Experimental analysis of a motorcycle semi-active rear suspension

The topic of this paper is the experimental analysis and development of a control system for a semi-active suspension in a 2-wheel vehicle. The control system is implemented via a semi-active electro-hydraulic damper located in the rear suspension of a motorbike. The entire design and analysis procedure is carried out: the semi-active damper is characterized; a wide range of control strategies is recalled and an innovative semi-active algorithm (Mix-1-Stroke) based on a single-sensor layout is proposed. The strategies are then implemented in the Electronic Control Unit of the motorbike. Tests, both on test-bench and on-road, are presented. The result is the comparative analysis of a wide portfolio of different suspension control strategies, which shows the effectiveness of the Mix-1-Sensor rationale.     

H ∞ control of a suspension with a magnetorheological damper

This research work presents an H _∞ controller based on a Takagi–Sugeno (T–S) fuzzy model for a two-degrees-of-freedom (2-DOF) one-quarter-vehicle semi-active suspension with a magnetorheological damper where the actuator dynamics are included in the control synthesis. These dynamics enclose nonlinear damper phenomena, avoided in many other studies, and that can improve the suspension system by means of a more accurate model. The objective is to obtain a semi-active suspension that considerably improves the passive suspension efficiency based on some frequency domain performance criteria. The advantage of having the T–S system as a reference is that each piecewise linear system can be exposed to the well-known control theory. Besides, the proposed solution is compared with the recent reported work to highlight its advantages. A case of study is included and simulation work supports the results. The methodology applied herein can be extended to a half-vehicle model, and to the four wheels to have a global chassis control in order to maximise passenger comfort and vehicle stability.     

A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems

Metaheuristic optimization algorithms address two main tasks in the process of problem solving: i) exploration (also called diversification) and ii) exploitation (also called intensification). Guaranteeing a trade-off between these operations is critical to good performance. However, although many methods have been proposed by which metaheuristics can achieve a balance between the exploration and exploitation stages, they are still worse than exact algorithms at exploitation tasks, where gradient-based mechanisms outperform metaheuristics when a local minimum is approximated. In this paper, a quasi-Newton method is introduced into a Chaotic Gravitational Search Algorithm as an exploitation method, with the purpose of improving the exploitation capabilities of this recent and promising population-based metaheuristic. The proposed approach, referred to as a Memetic Chaotic Gravitational Search Algorithm, is used to solve forty-five benchmark problems, both synthetic and real-world, to validate the method. The numerical results show that the adding of quasi-Newton search directions to the original (Chaotic) Gravitational Search Algorithm substantially improves its performance. Also, a comparison with the state-of-the-art algorithms: Particle Swarm Optimization, Genetic Algorithm, Rcr-JADE, COBIDE and RLMPSO, shows that the proposed approach is promising for certain real-world problems.     

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends.     

Robust controller design for a non-colocated spring-mass system via mixed 𝓁/∞ optimization

A successful controller design paradigm must take into account both model uncertainty and performance specifications. Model uncertainty can be addressed using the ??^∞ robust control framework. However, this framework cannot accommodate the realistic case where in addition to robustness considerations, the system is subject to time-domain specifications. We recently proposed design procedures to explicitly incorporate time-domain specifications into the ??^∞ framework. In this paper we apply these design procedures to the simple mass-spring system used as a benchmark in the 1990–1992 ACC, with the goal of minimizing the peak control effort while satisfying disturbance rejection, settling time, tracking and robustness specifications. The results show that there exist a severe trade-off between peak control action and robustness to unstructured model uncertainty.     

Performance assessment of advanced supervisory-regulatory control systems with subspace LQG benchmark

In this paper, a subspace method for LQG design and performance assessment is proposed for control systems in which supervisory and regulatory controllers are employed in a cascade structure. Usually, this is the case when an advanced controller is used in a supervisory control layer on the top of the regulatory control system, resulting in a cascade control structure. The objective of this study is to provide the optimal LQG control design in the cascade control structure and also to propose a method for calculation of the LQG ‘trade-off’ curves for performance assessment. The trade-off curve provides the optimal performance limit in terms of the best achievable input and output variances. Three possible scenarios for LQG control design in this supervisory-regulatory structure are discussed in the paper. The problem formulations are presented in the subspace framework to directly derive the control law and LQG trade-off curve without need of the conventional parametric models. A simulation example is provided to demonstrate the proposed method.     

履带车辆半主动悬挂多体及控制联合仿真研究

在履带车辆悬挂系统线性振动模型基础上，证明了悬挂最优阻尼比的存在，提出了统计意义上的最优阻尼控制策略，即为履带车辆悬挂半主动控制的理论基础。通过基于多体动力学理论的虚拟样机方法对某型履带车辆整车悬挂系统进行了仿真研究，结合“机械系统＋控制系统”联合仿真技术，对比了该车分别采用被动悬挂和半主动悬挂时的动力学特性，得出了该履带车辆采用半主动悬挂可显著降低车辆振动加速度，提高车辆乘坐舒适性的结论。从而验证了多体理论和联合仿真技术在履带车辆半主动悬挂技术的可行性。     

基于自适应遗传算法的半主动悬架模糊控制研究

模糊规则的正确选择是半主动悬架模糊控制器设计的关键和难点,本文提出一种自适应地选择交叉概率和变异概率的遗传算法,以车身垂直加速度均方根值为优化目标,对汽车半主动悬架模糊控制规则进行优化,以达到提高半主动悬架模糊控制器的控制效果,改善汽车行驶平顺性的目的。为了证明该优化方法的可行性,将该自适应遗传算法优化的模糊控制器对汽车半主动悬架进行控制,并建立Matlab文本与Simulink相结合的仿真模型。仿真结果表明：优化后的半主动悬架车身垂直加速度均方根值减小,汽车行驶的平顺性得到了提高。     

Integrated dynamic scheduling of hard and QoS degradable real-time tasks in multiprocessor systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. For tasks with hard deadlines, a deadline miss can be catastrophic while for Quality of Service (QoS) degradable tasks (soft real-time tasks) timely approximate results of poorer quality or occasional deadline misses are acceptable. Imprecise computation and (m,k)-firm guarantee are two workload models that quantify the trade-off between schedulability and result quality. In this paper, we propose dynamic scheduling algorithms for integrated scheduling of real-time tasks, represented by these workload models, in multiprocessor systems. The algorithms aim at improving the schedulability of tasks by exploiting the properties of these models in QoS degradation. We also show how the proposed algorithms can be adapted for integrated scheduling of multimedia streams and hard real-time tasks, and demonstrate their effectiveness in quantifying QoS degradation. Through simulation, we evaluate the performance of these algorithms using the metrics – success ratio (measure of schedulability) and quality. Our simulation results show that one of the proposed algorithms, multilevel degradation algorithm, outperforms the others in terms of both the performance metrics.     

Anytime diagnosis for reconfiguration

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.     

列车横向半主动悬挂模糊控制策略研究

针对17自由度列车横向半主动悬挂,提出以横向振动加速度最大值和均方根值作为评价指标,分析了列车横向悬挂模糊控制策略。列车横向半主动悬挂广泛地采用模糊控制调整阻尼值,减少横向振动,提高横向平稳性。因此,利用simulink建立列车横向悬挂模型,设计模糊控制策略直接法和间接法,进行了仿真。仿真结果显示,由于横移和摇头加速度的相互作用,使得前端横向合成加速度大于后端;通过加速度功率谱密度函数值分析对比,直接法综合控制效果好,好于间接法和被动悬挂控制。     

野草入侵模糊算法的半主动空气悬架研究

为了抑制由路面不平引起的车辆振动,结合磁流变阻尼器和空气弹簧的变阻尼/刚度特性,设计了含内置永磁体式磁流变阻尼器的半主动空气悬架系统.基于电磁学原理,对内置永磁体式磁流变阻尼器的力学特性建模.建立了1/4车辆二自由度动力学模型,并利用野草入侵算法对常规模糊算法规则进行优化,开发了野草入侵-模糊混合控制策略对内置永磁体磁流变阻尼器的空气悬架进行半主动控制.为验证该控制策略在磁流变空气悬架的半主动控制效果,进行了C级路面随机输入及凸块脉冲输入仿真分析,仿真结果可知,野草入侵-模糊控制策略能有效地提高半主动悬架系统的综合性能.并且通过台架试验进一步表明,利用该控制策略能够使车身振动加速度及悬架动挠度分别减小25.87%、35.13%.     

Semantic-based locking technique in object-oriented databases

This paper presents a locking-based concurrency control dealing with three important issues in object-oriented databases: semantics of methods, nested method invocation and referentially shared object. In the proposed scheme, locks are required for the execution of methods instead of atomic operations. This can reduce the locking overhead and deadlocks due to lock escalation. Also, a way of automating commutativity of methods is provided. In addition, concurrency is increased further by use of run-time information. A performance study is conducted by means of simulation using 007 benchmark. Through the simulation, the proposed technique is then compared with the two existing techniques. The performance studies show that the proposed scheme is superior to existing works.     

Vibration control of vehicle active suspension system using a new robust neural network control system

The main problem of vehicle vibration comes from road roughness. For that reason, it is necessary to control vibration of vehicle’s suspension by using a robust artificial neural network control system scheme. Neural network based robust control system is designed to control vibration of vehicle’s suspensions for full suspension system. Moreover, the full vehicle system has seven degrees of freedom on the vertical direction of vehicle’s chassis, on the angular variation around X-axis and on the angular variation around Y-axis. The proposed control system is consisted of a robust controller, a neural controller, a model neural network of vehicle’s suspension system. On the other hand, standard PID controller is also used to control whole vehicle’s suspension system for comparison.Consequently, random road roughnesses are used as disturbance of control system. The simulation results are indicated that the proposed control system has superior performance at adapting random road disturbance for vehicle’s suspension.     

Road-frequency adaptive control for semi-active suspension systems

In this paper, a road-frequency adaptive control for semi-active suspension systems is investigated. The control aims to improve the vehicle suspension performance (ride comfort and wheel handling) for all frequency regions of road disturbances. In order to achieve this aim, the control law is extended from the conventional skyhook control, and the controller gains are scheduled for various frequency regions of road disturbances. By using the data measured from a relative displacement sensor, a state estimator based on a Kalman filter for estimating the required state variables is designed. Road disturbance frequencies are estimated by using a first order zero-crossing algorithm. The efficiency of the proposed control is shown through numerical simulations.     

Approximation trade-offs in a Markovian stream warehouse: An empirical study

A large amount of the world's data is both sequential and low-level. Many applications need to query higher-level information (e.g., words and sentences) that is inferred from these low-level sequences (e.g., raw audio signals) using a model (e.g., a hidden Markov model). This inference process is typically statistical, resulting in high-level sequences that are imprecise. Once archived, these imprecise streams are difficult to query efficiently because of their rich semantics and large volumes, forcing applications to sacrifice either performance or accuracy. There exists little work, however, that characterizes this trade-off space and helps applications make an appropriate choice.In this paper, we study the effects – on both efficiency and accuracy – of various stream approximations such as ignoring correlations, ignoring low-probability states, or retaining only the single most likely sequence of events. Through experiments on a real-world RFID data set, we identify conditions under which various approximations can improve performance by several orders of magnitude, with only minimal effects on query results. We also identify cases when the full rich semantics are necessary. This study is the first to evaluate the cost vs. quality trade-off of imprecise stream models.We perform this study using Lahar, a prototype Markovian stream warehouse. A secondary contribution of this paper is the development of query semantics and algorithms for processing aggregation queries on the output of pattern queries—we develop these queries in order to more fully understand the effects of approximation on a wider set of imprecise stream queries.     

Consistency of noise covariance estimation in joint input–output closed-loop subspace identification with application in LQG benchmarking

The LQG trade-off curve has been used as a benchmark for control loop performance assessment. The subspace approach to estimating the LQG benchmark has been proposed in the literature which requires certain intermediate matrices in subspace identification as well as the covariance matrix of the noise. It is shown in this paper that many existing closed-loop identification methods do not give a consistent estimate of the noise covariance matrix. As a result, we propose an alternative subspace formulation for the joint input–output closed-loop identification for which the consistency of the required subspace matrices and noise covariance is guaranteed. Simulation studies and experimental results are provided to demonstrate the utility of the proposed method.     

Tracking control for magnetic-suspension systems with online unknown mass identification

This paper proposes a new nonlinear tracking control scheme with simultaneous unknown mass identification for magnetic suspension systems. Specifically, an amplitude-saturated adaptive control law is developed to achieve stable tracking and accurately estimate the unknown suspended mass simultaneously. The stability is assured with rigorous Lyapunov-based analysis. As far as we know, this is the first continuous control method for magnetic suspension systems with unknown levitated ball mass and actuator saturation, yielding an asymptotic result to achieve simultaneous tracking control and mass identification. Through hardware experiments, we verify the performance of the proposed method and compare it with existing methods.     

Slowing deforestation pace through subsidies: a differential game

This paper deals with the issue of deforestation, one of the main global environmental problems. We consider two players having different utilities for forest conservation. The first one (call it North) represents a set of countries having as an objective to maximize the size of tropical forest at a certain time horizon. The (South) second player's revenue function involves a trade-off between forest exploitation and agricultural activities. We study two scenarios. In the first one, the assumption is a laisser-faire policy, where the South solves an optimal control problem over a finite horizon. Revenue and optimal forest exploitation obtained represent a benchmark for the second scenario, where the North offers subsidies to the South to reduce the deforestation rate. The two scenarios are analyzed and compared in terms of strategies, outcomes and forest conservation.