Nonminimal state space approach to multivariable ramp metering control of motorway bottlenecks

Taylor, C. James; Young, Peter C.; Chotai, A.; Whittaker, Joe

doi:10.1049/ip-cta:19982383

Cited by 20 publications

(8 citation statements)

References 8 publications

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“…On‐ramp metering regulation is still an open problem due to the many factors that can influence traffic. On‐line on‐ramp metering resorts to the use of feedback control to take into account current traffic situation; some examples can be cited: Integral control , Linear Quadratic Control and Linear Quadratic Integral Control , Proportional‐Integral plus Linear Quadratic Control , Model Predictive Control or Adaptive Control .…”

Section: Introductionmentioning

confidence: 99%

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Stability of Local On‐Ramp Metering Control Laws

Álvarez-Icaza

Rosas-Jaimes

Lárraga

2016

Asian Journal of Control

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A framework for stability analysis of local on‐ramp metering control strategies based on the cell transmission model is presented. Within this framework, it is possible to formulate Lyapunov and input‐state stability results for on‐ramp metering control strategies in an open section of highway with on‐ramps. Using this analysis, recommendations for the design of on‐ramp metering control laws set points are derived. Two examples on the use of such analysis are presented. One deals with the stability analysis of a local on‐ramp metering control law and the other with the design of a disturbance observer that, used in combination with the local on‐ramp metering control law, provides a more robust response to traffic regulation. Simulation results are included that confirm the possibility of using this framework to test the impact of local on‐ramp metering control strategies.

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Section: Introductionmentioning

confidence: 99%

“…ear Quadratic Integral Control [4], Proportional-Integral plus Linear Quadratic Control [11], Model Predictive Control [8] or Adaptive Control [6].…”

Section: Introductionmentioning

confidence: 99%

Stability of Local On‐Ramp Metering Control Laws

Álvarez-Icaza

Rosas-Jaimes

Lárraga

2016

Asian Journal of Control

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“…Note that, whichever PIP structure is chosen, it is always converted into the equivalent incremental feedback or incremental forward path form of the algorithm [3]. This provides an inherent means of avoiding integral windup when the controller is subjected to constraints on the actuator signal, as in the present example.…”

Section: Control Structurementioning

confidence: 99%

“…3,4,5], including the 1998 Gasifier Challenge [6]. The latter research was based on the same pilot integrated plant for an air blown gasification cycle, as that utilised in the present study [7,8].…”

Section: Introductionmentioning

confidence: 99%

Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation

Taylor

Shaban

2006

IEE Proceedings - Control Theory and Applications

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Multivariable Proportional-Integral-Plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data-based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete-time transfer function models obtained in this manner, are represented in a NonMinimum State Space (NMSS) form suitable for PIP control system design.Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resort to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non-minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm.

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“…The results of this test show that ramp metering not only serves the purposes of improving traffic flow and traffic safety, but also improves travel time reliability [17,73]. A number of studies have simulated ramp metering for different transportation networks and traffic scenarios, with different control approaches, and with the use of microscopic and macroscopic traffic flow models [40,46,68,92,94,113]. Generally the total network travel time is considered as the performance measure and is improved by about 0.39 %-30 % when using ramp metering.…”

Section: Field Tests and Simulation Studiesmentioning

confidence: 99%