Hence it is a difficult task to control it efficiently. Therefore, a multistep heuristic dynamic programming mshdp method is developed for solving the optimal control problem of nonlinear discretetime systems. Supplementary damping controller design using direct. Modelfree dual heuristic dynamic programming ieee xplore. To handle the aforementioned challenges, a modelfree solution which does not consider the dynamics of the agents and does not use the graphs outneighbors weights is proposed in the following development. Datadriven heuristic dynamic programming with virtual reality. Section 3 contains the dynamic programming principle and the hjb partial integro. Comparison of heuristic dynamic programming and dual heuristic programming adapti v e critics for neurocontrol of a t urbogenerator ganesh k. An overview of research on adaptive dynamic programming. Modelfree value iteration solution for dynamic graphical. Mpc no need to develop process model develop policy from data directly able to work with complex nonlinear, stochastic environments fast online execution. Heuristic dynamic programming hdp, dual heuristic programming dhp and globalized dual heuristic programming gdhp, in the order of increasing power and complexity. Instead, it employs optimization principles to produce modelfree control strategies.
A modelfree robust policy iteration algorithm for optimal control of nonlinear systems. Adaptivecriticbased neural networks for aircraft optimal control. This article focuses on the implementation of an approximate dynamic programming algorithm in the discrete tracking control system of the threedegrees of freedom scorboter 4pc robotic manipulator. The heuristic dynamic programming approach in boost converters, sepehr saadatmand, pourya shamsi, and mehdi ferdowsi. Electrical and computer engineering faculty research. Section iii provides our simulation studies on two typical examples, followed by the discussion and conclusion in section iv. Both traditional heuristic dynamic programming algorithm and incremental. The feedback variables are completely based on local measurements from the generators. Modelfree dual heuristic dynamic programming request pdf. According to the outcome of the critic network, approximate dynamic programming can be divided into three families. Heuristic dynamic programming with internal goal representation 2103 fig. This is called a modelfree approach, because it does not need any a priori model information at the beginning of the algorithm nor on.
In this paper, a novel iterative qlearning algorithm, called policy iteration based deterministic qlearning algorithm, is developed to solve the optimal control problems for discretetime deterministic nonlinear systems. In this brief, we propose a model free dhp mfdhp design based on finitedifference technique. As an imitation of the biological nervous systems, neural networks nns, which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. Two dash lines separate the diagram into three parts. The presented incremental model based dual heuristic programming method can adaptively generate a nearoptimal controller online without a priori information of the system dynamics or an offline. Brief paper modelfree qlearningdesignsforlineardiscrete. However, the advantage of using generic nn architectures is that no manual or. The battle between stochastic dynamic programming and reinforcement learning pascal cot e 1, richard arsenault 2, quentin desreumaux 3 1power operation, rio tinto aluminum saguenay, qu ebec, canada.
Action dependent heuristic dynamic programming for home. In this paper, we analyze an internal goal structure based on heuristic dynamic programming, named grhdp, to tackle the 2d maze navigation problem. Robust adaptive dynamic programming hao yu, zhongping jiang download bok. Optimalcontrol of photovoltaic solar energy system critics.
Reinforcement learning and approximate dynamic programming. The interrelationships between members of the acd family have been generalized and explained in 6. Mshdp speeds up value iteration and avoids the requirement of initial admissible control policy in policy iteration at the same time. The foundation of adp can be traced back to the classic bellmans principle of optimality 24.
Pdf comparison of heuristic dynamic programming and dual. Request pdf modelfree dual heuristic dynamic programming modelbased dual heuristic dynamic programming mbdhp is a popular approach in approximating optimal solutions in control problems. The idea is to use an iterative adaptive dynamic programming adp technique to construct the iterative control law which optimizes the iterative q function. Pdf incremental model based heuristic dynamic programming for. In section 2, we describe the setting we work with and formulate the problem we propose to address. The approach does not depend on the dynamical models of the considered systems. Both traditional heuristic dynamic programming algorithm and incremen tal. Some modelfree or partially modelfree rl methods 21. In this paper, we integrated one additional network. Programming controller using incremental models, which is named incremental model based heuristic dynamic programming ihdp, is developed as a modelfree adaptive control approach for nonlinear unknown systems. Classical reinforcement learning approaches have been introduced to solve this problem in literature, yet no intermediate reward has been assigned before reaching the final goal. These have been categorized as heuristic dynamic programming hdp, actiondependent heuristic dynamic programming adhdp, dual heuristic programming dhp, and actiondependent dual heuristic programming addhp in the adaptive critic literature 1112.
Model based dual heuristic programmingidhp, and incremental model based action. The neural network controller such as neural network predictive controller and dual heuristic programming are recently used in controlling gridconnected inverters 10, 11. In this paper, we present a new model free globalized dual heuristic dynamic programming gdhp approach for the discretetime nonlinear zerosum game problems. Here decisions are the result of an interplay between a fast, automatic, heuristicbased system 1 and a slower, deliberate, calculating system 2. Vertical takeoff and landing vtol aircraft system is a nonlinear complex system with multivariable largedisturbances. Approximate dynamic programming and reinforcement learning, honolulu, hi, apr.
Incremental model based online dual heuristic programming for nonlinear adaptive control. Automotive engine torque and airfuel ratio control using dual heuristic dynamic programming. Datadriven heuristic dynamic programming with virtual. Online discretetime lqr controller design with integral action for bulk bucket wheel reclaimer operational processes via actiondependent heuristic dynamic programming isa transactions, vol. First, the online learning algorithm is proposed based on the gdhp method to solve the hamiltonjacobiisaacs equation associated with h. A general utility function representation for dual heuristic. Incremental model based actor critic designs for optimal adaptive. Multistep heuristic dynamic programming for optimal. The recurrent connections or context units in neural. Dynamic programming hdp, dual heuristic dynamic programming dhp. Autonomous control of a line follower robot using a qlearning controller, sepehr saadatmand, sima azizi, mohammadamir kavousi, and donald c.
Pdf this paper presents a new and effective approach, incremental model based. Model neuralnetwork structure with 12 inputs, 14 sigmoidal hidden layer neurons, and. In this brief, we propose a modelfree dhp mfdhp design based on finitedifference technique. Finitehorizon optimal tracking guidance for aircraft. The adhdp uses two neural networks, an action network which provides the control signals and a critic network which criticizes the action network performance. Herein, a novel online adaptive learning framework is introduced to solve actiondependent dual heuristic dynamic programming problems. Modelbased dual heuristic dynamic programming mbdhp is a popular approach in approximating optimal solutions in control. It is known that the nonlinear optimal control problem relies on the solution of the hamiltonjacobibellman hjb equation, which is a nonlinear partial di. Section6demonstrates the adaptive critics implementations for the proposed modelfree gradientbased solution. This method is based on a class of adaptive critic designs acds called action dependent heuristic dynamic programming adhdp and it has the capability to learn from the environment. Besides, three actiondependent forms were presented.
For solving a sequential decisionmaking problem in a nonmarkovian domain, standard dynamic programming dp requires a complete mathematical model. Incremental model based heuristic dynamic programming for. The chapter also looks at the main features of the aforementioned family of algorithms and provides a descripion of selected actorcritic learning methods such as heuristic dynamic programming, dualheuristic dynamic programming and global dualheuristic dynamic programming which assume availability of a mathematical model, as well as model. The foundation of adp can be traced back to the classic bellman. A novel policy iteration based deterministic q learning. The neural network controller is trained algebraically, offline, by the observation that its gradients must equal corresponding linear gain matrices at chosen operating points. The adp method can be categorized as heuristic dynamic programming hdp, dual heuristic dynamic programming dhp, and globalized dual heuristic dynamic programming gdhp. By contrast, this paper describes a totally modelfree approach by actorcritic reinforcement learning with recurrent neural networks.
A nonlinear control system comprising a network of networks is taught by the use of a twophase learning procedure realized through novel training techniques and an adaptive critic design. The purpose is to estimate the system cost function. It is related to the reinforcement learning rl while using the adaptivecritic ac design framework. Dual heuristic programming is a method for estimating the gradient of the. Reinforcement learning rl and adaptive dynamic programming adp has been one of the most critical research fields in science and engineering for modern complex systems. Wen, multimachine power system control based on dual heuristic dynamic programming, in proc. Modelfree dual heuristic dynamic programming ieee journals. Modelfree adaptive control for unknown nonlinear zerosum. Robust adaptive dynamic programming hao yu, zhongping. Approximate dynamic programming in tracking control of a.
Datadriven modelfree tracking reinforcement learning. Modelfree dual heuristic dynamic programming z ni, h he, x zhong, dv prokhorov ieee transactions on neural networks and learning systems 26 8, 18341839, 2015. In order to avoid the safety accidents caused by earth pressure imbalance during shield machine tunneling process, the earth pressure between excavation. This serves as a modelfree solution framework for the classical action dependent dual heuristic dynamic programming problems. Globalized dual heuristic programming algorithms 23, 26 were developed. Modelfree gradientbased adaptive learning controller for. Reinforcement learning overview of recent progress and. Earth pressure balance control for shield tunneling. Request pdf model free dual heuristic dynamic programming modelbased dual heuristic dynamic programming mbdhp is a popular approach in approximating optimal solutions in control problems. We generalize the dual system framework to the case of. This book describes the latest rl and adp techniques for decision and control in human engineered systems, covering both single player decision and control and multiplayer. Adaptivecriticbased neural networks for aircraft optimal. Modelbased dual heuristic dynamic programming mbdhp is a popular approach in approximating optimal solutions in control problems.
For the first time, this study applies the most advanced kernelbased dual heuristic programming dhp algorithm into solving the optimal control problems of vtol aircraft systems successfully. Ieee symposium on computational intelligence applications in smart grid ciasg14, ieee symposium series on computational intelligence ssci, orlando, fl, 2014. Yet, it usually requires offline training for the model network, and thus resulting in extra computational cost. Paper entitled modelfree dual heuristic dynamic programming has been accepted by ieee trans. Qlearning for optimal control of continuoustime systems. Pdf dual heuristic dynamic programing control of grid. Section5introduces the modelfree gradientbased solution and the underlying riccati development. Reinforcement learning and inverse reinforcement learning. Totally modelfree actorcritic recurrent neuralnetwork. Our paper a parametric classification rule based on the exponentially embedded family, tnnls, 262, pp.