P1-TS fuzzy scheduling control system design using local pole placement and interval analysis

Abstract

Abstract. The linear parameter-varying (LPV) discrete-time model based design of a fuzzy scheduling control scheme is developed through incorporating the advantages of P1-TS theory, and applying the local pole placement method and interval analysis of closed-loop system polynomial coefficients. The synthesis of fuzzy scheduling control scheme is proposed in the form of iterative procedure, which enables to find the appropriate number of intervals of a fuzzy interpolator ensuring that a family of local linear controllers places closed-loop polynomial coefficients within a desired range. The computational complexity of multidimensional fuzzy scheduling control scheme synthesis is reduced using a fundamental matrix method and recursive procedure for fuzzy rule-based interpretation. The usability of the proposed method is illustrated by an implementation example and experimental results obtained on a laboratory scaled overhead crane.

Design of simultaneous input-shaping-based SIRMs fuzzy control for double-pendulum-type overhead cranes

“…This appears to be beneficial both for control design and analysis. But the method is criticized for its exponential rule explosion [1]. The SIRMs fuzzy method presented by Yi et al [2] can dramatically reduce the numbers of rules and parameters.…”

“…Overhead cranes are widely used in many places, such as warehouses, disaster sites, nuclear plants, shipyards and construction sites, to transport objects because of their heavy payload capabilities [1,2]. They are one of the most important elements in global industrialization and play an irreplaceable role in economic development.…”

“…Various methods concerning the control problem have been presented, i.e., fuzzy logic control [1][2][3], energy-based control [4], adaptive control [5], trajectory planning [6], input shaping [7], sliding mode control [8][9][10], to name but a few. See [11] for a review of these efforts in the crane control problem.…”

“…See [11] for a review of these efforts in the crane control problem. In most of the reports about overhead crane control, including but not limited to [1][2][3][4][5][6][7][8][9][10], the mass-point assumption integrates all the suspending parts. The assumption leads to single-pendulum dynamics and most of the previous work on crane control has focused on how to resist the single-pendulum oscillations.…”

Higher order fuzzy logic in controlling selective catalytic reduction systems

“…Examples of fuzzy logic systems applications are broadly known, e.g. [3][4][5]. Another work on FLSs in the industry and the environment is [6].…”