The CRONE suspension

The paper presents an active vibration control system based on low dynamic stiness of suspension. Using a simple two degrees-of-freedom system a few basic concepts of lowering suspension dynamic stiness are presented. Through reducing the dynamic component of force between the protected subsystem and remaining part of the system, considerable vibration suppression is achieved. Linear and nonlinear algorithms are proposed. In the case of nonlinear control algorithm, the sucient link between the protected subsystem and the remaining part of the system necessary to change the position of the protected subsystem is maintained. Experiments described in the paper cover two dierent cases. In the rst case, the suspension operated as the passive suspension, while in the second case, the active reduction system was included. The results are presented graphically.

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“…Utilizing equations (2,3) and (9), the equation describing the force generated by linear motor takes the form…”

Section: Nonlinear Control Algorithmmentioning

confidence: 99%

Active Suspension Based on Low Dynamic Stiffness

2013

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“…However, in the recent years, emergence of effective methods in differentiation and integration of non-integer order equations makes fractional-order systems more and more attractive for the A New Approach to Design Smith Predictor Based Fractional Order Controllers Khosro Khandani and Ali Akbar Jalali systems control community. The TID controller [10], the CRONE controllers [11], [12] and [13] and the fractional lead-lag compensator [14] and [15] are some of the well-known fractional-order controllers. In some of these papers it is verified that the fractional-order controllers can have better disturbance rejection ratios and less sensitivity to plant parameter variations compared to the traditional controllers [16].…”

Section: Fractional Calculus and Fractional Controlmentioning

confidence: 99%

A New Approach to Design Smith Predictor Based Fractional Order Controllers

Khandani¹,

Jalali²

2010

IJCEE

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I. INTRODUCTIONDead times, or time delays, are found in many processes in industry. Dead times are mainly caused by the time required to transport mass, energy or information, but they can also be caused by processing time or by the accumulation of time lags in a number of simple dynamic systems connected in series [1]. Compared to processes without delays, the presence of a delay in the process greatly complicates the analytical aspects of the control system design, making it more difficult to achieve a satisfactory level of control [2]. Time-delay has been a common phenomenon to overcome whenever we close a feedback loop for the purpose of controlling any system. Recent increase of control applications in the variety gives more importance to systematic methods to cope with time delay. In order to compensate the negative effects of time delay, a well-known and highly effective dead-time compensator for stable processes is Smith predictor [3]. In this predictor scheme, a mathematical model of the process is implemented in an internal feedback loop around a conventional controller. The distinctive scheme was proposed by O.J.M.Smith approximately 50 years ago, and is still attracting much attention for its usefulness. The major advantage of the Smith predictor is that delay issues can be ignored when designing the controller.Fractional-order dynamic systems and controllers, which are based on fractional-order calculus have been gaining attention in several research communities since the last few years [4]. A few recent works in this direction as well as

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“…Numerous investigations point out that fractional phenomena are encountered in many physics and engineering sciences, such as electromagnetics [1] and quantum mechanics [2] . Moreover, with the theoretical development of fractional differential equations [3] , fractional controllers have been proposed which possess more flexibilities and robustness, e.g., the P I λ D u controller [4] , the CRONE principle [5] , and other variations [6] . Many fundamentals and applications of fractional order control systems can be found in [7] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Bounded real lemmas for fractional order systems

Liang

Wei

Pan

et al. 2015

Int. J. Autom. Comput.

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This paper derives the bounded real lemmas corresponding to L∞ norm and H∞ norm (L-BR and H-BR) of fractional order systems. The lemmas reduce the original computations of norms into linear matrix inequality (LMI) problems, which can be performed in a computationally efficient fashion. This convex relaxation is enlightened from the generalized Kalman-YakubovichPopov (KYP) lemma and brings no conservatism to the L-BR. Meanwhile, an H-BR is developed similarly but with some conservatism. However, it can test the system stability automatically in addition to the norm computation, which is of fundamental importance for system analysis. From this advantage, we further address the synthesis problem of H∞ control for fractional order systems in the form of LMI. Three illustrative examples are given to show the effectiveness of our methods.

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The CRONE suspension

Cited by 280 publications

References 4 publications

Active Suspension Based on Low Dynamic Stiffness

Active Suspension Based on Low Dynamic Stiffness

A New Approach to Design Smith Predictor Based Fractional Order Controllers

Bounded real lemmas for fractional order systems

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