Controller tuning by means of evolutionary multiobjective optimization: current trends and applications

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research.The first author is grateful for the hospitality and availability of the UTC at the University of Sheffield during his academic research stay at 2011; especially to Dr. P.J. Fleming for his valuable comments and insights in the development of this paper. This work was partially supported by Grant FPI-2010/19 and project PAID-2011/2732 from the Universitat Politecnica de Valencia and projects TIN2011-28082 and ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness.Reynoso Meza, G.; Blasco Ferragud, FX.; Sanchís Saez, J.; Martínez Iranzo, MA. (2014). Controller tuning by means of evolutionary multiobjective optimization: current trends and applications. Control Engineering Practice. 28:58-73. https://doi.org/10.1016/j.conengprac.2014.03.003S58732

Pareto Optimization of a Five-Degree of Freedom Vehicle Vibration Model Using a MultiObjective Uniform-Diversity Genetic Algorithm (MUGA), Engineering

a b s t r a c t In this paper, a new multi-objective uniform-diversity genetic algorithm (MUGA) with a diversity preserving mechanism called the e-elimination algorithm is used for Pareto optimization of a fivedegree of freedom vehicle vibration model considering the five conflicting functions simultaneously. The important conflicting objective functions that have been considered in this work are, namely, seat acceleration, forward tire velocity, rear tire velocity, relative displacement between sprung mass and forward tire and relative displacement between sprung mass and rear tire. Further, different pairs of these objective functions have also been selected for 2-objective optimization processes. The comparison of the obtained results with those in the literature demonstrates the superiority of the results of this work. It is shown that the results of 5-objective optimization include those of 2-objective optimization and, therefore, provide more choices for optimal design of a vehicle vibration model

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Robust Geotechnical Design - Methodology and Applications

This dissertation is aimed at developing a novel robust geotechnical design methodology and demonstrating this methodology for the design of geotechnical systems. The goal of a robust design is to make the response of a system insensitive to, or robust against, the variation of uncertain geotechnical parameters (termed noise factors in the context of robust design) by carefully adjusting design parameters (those that can be controlled by the designer such as geometry of the design). Through an extensive investigation, a robust geotechnical design methodology that considers explicitly safety, robustness, and cost is developed. Various robustness measures are considered in this study, and the developed methodology is implemented with a multi-objective optimization scheme, in which safety is considered as a constraint and cost and robustness are treated as the objectives. Because the cost and the robustness are conflicting objectives, the robust design optimization does not yield a single best solution. Rather, a Pareto front is obtained, which is a collection of non-dominated optimal designs. The Pareto front reveals a trade-off relationship between cost and robustness, which enables the engineer to make an informed design decision according to a target level of cost or robustness. The significance and versatility of the new design methodology are illustrated with multiple geotechnical applications, including the design of drilled shafts, shallow foundations, and braced excavations

Robust and Multi-Objective Model Predictive Control Design for Nonlinear Systems

The multi-objective trade-off paradigm has become a very valuable design tool in engineering problems that have conflicting objectives. Recently, many control designers have worked on the design methods which satisfy multiple design specifications called multi-objective control design. However,the main challenge posed for the MPC design lies in the high computation load preventing its application to the fast dynamic system control in real-time. To meet this challenge, this thesis has proposed several methods covering nonlinear system modeling, on-line MPC design and multi-objective optimization. First, the thesis has proposed a robust MPC to control the shimmy vibration of the landing gear with probabilistic uncertainty. Then, an on-line MPC method has been proposed for image-based visual servoing control of a 6 DOF Denso robot. Finally, a multi-objective MPC has been introduced to allow the designers consider multiple objectives in MPC design. In this thesis, Tensor Product (TP) model transformation as a powerful tool in the modeling of the complex nonlinear systems is used to find the linear parameter-varying (LPV) models of the nonlinear systems. Higher-order singular value decomposition (HOSVD) technique is used to obtain a minimal order of the model tensor. Furthermore, to design a robust MPC for nonlinear systems in the presence of uncertainties which degrades the system performance and can lead to instability, we consider the parameters of the nonlinear systems with probabilistic uncertainties in the modeling using TP transformation. In this thesis, a computationally efficient methods for MPC design of image-based visual servoing, i.e. a fast dynamic system has been proposed. The controller is designed considering the robotic visual servoing system's input and output constraints, such as robot physical limitations and visibility constraints. The main contributions of this thesis are: (i) design MPC for nonlinear systems with probabilistic uncertainties that guarantees robust stability and performance of the systems; (ii) develop a real-time MPC method for a fast dynamical system; (iii) to propose a new multi-objective MPC for nonlinear systems using game theory. A diverse range of systems with nonlinearities and uncertainties including landing gear system, 6 DOF Denso robot are studied in this thesis. The simulation and real-time experimental results are presented and discussed in this thesis to verify the effectiveness of the proposed methods

Otimização paramétrica robusta multiobjetivo aplicada em suspensão veicular

A presente dissertação aplica uma metodologia de otimização robusta multiobjetivo ao problema da otimização de parâmetros da suspensão de um modelo numérico de meio carro com 5 graus de liberdade. A fim de aumentar o conforto do motorista do veículo sem prejudicar a dirigibilidade, a função objetivo escolhida foi a aceleração rms ponderada conforme a norma ISO 2631 (1997) com restrição no espaço de trabalho da suspensão. A otimização robusta é baseada em uma abordagem probabilística, mais completa do que aquela baseada em intervalos. A solução é comparada com uma otimização determinística, que não leva em consideração as incertezas. O estudo leva em conta diferentes aproximações presentes na literatura para a média e desvio padrão da função e da restrição, comparando os benefícios e prejuízos dos métodos. A solução gerada pela otimização robusta multiobjetivo escolhida resulta em uma média de aceleração rms ponderada de 0,205 /ଶ, contra 0,183 /ଶ da otimização determinística. Estas soluções, robusta e determinística, representam uma redução de 85,25% e 86,82% da aceleração da configuração de referência, respectivamente. No entanto, a probabilidade de falha calculada a partir do método de Monte Carlo com 25000 amostras mostrou que a otimização robusta permaneceu dentro do intervalo de segurança aceitável do espaço de trabalho da suspensão que foi estipulado em 10%, com apenas 8,69% de chance de falha da restrição, contra 66,23% de chance de falha para a solução determinística.This dissertation applies a multiobjective robust optimization methodology to the suspension optimization problem of a 5 degrees of freedom half-car numerical model. In order to increase the driver’s comfort without compromising the drivability, the chosen objective function was the weighted rms acceleration according to ISO 2631 (1997) with constrain regarding the suspension working space. The robust optimization is based in a probabilistic approach, more complete compared to the interval based approach. The study accounts for different approximation approaches present in the literature for the mean and deviation of function and constrain, comparing the advantages and disadvantages of each method. The chosen solution generated by the multiobjective robust optimization results in a mean for weighted rms acceleration of 0.205 /² against 0.183 /ଶ for the deterministic solution. These solutions, robust and deterministic, represent a reduction of 85.25% and 86.82% of the acceleration of the reference configuration, respectively. However, the failure probability calculated with the Monte Carlo method using 25000 samples, show that the robust optimization remained within the acceptable safety range of the suspension workspace which has been set to 10%, with an 8.69% chance of failure, against 66.23% chance of failure for the deterministic solution

Integrated optimisation for dynamic modelling, path planning and energy management in hybrid race vehicles

Simulation software has for many years been developed to enhance the research and development phase of new vehicle introductions. With the introduction of the testing embargo in most forms of world championship motorsport, model validation is a necessity. To optimise the unknown vehicle and tyre parameters and to reduce the error between measured and simulated data in such a multi-input multi-output non-convex optimisation problem, a novel multi-objective particle swarm optimisation (PSO) technique is applied to ensure a fully validated vehicle model is developed and analysed for speed and performance. These optimisation algorithms are further developed to explore the trajectory planning problem to improve the lap time for the shortest path, minimum curvature and a combined approach, producing optimal racing line pathways and vehicle dynamic inputs and output responses by exploring trajectories and vehicle traction circle limits. Finally, a hybrid electric vehicle transient dynamics model for the control of energy management is presented. The hybrid powertrain contains an internal combustion engine, kinetic energy recovery system and heat energy recovery system with deployment and harvesting control parameters. The performance of single-objective and multi-objective particle swarm optimisation algorithms are compared and analysed. The proposed simulation model and optimisation techniques are applied to address an array of problems, including model validation, racing line trajectory design, fastest lap time problem, and energy management strategies. All results are validated and optimised with respect to the experimental data collected on the real track in Silverstone to ensure the results can be applied to physical real-world scenarios

Hybrid Electromagnetic Vibration Isolation Systems

Traditionally, dynamic systems are equipped with passive technologies like viscous shock absorbers and rubber vibration isolators to attenuate disturbances. Passive elements are cost effective, simple to manufacture, and have a long life span. However, the dynamic characteristics of passive devices are fixed and tuned for a set of inputs or system conditions. Thus in many applications when variation of input or system conditions is present, sub-optimal performance is realized. The other fundamental flaw associated with passive devices is that they expel the undesired kinetic energy as heat. Recently, the introduction of electromagnetic technologies to the vibration isolation systems has provided researchers with new opportunities for realizing active/semi-active vibration isolation systems with the additional benefit of energy regeneration (in semi-active mode). Electromagnetic vibration isolators are often suffer from a couple of shortcomings that precludes their implementations in many applications. Examples of these short comings include bulky designs, low force density, high energy consumption (in active mode), and fail-safe operation problem. This PhD research aims at developing optimal hybrid-electromagnetic vibration isolation systems to provide active/semi-active and regenerative vibration isolation for various applications. The idea is to overcome the aforementioned shortcomings by integrating electromagnetic actuators, conventional damping technologies, and stiffness elements into single hybrid packages. In this research, for both semi-active and active cases, hybrid electromagnetic solutions are proposed. In the first step of this study, the concept of semi-active hybrid damper is proposed and experimentally tested that is composed of a passive hydraulic and a semi-active electromagnetic components. The hydraulic medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and energy regeneration to the hybrid design. Based on the modeling and optimization studies, presented in this work, an extended analysis of the electromagnetic damping component of the hybrid damper is presented which can serve as potent tool for the designers who seek maximizing the adaptability (and regeneration capacity) of the hybrid damper. The experimental results (from the optimized design) show that the damper is able to produce damping coefficients of 1300 and 0-238 Ns/m through the viscous and electromagnetic components, respectively. In particular, the concept of hybrid damping for the application of vehicle suspension system is studied. It is shown that the whole suspension system can be adjusted such that the implementation of the hybrid damper, not only would not add any adverse effects to the main functionally of the suspension, but it would also provide a better dynamics, and enhance the vehicle fuel consumption (by regenerating a portion of wasted vibration energy). In the second step, the hybrid damper concept is extended to an active hybrid electromagnetic vibration isolation systems. To achieve this target, a passive pneumatic spring is fused together with an active electromagnetic actuator in a single hybrid package. The active electromagnetic component maintains a base line stiffness and support for the system, and also provides active vibration for a wide frequency range. The passive pneumatic spring makes the system fail-safe, increases the stiffness and support of the system for larger masses and dead loads, and further guarantees a very low transmissibility at high frequencies. The FEM and experimental results confirmed the high force density of the proposed electromagnetic component, comparing to a voice coil of similar size. In the proposed design, with a diameter of ~125 mm and a height of ~60 mm, a force variation of ~318 N is obtained for the currents of I=±2 A. Furthermore, it is demonstrated that the proposed actuator has a small time constant (ratio of inductance to resistance for the coils) of less than 5.2 ms, with negligible eddy current effect, making the vibration isolator suitable for wide bandwidth applications. According to the results, the active controllers are able to enhance the performance of the passive elements by up to 80% and 95% in terms of acceleration and force transmissibilities, respectively

Advanced Modeling and Research in Hybrid Microgrid Control and Optimization

This book presents the latest solutions in fuel cell (FC) and renewable energy implementation in mobile and stationary applications. The implementation of advanced energy management and optimization strategies are detailed for fuel cell and renewable microgrids, and for the multi-FC stack architecture of FC/electric vehicles to enhance the reliability of these systems and to reduce the costs related to energy production and maintenance. Cyber-security methods based on blockchain technology to increase the resilience of FC renewable hybrid microgrids are also presented. Therefore, this book is for all readers interested in these challenging directions of research