  
Optimization Techniques ◽  
Optimization Strategy ◽  
Single Level ◽  
Control Optimization ◽  
Synthesis Design ◽  
Gradient Based ◽  
Operation Control

As primary tools for the development of energy systems, optimization techniques have been studied for decades. However, for large-scale synthesis/design and operation/control optimization problems, it may turn out that it is impractical to solve the entire problem as a single optimization problem. In this paper, a multi-level optimization strategy, dynamic iterative local-global optimization (DILGO), is utilized for the synthesis/design and operation/control optimization of a 5 kWe PEMFC (Proton Exchange Membrane Fuel Cell) system. The strategy decomposes the system into three subsystems: a stack subsystem (SS), a fuel processing subsystem (FPS), and a work and air recovery subsystem (WRAS) and, thus, into three optimization sub-problems. To validate the decomposition strategy, the results are compared with a single-level dynamic optimization, in which the whole system is optimized together. In addition, for the purpose of comparison between different optimization algorithms, gradient-based optimization results are compared with those for a hybrid heuristic/gradient-based optimization algorithm.

A Hybrid Multi-Agent-Based BFPSO Algorithm for Optimization of Benchmark Functions

2019 ◽  
Vol 29 (07) ◽  
pp. 2050112
Author(s):  
Renuka Kamdar ◽  
Priyanka Paliwal ◽  
Yogendra Kumar
Keyword(s):  
Optimization Problems ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
System Optimization ◽  
High Dimensional ◽  
Potential Candidate ◽  
Optimization Strategy ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Multi Agent

The goal to provide faster and optimal solution to complex and high-dimensional problem is pushing the technical envelope related to new algorithms. While many approaches use centralized strategies, the concept of multi-agent systems (MASS) is creating a new option related to distributed analyses for the optimization problems. A novel learning algorithm for solving the global numerical optimization problems is proposed. The proposed learning algorithm integrates the multi-agent system and the hybrid butterfly–particle swarm optimization (BFPSO) algorithm. Thus it is named as multi-agent-based BFPSO (MABFPSO). In order to obtain the optimal solution quickly, each agent competes and cooperates with its neighbors and it can also learn by using its knowledge. Making use of these agent–agent interactions and sensitivity and probability mechanism of BFPSO, MABFPSO realizes the purpose of optimizing the value of objective function. The designed MABFPSO algorithm is tested on specific benchmark functions. Simulations of the proposed algorithm have been performed for the optimization of functions of 2, 20 and 30 dimensions. The comparative simulation results with conventional PSO approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low-and high-dimensional functions. The optimization strategy is general and can be used to solve other power system optimization problems as well.

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