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Near-optimal control with adaptive receding horizon for discrete-time piecewise affine systems * *This work was supported by the Chinese Scholarship Council, as well as by the Agence Universitaire de la Francophonie (AUF) and the Romanian Institute for Atomic Physics (IFA) under the AUF-RO project NETASSIST
2017
IFAC-PapersOnLine
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We consider the infinite-horizon optimal control of discrete-time, Lipschitz continuous piecewise affine systems with a single input. Stage costs are discounted, bounded, and use a 1 or ∞-norm. ...
Rather than using the usual fixed-horizon approach from model-predictive control, we tailor an adaptive-horizon method called optimistic planning for continuous actions (OPC) to solve the piecewise affine ...
Keywords: piecewise affine systems, nonlinear predictive control, optimistic planning, near-optimality analysis. ...
doi:10.1016/j.ifacol.2017.08.806
fatcat:sbhy4wk2rbgmtebddj6pwl37xm
Receding-horizon control for max-plus linear systems with discrete actions using optimistic planning
2016
2016 13th International Workshop on Discrete Event Systems (WODES)
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The proposed optimistic planning approach allows us to limit the computational budget and also yields a characterization of the level of near-optimality of the resulting solution. ...
The resulting optimal control problem is solved based on an optimistic planning algorithm. ...
Therefore, optimistic planning can be used for optimal control of very general nonlinear discretetime systems and in addition it is able to deal with uncertainties because of infinite search space and ...
doi:10.1109/wodes.2016.7497879
dblp:conf/wodes/XuBBS16
fatcat:swsioq4zxjbm7bwj5gnh4rsfqi
Near-optimal strategies for nonlinear networked control systems using optimistic planning
2013
2013 American Control Conference
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Exploiting a recent optimistic planning algorithm from the artificial intelligence field, we propose two control strategies that take into account communication constraints induced by the use of the network ...
We consider the scenario where a controller communicates with a general nonlinear plant via a network, and must optimize a performance index. ...
Buşoniu is also associated with the Automation Department, Technical University of Cluj-Napoca, Romania. J. Daafouz is also with the IUF. ...
doi:10.1109/acc.2013.6580294
fatcat:critourz6jabffn6d5ctm5lx5y
Optimistic Planning for the Near-Optimal Control of General Nonlinear Systems with Continuous Transition Distributions
2014
IFAC Proceedings Volumes
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It works for very general nonlinear dynamics and cost functions, and its analysis establishes a tight relationship between computation invested and near-optimality. ...
Optimistic planning is an optimal control approach from artificial intelligence, which can be applied in receding horizon. ...
Here we consider the optimal control of general nonlinear systems with nonquadratic, discounted cost functions, in discrete time and for discrete inputs (control actions). ...
doi:10.3182/20140824-6-za-1003.01295
fatcat:axvl4efj4jfv5a2fr4jyxnkzby
Optimistic planning with long sequences of identical actions for near-optimal nonlinear control
2014
2014 IEEE International Conference on Automation, Quality and Testing, Robotics
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Optimistic planning for deterministic systems (OPD) is an algorithm able to find near-optimal control for very general, nonlinear systems. ...
This paper proposes an adaptation of OPD for a specific subclass of control problems where control actions do not change often (e.g. bang-bang, time-optimal control). ...
Note that γ ∈ (0, 1) is the discount factor and optimistic planning is adapted to such discounted optimal control problems.
C. ...
doi:10.1109/aqtr.2014.6857826
fatcat:lczozk76engxzjcphs5y3bb6mm
A general approach for consensus using optimistic planning
2013
2013 American Control Conference
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We propose a consensus approach based on optimistic planning (OP), a predictive control algorithm that finds near-optimal control actions for any nonlinear dynamics and reward (cost) function. ...
An important challenge in multiagent systems is consensus, in which the agents are required to synchronize certain controlled variables of interest, often using only an incomplete and time-varying communication ...
SINGLE-AGENT OPTIMISTIC PLANNING Consider an optimal control problem for a deterministic, discrete-time nonlinear system x k+1 = f (x k , u k ) with states x and actions u. ...
doi:10.1109/acc.2013.6579891
fatcat:jun4ar3ojregbhqku2nmckiyiq
Optimistic planning with a limited number of action switches for near-optimal nonlinear control
2014
53rd IEEE Conference on Decision and Control
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We consider infinite-horizon optimal control of nonlinear systems where the actions (inputs) are discrete. ...
It inherits the generality of the OP class, so it works for nonlinear, nonsmooth systems with nonquadratic costs. ...
BACKGROUND: OPTIMAL CONTROL AND OPTIMISTIC PLANNING FOR DETERMINISTIC SYSTEMS Consider a Markov decision process (MDP) describing an optimal control problem with state x ∈ X, action u ∈ U , transition ...
doi:10.1109/cdc.2014.7039935
dblp:conf/cdc/MatheBMS14
fatcat:o2omsypunfeurbiy7p6e3nz4iq
Planning methods for the optimal control and performance certification of general nonlinear switched systems
2015
2015 54th IEEE Conference on Decision and Control (CDC)
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We use an optimistic planning (OP) algorithm that can solve general optimal control with discrete inputs such as switches. ...
The first is optimal control of the switches so as to minimize the discounted infinite-horizon sum of the costs. ...
Optimistic planning [8] , [15] is used to search the space of possible sequences of switches. ...
doi:10.1109/cdc.2015.7402777
dblp:conf/cdc/BusoniuBDM15
fatcat:udivpqmagbbr3kfskjkqngtuke
Real-Time Optimistic Planning with Action Sequences
2015
2015 20th International Conference on Control Systems and Computer Science
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Optimistic planning (OP) is a promising approach for receding-horizon optimal control of general nonlinear systems. ...
This generality comes however at large computational costs, which so far have prevented the application of OP to the control of nonlinear physical systems in real-time. ...
An optimistic planning (OP) [2] type of algorithm is adopted, which uses the model to compute an adaptive-horizon sequence of control actions that is near-optimal for the current state of the system, ...
doi:10.1109/cscs.2015.64
dblp:conf/cscs/WensveenBB15
fatcat:jcjmnzh7sffljdxpna45meyyxa
Near-optimal control of nonlinear switched systems with non-cooperative switching rules
2017
2017 American Control Conference (ACC)
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We show how the framework can be used to model switched systems with time delays on the control channel, and provide an illustrative simulation for such a system with nonlinear modes. ...
For any combination of dwell times, OMSδ returns a sequence of switches that is provably near-optimal, and can be applied in receding horizon for closed loop control. ...
Near-Optimal Control of Nonlinear Switched Systems with Non-Cooperative Switching Rules Jihene Ben Rejeb, Lucian Buşoniu, Irinel-Constantin Morȃrescu, Jamal Daafouz Abstract-This paper presents a predictive ...
doi:10.23919/acc.2017.7963352
dblp:conf/amcc/RejebBMD17
fatcat:cbcgnriafbb4nkfxvqedbyim2y
Topology-preserving flocking of nonlinear agents using optimistic planning
2015
Control Theory and Technology
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We build a flocking method for general nonlinear agent dynamics, by using at each agent a near-optimal control technique from artificial intelligence called optimistic planning. ...
By defining the rewards to be optimized in a well-chosen way, the preservation of the interconnection topology is guaranteed, under a controllability assumption. ...
This is related to the stability of the nearoptimal control produced by OP, and since the objective function is discounted such a stability property is a big open question in the optimal control field ...
doi:10.1007/s11768-015-4107-5
fatcat:pljx4qhxavd57blfpellf5htoi
Near-Optimal Strategies for Nonlinear and Uncertain Networked Control Systems
2016
IEEE Transactions on Automatic Control
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Index Terms-networked control systems, optimal control, nonlinear systems, planning, predictive control. L. Buşoniu is with the Technical University of Cluj-Napoca, Romania, lucian@busoniu.net. R. ...
Exploiting some optimistic planning algorithms from the artificial intelligence field, we propose two control strategies that take into account the communication constraints induced by the use of the network ...
All this is done for general, nonlinear and not necessarily smooth systems, and for general bounded rewards, where the optimal control objective is to maximize the discounted sum of rewards. ...
doi:10.1109/tac.2015.2484358
fatcat:3sffxhzagnhuhmwgirhszrysoy
Planning for optimal control and performance certification in nonlinear systems with controlled or uncontrolled switches
2017
Automatica
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We use optimistic planning (OP) algorithms that can solve general optimal control with discrete inputs such as switches. ...
The first is optimal control of the switching rule so as to optimize the infinite-horizon discounted cost. ...
[39] for optimal control. A recent nonlinear result is given in [44] , where the stability properties of optimal mode inputs are analyzed for Markov jump systems with nonlinear controlled modes. ...
doi:10.1016/j.automatica.2016.12.027
fatcat:4wblrlfidvhkxbz626guk5lgze
A Survey of Optimistic Planning in Markov Decision Processes
[chapter]
2013
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
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The resulting optimistic planning framework integrates several types of optimism previously used in planning, optimization, and reinforcement learning, in order to obtain several intuitive algorithms with ...
We review a class of online planning algorithms for deterministic and stochastic optimal control problems, modeled as Markov decision processes. ...
INTRODUCTION This chapter considers online algorithms for problems in which a nonlinear, possibly stochastic dynamic system must be optimally controlled in discrete time. ...
doi:10.1002/9781118453988.ch22
fatcat:ix4pbp4qpvc2lp44ckd63o3xa4
Optimistic planning for sparsely stochastic systems
2011
2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)
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Index Terms-online planning, optimistic planning, Markov decision processes, stochastic systems, model-predictive control. ...
More specifically, the active selection method is optimistic in that it chooses the most promising states first, so the novel algorithm is called optimistic planning for sparsely stochastic systems. ...
OLOP works for general, nonsparsely stochastic systems with finitely many actions, but plans in 'open loop', using only sequences of actions. ...
doi:10.1109/adprl.2011.5967375
dblp:conf/adprl/BusoniuMSB11
fatcat:2goevcn74bf7bmpc37ysohz64e
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