Evaluation and Selection of Adaptive Traffic Control Strategies on Transportation Networks

2008 ◽  
Vol 2064 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Sandeep Mudigonda ◽  
Kaan Ozbay ◽  
Harsh Doshi
Keyword(s):  
Traffic Control ◽  
Transportation Networks ◽  
Control Strategies ◽  
Adaptive Traffic Control ◽  
Selection Of

scholarly journals Zero-Width Quasi-Sliding Mode Band in the Presence of Non-Matched Uncertainties

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3011
Author(s):  
Paweł Latosiński ◽  
Andrzej Bartoszewicz
Keyword(s):  
Sliding Mode Control ◽  
Discrete Time ◽  
Sliding Mode ◽  
Control Strategies ◽  
Relative Degree ◽  
Band Width ◽  
Representative Point ◽  
Mode Control ◽  
Sliding Motion ◽  
Selection Of

Sliding mode control strategies are well known for ensuring robustness of the system with respect to disturbance and model uncertainties. For continuous-time plants, they achieve this property by confining the system state to a particular hyperplane in the state space. Contrary to this, discrete-time sliding mode control (DSMC) strategies only drive the system representative point to a certain vicinity of that hyperplane. In established literature on DSMC, the width of this vicinity has always been strictly greater than zero in the presence of uncertainties. Thus, ideal sliding motion was considered impossible for discrete-time systems. In this paper, a new approach to DSMC design is presented with the aim of driving the system representative point exactly onto the sliding hyperplane even in the presence of uncertainties. As a result, the quasi-sliding mode band width is effectively reduced to zero and ideal discrete-time sliding motion is ensured. This is achieved with the proper selection of the sliding hyperplane, using the unique properties of relative degree two sliding variables. It is further demonstrated that, even in cases where selection of a relative degree two sliding variable is impossible, one can use the proposed technique to significantly reduce the quasi-sliding mode band width.

Frequency-Weighted Variable-Length Controllers Using Anytime Control Strategies

2011 ◽  
Author(s):  
Gundula B. Runge ◽  
Al Ferri ◽  
Bonnie Ferri
Keyword(s):  
Time Scale ◽  
Weighting Function ◽  
Control Strategies ◽  
Low Frequency ◽  
Low Frequencies ◽  
High Frequencies ◽  
Frequency Components ◽  
Computational Resources ◽  
Weighted Variable ◽  
Selection Of

This paper considers an anytime strategy to implement controllers that react to changing computational resources. The anytime controllers developed in this paper are suitable for cases when the time scale of switching is in the order of the task execution time, that is, on the time scale found commonly with sporadically missed deadlines. This paper extends the prior work by developing frequency-weighted anytime controllers. The selection of the weighting function is driven by the expectation of the situations that would require anytime operation. For example, if the anytime operation is due to occasional and isolated missed deadlines, then the weighting on high frequencies should be larger than that for low frequencies. Low frequency components will have a smaller change over one sample time, so failing to update these components for one sample period will have less effect than with the high frequency components. An example will be included that applies the anytime control strategy to a model of a DC motor with deadzone and saturation nonlinearities.

scholarly journals Transport modeling: An artificial immune system approach

2006 ◽  
Vol 16 (1) ◽  
pp. 3-30
Author(s):  
Dusan Teodorovic ◽  
Jovan Popovic ◽  
Panta Lucic
Keyword(s):  
Immune System ◽  
Real Time ◽  
Traffic Control ◽  
System Approach ◽  
Artificial Immune System ◽  
Control Strategies ◽  
Transportation Systems ◽  
Artificial Immune ◽  
Real Time Traffic ◽  
Wide Range

This paper describes an artificial immune system approach (AIS) to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions) that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies) for different antigens (different traffic "scenarios"). This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.

Sign in / Sign up

Export Citation Format

Share Document