Single Agent Indirect Herding of Multiple Targets: A Switched Adaptive Control Approach

Licitra, Ryan A.; Bell, Zachary I.; Doucette, Emily A.; Dixon, Warren E.

doi:10.1109/lcsys.2017.2763968

Cited by 28 publications

(15 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the special case where A p , B p , K p , E p , and K r are constant and E r = 0 n c ×n c and (A p , B p ) is a controllable pair, our matrix A r in (4) agrees with the matrix A r in the more basic case from 30 and Yucelen et al 23 , where there is no frequency limiting control, and in that case the error dynamics have the form (13)…”

Section: Basic Casementioning

confidence: 55%

“…While usually dependent on some structural properties of the system, adaptive control is valuable because of its ability to solve tracking problems for a wide range of possible values of the unknown model parameters including higher‐order systems. One area where adaptive control is important is in aerospace problems . In basic adaptive control for some classes of control systems (such as linear systems), one can often use nonstrict (or weak) Lyapunov functions to ensure that tracking objectives are realized, and then achieve parameter identification (ie, convergence of the parameter estimates to the true parameter values) provided that a persistency of excitation (PE) condition is also satisfied.…”

Section: Introductionmentioning

confidence: 99%

“…One area where adaptive control is important is in aerospace problems. [5][6][7][8][9][10][11][12][13][14][15][16] In basic adaptive control for some classes of control systems (such as linear systems), one can often use nonstrict (or weak) Lyapunov functions to ensure that tracking objectives are realized, and then achieve parameter identification (ie, convergence of the parameter estimates to the true parameter values) provided that a persistency of excitation (PE) condition is also satisfied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tracking and parameter identification for model reference adaptive control

Malisoff

2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary We provide barrier Lyapunov functions for model reference adaptive control algorithms, allowing us to prove robustness in the input‐to‐state stability framework and to compute rates of exponential convergence of the tracking and parameter identification errors to zero. Our results ensure identification of all entries of the unknown weight and control effectiveness matrices. We provide easily checked sufficient conditions for our relaxed persistency of excitation conditions to hold. Our illustrative numerical example demonstrates the performance of the control methods.

show abstract

Section: Basic Casementioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tracking and parameter identification for model reference adaptive control

Malisoff

2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…In Licitra et al (2017Licitra et al ( , 2018, the authors discuss herding using a switched-system approach; the herder (defender) chases targets (evaders/attackers) sequentially by switching among them so that certain dwell-time conditions are satisfied to guarantee stability of the resulting trajectories. However, the assumption that only one of the targets is influenced by the herder at any time might be limiting and non-practical in real applications.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the aforementioned approaches for herding are limiting due to one or some of the following aspects: (1) simplified motion models (Varava et al, 2017;Pierson and Schwager, 2018), (2) absence of obstacles in the environment (Licitra et al, 2017(Licitra et al, , 2018Paranjape et al, 2018), (3) no consideration of intra-team collisions (Varava et al, 2017;Pierson and Schwager, 2018), ( 4) assumption on a particular form of potential field to model the repulsive motion of the attackers with respect to the defenders (Licitra et al, 2017(Licitra et al, , 2018Paranjape et al, 2018;Pierson and Schwager, 2018).…”

Section: Related Workmentioning

confidence: 99%

Aerial Swarm Defense by StringNet Herding: Theory and Experiments

2021

View full text Add to dashboard Cite

This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.

show abstract