This paper presents a three-phase iterative direct current optimal power flow (DCOPF) algorithm with fictitious nodal demand. Power losses and realistic distribution system operating constraints such as line flow limits and phase imbalance limits are carefully modeled in the DCOPF formulation. The definition of locational marginal prices (LMPs) is extended to three-phase distribution systems. The three-phase LMP decomposition is derived based on the Lagrangian function. The proposed algorithm is implemented in an IEEE test case and compared with three-phase alternating current optimal power flow (ACOPF) algorithm. The simulation results show that the proposed DCOPF algorithm is effective in coordinating the operations of distributed energy resources (DERs) and managing phase imbalance and thermal overloading. The proposed iterative three-phase DCOPF algorithm provides not only a computationally efficient solution but also a good approximation to the ACOPF solution.
more »
« less
Optimal phase control of biological oscillators using augmented phase reduction
We develop a novel optimal control algorithm to change the phase of an oscillator using a minimum energy input, which also minimizes the oscillator’s transversal distance to the uncontrolled periodic orbit. Our algorithm uses a two-dimensional reduction technique based on both isochrons and isostables. We develop a novel method to eliminate cardiac alternans by connecting our control algorithm with the underlying physiological problem. We also describe how the devised algorithm can be used for spike timing control which can potentially help with motor symptoms of essential and parkinsonian tremor, and aid in treating jet lag. To demonstrate the advantages of this algorithm, we compare it with a previously proposed optimal control algorithm based on standard phase reduction for the Hopf bifurcation normal form, and models for cardiac pacemaker cells, thalamic neurons, and circadian gene regulation cycle in the suprachiasmatic nucleus. We show that our control algorithm is effective even when a large phase change is required or when the nontrivial Floquet multiplier is close to unity; in such cases, the previously proposed control algorithm fails.
more »
« less
- Award ID(s):
- 1635542
- PAR ID:
- 10075851
- Date Published:
- Journal Name:
- Biological cybernetics
- ISSN:
- 1432-0770
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In this article, we devise two related control algorithms to change the degree of synchrony of a population of noise-free, identical, uncoupled neural oscillators using a single control input. The algorithms are based on phase reduction, and use a population-level partial differential equation formulation to change the phase distribution of the neurons as desired. Motivated by the pathological neural synchronization hypothesized to be present in patients suffering from essential and parkinsonian tremor, we take our control objective to be the desynchronization of an initially synchronized neural population. Through numerical simulations, we are able to show that our algorithms work for both Type I and Type II neural populations. To demonstrate the versatility of our control algorithms, we also show that they can be applied to synchronize an initially desynchronized neural population as well. For the systems considered in this paper, the control algorithms can be applied to achieve any desired traveling-wave neural phase distribution, as long as the combination of initial and desired phase distributions is non-degenerate.more » « less
-
An immunotherapy trial often uses the phase I/II design to identify the optimal biological dose, which monitors the efficacy and toxicity outcomes simultaneously in a single trial. The progression-free survival rate is often used as the efficacy outcome in phase I/II immunotherapy trials. As a result, patients developing disease progression in phase I/II immunotherapy trials are generally seriously ill and are often treated off the trial for ethical consideration. Consequently, the happening of disease progression will terminate the toxicity event but not vice versa, so the issue of the semi-competing risks arises. Moreover, this issue can become more intractable with the late-onset outcomes, which happens when a relatively long follow-up time is required to ascertain progression-free survival. This paper proposes a novel Bayesian adaptive phase I/II design accounting for semi-competing risks outcomes for immunotherapy trials, referred to as the dose-finding design accounting for semi-competing risks outcomes for immunotherapy trials (SCI) design. To tackle the issue of the semi-competing risks in the presence of late-onset outcomes, we re-construct the likelihood function based on each patient's actual follow-up time and develop a data augmentation method to efficiently draw posterior samples from a series of Beta-binomial distributions. We propose a concise curve-free dose-finding algorithm to adaptively identify the optimal biological dose using accumulated data without making any parametric dose–response assumptions. Numerical studies show that the proposed SCI design yields good operating characteristics in dose selection, patient allocation, and trial duration.more » « less
-
This paper introduces a strategy for satisfying basic control objectives for systems whose dynamics are almost entirely unknown. This setting is motivated by a scenario where a system undergoes a critical failure, thus significantly changing its dynamics. In such a case, retaining the ability to satisfy basic control objectives such as reach-avoid is imperative. To deal with significant restrictions on our knowledge of system dynamics, we develop a theory of myopic control. The primary goal of myopic control is to, at any given time, optimize the current direction of the system trajectory, given solely the limited information obtained about the system until that time. Building upon this notion, we propose a control algorithm which simultaneously uses small perturbations in the control effort to learn local system dynamics while moving in the direction which seems to be optimal based on previously obtained knowledge. We show that the algorithm results in a trajectory that is nearly optimal in the myopic sense, i.e., it is moving in a direction that seems to be nearly the best at the given time, and provide formal bounds for suboptimality. We demonstrate the usefulness of the proposed algorithm on a high-fidelity simulation of a damaged Boeing 747 seeking to remain in level flight.more » « less
-
This paper studies the adaptive optimal control problem for a class of linear time-delay systems described by delay differential equations (DDEs). A crucial strategy is to take advantage of recent developments in reinforcement learning (RL) and adaptive dynamic programming (ADP) and develop novel methods to learn adaptive optimal controllers from finite samples of input and state data. In this paper, the data-driven policy iteration (PI) is proposed to solve the infinite-dimensional algebraic Riccati equation (ARE) iteratively in the absence of exact model knowledge. Interestingly, the proposed recursive PI algorithm is new in the present context of continuous-time time-delay systems, even when the model knowledge is assumed known. The efficacy of the proposed learning-based control methods is validated by means of practical applications arising from metal cutting and autonomous driving.more » « less
An official website of the United States government

