On the adaptive control of linear systems using the open-loop-feedback-optimal approach

Ku, R. T.; Athans, Michael

doi:10.1109/tac.1973.1100368

Cited by 36 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first is open-loop (OL) scheduling, in which the scheduling is performed only after all multistep decisions are exhausted [18]. The second is open-loop feedback (OLF) scheduling, in which only the first scheduling decision is executed, and the nonmyopic scheduling is repeated at each time step [18][19][20][21][22]. Although our algorithm description is based on OL scheduling, the optimization framework for both scheduling schemes is the same [18].…”

Section: Nonmyopic Sensor Schedulingmentioning

confidence: 99%

Nonmyopic Sensor Scheduling and its Efficient Implementation for Target Tracking Applications

Chhetri

Morrell

Papandreou-Suppappola

2006

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

We propose two nonmyopic sensor scheduling algorithms for target tracking applications. We consider a scenario where a bearingonly sensor is constrained to move in a finite number of directions to track a target in a two-dimensional plane. Both algorithms provide the best sensor sequence by minimizing a predicted expected scheduler cost over a finite time-horizon. The first algorithm approximately computes the scheduler costs based on the predicted covariance matrix of the tracker error. The second algorithm uses the unscented transform in conjunction with a particle filter to approximate covariance-based costs or information-theoretic costs. We also propose the use of two branch-and-bound-based optimal pruning algorithms for efficient implementation of the scheduling algorithms. We design the first pruning algorithm by combining branch-and-bound with a breadth-first search and a greedy-search; the second pruning algorithm combines branch-and-bound with a uniform-cost search. Simulation results demonstrate the advantage of nonmyopic scheduling over myopic scheduling and the significant savings in computational and memory resources when using the pruning algorithms.

show abstract

Section: Nonmyopic Sensor Schedulingmentioning

confidence: 99%

Nonmyopic Sensor Scheduling and its Efficient Implementation for Target Tracking Applications

Chhetri

Morrell

Papandreou-Suppappola

2006

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The majority of the computational expense in calculating the additional quantities hxi and hij is in calculating the terms which are also required in calculating hi,, and /!2,, in the CE control law. This is one of the major advantages of the proposed cautious controller over the existing multi-step horizon cautious controllers of Tse and Bar-Shalom (1973) and Ku and Athans (1973), which have no closed form solution and are computationally expensive. Also, the arbitrary time horizon N in the cost function of (3) allows greater flexibility in controller design than in the cautious control approaches of Hughes and Jacobs (1973), Wittenmark (1975), Milito et al (1980, and Mookerjee and Bar Shalom (1989), where the prediction horizon is restricted to only one timestep.…”

Section: Z{k + 1) = A{k)z{k) + B{k)u(k) + W(k) Y(k) = Cz(k) + V(k)mentioning

confidence: 99%

“…Many of these techniques have no closed-form solution and are too complicated to practically implement (Tse and Bar-Shalom, 1973;Ku and Athans, 1973;Mookerjee and Bar Shalom, 1989). Sternby (1976) develops a dual controller for the specific case that the plant is a one-dimensional four-state Markov chain.…”

mentioning

confidence: 99%