Search

Summary: In this article, a novel event‐triggered zero‐sum differential game through value iteration (VI)‐based adaptive fuzzy control is proposed for modular robot manipulators (MRMs) with uncertain disturbance. The subsystem dynamic of MRMs is established by joint torque feedback (JTF) technology. An identifier based on fuzzy logic model (FLM) is established to identify unknown system dynamics. Then, the trajectory tracking control issue of MRMs with uncertain disturbance is transformed into a two‐player zero‐sum differential game. On the basis of event‐triggered mechanism and VI algorithm, the event‐triggered Hamilton–Jacobi–Issacs (HJI) equation is approximately solved through establishment of actor‐critic structure. Different from the traditional adaptive dynamic programming (ADP) algorithm, this scheme is based on adaptive fuzzy optimal control through an actor‐critic structure, which refers to adaptation of both optimal cost function and optimal control policy. Moreover, tracking errors of closed‐loop MRM system are demonstrated to be uniformly ultimately bounded (UUB) under Lyapunov stability theorem. At last, experimental examples and comparisons show the reliability and effectiveness of this method. [ABSTRACT FROM AUTHOR]

A novel policy gradient (PG) adaptive dynamic programming method is developed to deal with nonlinear discrete-time zero-sum games with unknown dynamics. To facilitate the implementation, a policy iteration algorithm is established to approximate the iterative Q-function, as well as the control and disturbance policies via three neural network (NN) approximators, respectively. Then, the iterative Q-function is exploited to update the control and disturbance policies via PG method. To stabilize the training process and improve the data usage efficiency, the experience replay technique is applied to train the weight vectors of the three NNs by using mini-batch empirical data from replay memory. Furthermore, the convergence in terms of the iterative Q-function is proved. Simulation results of two numerical examples are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the guidance system with unknown dynamics is modeled as a partially unknown zero-sum differential game system. Then, a periodic event-triggered optimal control algorithm is designed to interception target under a plug-n-play framework. To realize this algorithm, generalized fuzzy hyperbolic models are employed to construct the identifier-critic structure, where the online identifier is used to estimate unknown dynamics, meanwhile, the generalized fuzzy hyperbolic models-based critic network is utilized to approximate the cost function. Note that plug-n-play framework lets both the designed identifier and critic network work simultaneously, in other words, the prior system information is no longer required, which simplifies the network structure. Using the Lyapunov function method, the approximate optimal control strategy and corresponding weight updating laws are derived to guarantee that the closed-loop system and weight approximation errors are uniformly ultimately bounded, where an additional function is added to weight updating laws to release the requirement for admissible initial control. Finally, to compare the intercept effects and the utilization ratio of communication resources of the periodic event-triggered control algorithm and the common adaptive dynamic programming algorithm, the missile interception system is introduced as an example. • The guidance system with unknown dynamics is modeled as a partially unknown zero-sum differential game system. Then, the event-based optimal control strategy is designed to improve the utilization of data transmissions. • A GFHM-based identifier is proposed under a plug-n-play framework to approximate unknown nonlinear functions and eliminate the priori identification process. • This paper proposes the fuzzy ADP approach to design the optimal controller, where the single critic network is adopted to approximate the optimal performance index by using GFHMs. [ABSTRACT FROM AUTHOR]

This paper investigates the fuzzy approximation-based optimal consensus control problem for nonlinear multiagent systems with unknown perturbations. By constructing local error dynamics, the considered optimal consensus problem is reformulated as finding Nash-equilibrium solutions to zero-sum games. Then, by using sliding mode control technology and the concept of hierarchical design, a series of control signals are sequentially designed to regulate the consensus error and minimize the local value function. In addition, an identifier-critic architecture is developed by using generalized fuzzy hyperbolic models, where the identifier is employed to relax the requirement for complete system dynamics information, and the hierarchical sliding mode surface-based critic network is applied to approximate optimal control inputs. Finally, A simulation example is presented to illustrate the validity of the proposed approach. [ABSTRACT FROM AUTHOR]