Reliability-based Control Research Articles

Machine learning enhanced control co-design optimization of an immersion cooled battery thermal management system

The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption.

Double-time-scale non-probabilistic reliability-based controller optimization for manipulator considering motion error and wear growth

Motion error and wear growth are two crucial factors that determine the performance of the manipulator system. The two factors are distinct from the sensitivity to time and respectively indicate its control performance and lifespan level. In this paper, a double-time-scale non-probabilistic reliability (DTSNPR)-based optimization method, which considers time-sensitive factor motion error as time-dependent reliability (TDR) and time-insensitive factor clearance wear growth as time-independent reliability (TIR), is proposed to comprehensively evaluate and optimize the controller of the manipulator system in consideration of these multi-scale factors. Meanwhile, for a highly nonlinear response problem of a manipulator system, the adaptive subinterval collocation method (ASICM) which transfers the highly nonlinear uncertainty propagation problem into a sequence of small subintervals, is adopted to obtain the response range more precisely. The proposed DTSNPR-based optimization method is applied to three numerical manipulator systems and ensures each of them under a predefined level of reliability upon both motion error and wear growth. The results also indicate that the proposed ASICM owns an advantage over computational cost and precision, with only 0.4% error and 1% computational cost compared with the Monte-Carlo methods.

A Single-Loop Framework for the Reliability-Based Control Co-Design Problem in the Dynamic System

When solving the control co-design (CCD) problem using the simultaneous strategy in a deterministic manner, the uncertainty stemming from the stochastic design variables is ignored, and might have a negative influence on the performance of the dynamic system. In attempting to overcome the undesirable effect of the uncertainty, this research investigates the reliability-based control co-design (RB-CCD) problem and presents a single-loop framework for RB-CCD based on the modified RB-CCD model and single-loop approach (SLA). Specifically, the modified model is deduced by introducing additional design variables and equality constraints (state equations and algebraic equality constraints) so as to transform the probabilistic constraints into inequality constraints. Meanwhile, to enhance the solution efficiency, SLA transforms the modified RB-CCD model into an equivalent single-loop deterministic CCD model by incorporating the approximate reliability information of the stochastic design variables into the deterministic optimization. Finally, a numerical example and an engineering example are implemented to verify the feasibility and effectiveness of the single-loop RB-CCD optimization framework. The results demonstrate that the suggested single-loop framework dramatically improves the reliability of the dynamic system, and significantly increases the solving efficiency without compromising accuracy.

Reliability-based control co-design of horizontal axis wind turbines

Reliability-based control co-design of horizontal axis wind turbines

Direct control method for improving stability and reliability of nonlinear stochastic dynamical systems

Optimal control for improving the stability and reliability of nonlinear stochastic dynamical systems is of great significance for enhancing system performances. However, it has not been adequately investigated because the evaluation indicators for stability (e.g. maximal Lyapunov exponent) and for reliability (e.g. mean first-passage time) cannot be explicitly expressed as the functions of system states. Here, a unified procedure is established to derive optimal control strategies for improving system stability and reliability, in which a physical intuition-inspired separation technique is adopted to split feedback control forces into conservative components and dissipative components, the stochastic averaging is then utilized to express the evaluation indicators of performances of controlled system, the optimal control strategies are finally derived by minimizing the performance indexes constituted by the sigmoid function of maximal Lyapunov exponent (for stability-based control)/the reciprocal of mean first-passage time (for reliability-based control), and the mean value of quadratic form of control force. The unified procedure converts the original functional extreme problem of optimal control into an extremum value problem of multivariable function which can be solved by optimization algorithms. A numerical example is worked out to illustrate the efficacy of the optimal control strategies for enhancing system performance.

Remaining Lifetime Prediction for Reliability-based Dry Clutch Control

Increasing requirements for reliability of modern powertrainscan be achieved by predictive maintenance and reliabilitybasedcontrol based on lifetime prediction. This contributionpresents lifetime prediction for a dry clutch, being an essentialcomponent of automated manual transmissions. Modelbaseddevelopment of lifetime prediction requires knowledgeof dry clutch wear, which was identified in previous experiments.The derived wear model allows estimation of characteristicwear-dependent values, like friction lining materiallosses and friction coefficient changes. Based on these estimatedvalues the presented lifetime prediction was developedby fusing these estimated values into a health index (HI) describingthe systems healthiness. Furthermore, the remaininguseful lifetime (RUL) becomes predictable from observationsof health index trend using an exponential weightedmoving average. Eventually, the presented lifetime predictionwas implemented and tested on real-time operating hardwaresimilar to common transmission control units. In order tocontrol the system lifetime in normal operation, target trendsfor health index and predicted remaining useful lifetime weredefined. Based on trend deviations, a fuzzy-logic based controlstrategy was realized, which sets the optimization targetfor a reliability-based control. Thus, the optimization targetcan be varied between comfort-optimized or wear-optimizedclutch engagements. Finally, an outline of reliability-basedcontrol concepts is given.

Utility of probability density evolution method for experimental reliability-based active vibration control

The utility of probability density evolution method for reliability-based active vibration control of a cantilevered flexible beam is experimentally investigated. In this respect, an optimal linear quadratic regulator (LQR) is utilized together with an observer to design an online full-state feedback controller. In order to design a well-performing controller and to simulate the controller performance, a system model is obtained via identification techniques. Reliability tests are consequently performed to verify the effectiveness of the presented reliability assessment method as a foundation for reliability-based control. Subsequently, a hybrid metaheuristic optimization scheme of continuous ant colony system is used for simultaneous integrated tuning of the LQR and observer parameters through all processes. Optimization of the controller and the observer is performed with the objective of system structural reliability. An optimized version of classical LQR controller is also developed for making comparisons between the two control methods. Finally, a comparison is made between the mean power consumption of the 2 controllers. It is realized that the reliability-based controller decreases operating costs without compromising system safety.

Reliability-based remediation control for contaminated groundwater

Reliability-based remediation control for contaminated groundwater

Reliability-based controller placement algorithm in software defined networking

Software Defined Networking (SDN) change the routing architecture from distribution to centralization. Then the centralized controller placement becomes an important issue since network delay and synchronization will be affected. In this paper, the problem of controller placement to optimize the network average reliability is studied. We first consider the optimization problem for controller placement under the assumption with the shortest path between controller and switches, and a clustering based optimal controller placement algorithm and sub-optimal algorithm base on greedy algorithm are proposed, respectively. Then, we extend the controller placement problem to more general case with multi-paths between controller and switches. In this case, to reduce the computation complexity, a Reliability Factor is defined to replace the network average reliability. After that, modified clustering based and greedy based algorithms are proposed to make the optimal and sub-optimal solution, respectively. Finally, simulation results demonstrate the performance of the proposed algorithms better than regular placement stratagem with the Internet2 OS3E and the Internet Topology Zoo.

Non-probabilistic reliability analysis of active control system for structural vibration

To solve the reliability problem of active control system of structural vibration,considering the uncertainties in controlled structure,reliability analysis of the active control system for structural vibration based on non-probabilistic reliability model was studied. The uncertain parameters were expressed in the form of interval number or vector. Calculation method of responses of the closed-loop system with interval parameters was proposed based on interval mathematics. Combined with the non-probabilistic reliability model,nonprobabilistic reliability analysis method,which requires the bounds of the uncertainties rather than the probability density function,was established for active vibration control systems. This reliability analysis approach had certain theoretical significance for reliability analysis of open-loop system,closed-loop control system and reliability-based controller design. The results of numerical examples demonstrate the effectiveness of the proposed method and the applicability for complex structures compared with the Monte Carlo method that can be taken as the benchmark of accuracy for judgment.

Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease

Objective. To explore the use of classical feedback control methods to achieve an improved deep brain stimulation (DBS) algorithm for application to Parkinson's disease (PD). Approach. A computational model of PD dynamics was employed to develop model-based rational feedback controller design. The restoration of thalamocortical relay capabilities to patients suffering from PD is formulated as a feedback control problem with the DBS waveform serving as the control input. Two high-level control strategies are tested: one that is driven by an online estimate of thalamic reliability, and another that acts to eliminate substantial decreases in the inhibition from the globus pallidus interna (GPi) to the thalamus. Control laws inspired by traditional proportional-integral-derivative (PID) methodology are prescribed for each strategy and simulated on this computational model of the basal ganglia network. Main Results. For control based upon thalamic reliability, a strategy of frequency proportional control with proportional bias delivered the optimal control achieved for a given energy expenditure. In comparison, control based upon synaptic inhibitory output from the GPi performed very well in comparison with those of reliability-based control, with considerable further reduction in energy expenditure relative to that of open-loop DBS. The best controller performance was amplitude proportional with derivative control and integral bias, which is full PID control. We demonstrated how optimizing the three components of PID control is feasible in this setting, although the complexity of these optimization functions argues for adaptive methods in implementation. Significance. Our findings point to the potential value of model-based rational design of feedback controllers for Parkinson's disease.

토너먼트 스케줄링을 이용한 무선싱크 혼잡제어

무선 싱크 노드에서 중요도가 낮은 데이터 패킷들을 업 스트림 할 때 혼잡과 지연이 발생하며, 이것은 에너지 효율, 메모리, 버퍼 크기 및 처리율 등에 영향을 미친다. 본 논문에서는 토너먼트 스케줄링을 이용한 새로운 무선 싱크 혼잡 제어 메커니즘을 제안한다. 제안된 기법은 스트림 결정 모듈과 이를 적용하는 서비스 차별화 모듈로 구성된다. 토너먼트에서 승리한 최종 승자는 혼잡을 효율적으로 제어할 뿐만 아니라 혼잡 지연에 따른 패킷 손실을 최소화한다. 또한 승자는 에너지 소모를 감소시키고, QoS를 향상시킨다. 본 논문에서는 시뮬레이션을 통해서 혼잡지시자 기반 제어기법, 신뢰도 기반 제어 기법, 최선 전송 제어 기법, 그리고 제안된 기법의 효율성을 살펴보며, 결과를 통하여 제안된 기법의 성능이 효율적임을 제시한다. The up-streams of the continuous streaming of data packets with lower importance level in the wireless sink node can cause congestion and delay, they affect on energy efficiency, memory size, buffer size, and throughput. This paper proposes a new wireless sink congestion control mechanism based on tournament scheduling. The proposed method consists of two module parts: stream decision module part and service differentiation module part. The final winner in the tournament controls congestion effectively, minimizes packet loss due to congestion, decreases energy consumption, and improves QoS. The simulation result shows that the proposed method is more effective and has better performance compared with those of congestion descriptor-based control method, reliability-based control method, and best-effort transmission control method.

Semi-Active Control Using Magnetorhelogical Dampers with Output Feedback and Distributed Sensing

Control of seismic response of a building fitted with magnetorheological dampers is considered using Optimal Static Output Feedback (OSOF) for desired damper forces. The Modified Bouc-Wen damper model is used and two control voltage laws based on the MR constraint filter, i.e., Semi-inverse Quadratic Voltage Law and Semi-inverse On-Off Voltage Law, are proposed. These appear to perform at least as well as an existing Clipped Voltage Law. Comparisons with available results from a robust reliability-based controller show OSOF control to be quite effective. Controlled response using OSOF is compared with Linear Quadratic Guassian (LQG) and passive-on controllers. Moderate to substantial reduction in maximum peak/RMS responses is mostly obtained with base configuration of sensors when using OSOF control, and controller CPU time reduces by two orders of magnitude. Parametric studies regarding sensor configuration and state/control weighting matrices are performed in order to obtain effective control. Effective OSOF control requires drift feedback with drift sensor preferably collocated with damper.

Semi-Active Control Using Magnetorhelogical Dampers with Output Feedback and Distributed Sensing

Control of seismic response of a building fitted with magnetorheological dampers is considered using Optimal Static Output Feedback (OSOF) for desired damper forces. The Modified Bouc-Wen damper model is used and two control voltage laws based on the MR constraint filter, i.e., Semi-inverse Quadratic Voltage Law and Semi-inverse On-Off Voltage Law, are proposed. These appear to perform at least as well as an existing Clipped Voltage Law. Comparisons with available results from a robust reliability-based controller show OSOF control to be quite effective. Controlled response using OSOF is compared with Linear Quadratic Guassian (LQG) and passive-on controllers. Moderate to substantial reduction in maximum peak/RMS responses is mostly obtained with base configuration of sensors when using OSOF control, and controller CPU time reduces by two orders of magnitude. Parametric studies regarding sensor configuration and state/control weighting matrices are performed in order to obtain effective control. Effective OSOF control requires drift feedback with drift sensor preferably collocated with damper.

SERVICE LEVEL ANALYSIS OF (S - 1, S) INVENTORY POLICY FOR NEGATIVE BINOMIAL DISTRIBUTED FAILURES

This paper deals with the analysis of uncertain parameters in the (S - 1, S) inventory control policy. In order to determine the number of spare parts under the policy, parameters in a probability model for the number of failures during a replenishment lead time should be interpreted first. In case of the negative binomial distributed failures, the presented interpretation facilitates the incorporation of experts' opinions into the estimation of uncertain parameters. Determination of parameters via their interpretation is useful for deciding a service level in the area of reliability-based inventory control.

Numerical Approximations of Design Points in Reliability Analysis under Parametric Changes

Traditional approaches for repeatedly updating reliability estimates, as needed in reliability-based optimal designs or real-time system control, require the iterative application of a reliability method. This paper explores a new strategy for repeatedly estimating reliability under frequent parameter variations. The central idea is to update the design point in the parameter domain, rather than in the traditional random variable domain, by evaluating several parametric sensitivity measures which are systems of nonlinear first-order ordinary differential equations relating the design point to parameter changes. Four numerical algorithms for evaluating the sensitivity measures are developed using the Euler and the improved Euler algorithms. Two solution procedures are applied. One procedure solves for the updated design point directly, while the other solves for both the unit normal vector at the design point and the reliability index separately, and evaluates the product of these to determine the updated design point. The numerical techniques are thoroughly compared with the classical Hasofer and Lind-Rackwitz and Fiessler (HL-RF) algorithm in five numerical examples regarding efficiency and accuracy. It is found that they are efficient and robust under given conditions.

Structural protection using MR dampers with clipped robust reliability-based control

Magnetorheological (MR) damper is one of the most promising semi-active control devices for mitigating seismic response, and so, it has received significant attention in recent years. Because of the inherent nonlinear nature of MR damper devices, one challenge to effectively utilize this technology is the development of an appropriate control algorithm. A semi-active control methodology is presented to control structural systems using MR dampers that addresses structural modeling uncertainties. Based on the same idea as the clipped-optimal linear controller, the proposed semi-active control law uses a robust reliability-based active linear control methodology to calculate the reference control forces that the MR dampers attempt to produce approximately. These reference control forces can be calculated using incomplete response measurements at the current and/or previous time steps without any state estimation. The main purpose of the proposed approach is to achieve a more robust performance for uncertain structural systems subjected to uncertain strong-motion excitation. The performance of the proposed approach is demonstrated through a simulated example of a ten-story building with MR dampers installed.

Structural reliability-based control of moored interconnected structures

In this paper, a model of a futuristic fish farming structure is developed and problems related to interconnected marine structures and strategies for configuration control are studied. A control system is designed that: (1) ensures limited loading of the mooring system; (2) keeps the chain of surface modules aligned transversely to the incoming current, and; (3) ensures positive strain in the connectors between the modules. Control actuation is by means of a thruster mounted on the first module, and a hydrofoil mounted on the last module. The performance of the control system is demonstrated by simulations, and evaluated by a structural reliability criterion referred to as the delta index.

General conditions for online estimation and optimization of reliability characteristics

A novel approach to estimate reliability properties of systems or components individually during operation is presented. It is distinguished between slow and fast reliability states based on an equivalent system representation. Conditions for their observability and control are given and objectives for optimal reliability-based control are discussed in general.

Reliability-based control optimization for active base isolation systems

A probability-based active control synthesis is proposed for seismic base isolation of a structure that is modeled as a linear dynamical system subjected to uncertain future ground motions that are modeled as a stochastic process. The performance objective is the minimization of the probability of failure, where failure is defined as the first-passage of the system trajectory across a generalized set of hyperplanes in the system response space. Versions of the approach are described for the case with no model uncertainty, as well as for the case with uncertain model parameters with probabilistically distributed values. Numerical issues pertaining to the optimization of the controller are discussed. The method is illustrated in a civil engineering context through application to the eight-storey base isolation benchmark structure model, using an array of ideal active control devices working in tandem with the passive base isolation bearings. Controllers are presented for cases with specified and uncertain earthquake spectral parameters, and for two different actuator configurations. Transient simulations are presented for seven earthquake records, and the performances of the controllers are analyzed under a number of metrics. Comparisons with the performance of a related linear-quadratic controller are presented and discussed, both for stationary as well as transient response. Copyright © 2005 John Wiley & Sons, Ltd.