Optimal switching between controlled subsystems with free mode sequence

Heydari, Ali; Balakrishnan, S. N.

doi:10.1016/j.neucom.2014.08.030

Cited by 19 publications

(11 citation statements)

References 37 publications

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“…Moreover, once the state becomes less than 1, if switching to mode 1 is eventually needed, i.e., the switching cost is unavoidable, then the switching should happen immediately based on Eq. (27), as done in Fig. 2.a.…”

Section: A Examplementioning

confidence: 99%

“…These potentials motivated the author of this study to investigate the application of ADP to switching problems in his PhD research. This was done through developing solutions to problems with fixed mode sequence and fixed number of switching [25], [26], problems with free mode sequence and controlled subsystems [27], and also problems with free mode sequence and autonomous subsystems [28]. The interesting feature of these developments is the fact that they provide approximate optimal solution for a vast domain of initial conditions.…”

Section: Introductionmentioning

confidence: 99%

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Feedback Solution to Optimal Switching Problems With Switching Cost

Heydari

2016

IEEE Trans. Neural Netw. Learning Syst.

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The problem of optimal switching between nonlinear autonomous subsystems is investigated in this paper where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to utilization of different modes. The mode sequence is unspecified and a switching cost term is used in the cost function for penalizing each switching. It is shown that once a switching cost is incorporated, the optimal cost-to-go function depends on the subsystem which was active at the previous time step. Afterward, an approximate dynamic programming-based method is developed, which provides an approximation of the optimal solution to the problem in a feedback form and for different initial conditions. Finally, the performance of the method is analyzed through numerical examples.

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Section: A Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feedback Solution to Optimal Switching Problems With Switching Cost

Heydari

2016

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

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“…ADP solutions for optimal control of switched systems with free mode sequence were investigated in [3], [5]- [10]. As for the fixed mode sequence, a transformation was introduced in [11] to treat the switching times as parameters.…”

Section: Introductionmentioning

confidence: 99%

Sub-Optimal Tracking in Switched Systems With Controlled Subsystems and Fixed-Mode Sequence Using Approximate Dynamic Programming

Sardarmehni

Song

2019

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive

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Optimal tracking in switched systems with controlled subsystem and Discrete-time (DT) dynamics is investigated. A feedback control policy is generated such that a) the system tracks the desired reference signal, and b) the optimal switching time instants are sought. For finding the optimal solution, approximate dynamic programming is used. Simulation results are provided to illustrate the effectiveness of the solution.

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“…Most optimal problems come with finite-horizon limitations in real life and here are some solutions. Heydari and Balakrishnan [23][24][25][26][27] developed a sequential of neural networks to account for optimal weights at each time step. Zhao et al [28,29] solved finite-horizon optimal problem by applying time-vary basis functions with additional time-to-go terms.…”

Section: Introductionmentioning

confidence: 99%

Finite‐Horizon Optimal Tracking Guidance for Aircraft Based on Approximate Dynamic Programming

Wan

Chang

et al. 2019

Mathematical Problems in Engineering

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Referring to the optimal tracking guidance of aircraft, the conventional time based kinematics model is transformed into a downrange based model by independent variable replacement. The deviations of in-flight altitude and flight path angle are penalized and corrected to achieve high precision tracking of reference trajectory. The tracking problem is solved as a linear quadratic regulator applying small perturbation theory, and the approximate dynamic programming method is used to cope with the solving of finite-horizon optimization. An actor-critic structure is established to approximate the optimal tracking controller and minimum cost function. The least squares method and Adam optimization algorithm are adopted to learn the parameters of critic network and actor network, respectively. A boosting trajectory with maximum final velocity is generated by Gauss pseudospectral method for the validation of guidance strategy. The results show that the trained feedback control parameters can effectively resist random wind disturbance, correct the initial altitude and flight path angle deviations, and achieve the goal of following a given trajectory.

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Optimal switching between controlled subsystems with free mode sequence

Cited by 19 publications

References 37 publications

Feedback Solution to Optimal Switching Problems With Switching Cost

Feedback Solution to Optimal Switching Problems With Switching Cost

Sub-Optimal Tracking in Switched Systems With Controlled Subsystems and Fixed-Mode Sequence Using Approximate Dynamic Programming

Finite‐Horizon Optimal Tracking Guidance for Aircraft Based on Approximate Dynamic Programming

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