Backstepping‐based zero‐sum differential games for missile‐target interception systems with input and output constraints

This paper presents a decentralized zero-sum optimal control method for MRMs with environmental collisions via an actor-critic-identifier (ACI) structure-based adaptive dynamic programming (ADP) algorithm. The dynamic model of the MRMs is formulated via a novel collision identification method that is deployed for each joint module, in which the local position and torque information are used to design the model compensation controller. A neural network (NN) identifier is developed to compensate the model uncertainties and then, the optimal control problem of the MRMs with environmental collisions can be transformed into a two-player zero-sum optimal control one. Based on the ADP algorithm, the Hamilton-Jacobi-Isaacs (HJI) equation is solved by constructing the actor-critic NNs, thus making the derivation of the approximate optimal control policy feasible. Based on the Lyapunov theory, the closed-loop robotic system is proved to be asymptotically stable. Finally, the experiments are conducted to verify the effectiveness and advantages of the proposed method.INDEX TERMS Adaptive dynamic programming, collision identification, decentralized optimal control, modular robot manipulators, zero-sum game.

Section: Aci Structure-based Decentralized Zero-sum Optimal Control V...mentioning

confidence: 99%

Section: Aci Structure-based Decentralized Zero-sum Optimal Control V...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions

Dong

Zhou

et al. 2019

“…3) In this paper, the strict-feedback system is transformed into an output feedback system firstly, then an adaptive observer is constructed to estimate the states. Compared with [9,20,34,35], this paper does not require the whole states of the system are measurable. For missile guidance systems, the benefit is that it can save space inside the warhead and be equipped with other advanced equipment to enhance the missile's power.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Observer-Based Dynamic Surface Control for Input-Saturated Nonlinear Systems and its Application to Missile Guidance

Zhang

2020

In this paper, a fuzzy observer-based tracking control scheme is proposed for a class of strict-feedback uncertain nonlinear system with input saturation. To facilitate the fuzzy logic system (FLS) design, the original uncertain nonlinear system is transformed into an output feedback system firstly. Then, an adaptive state observer is constructed. In order to efficiently tackle the input saturation, an auxiliary system (AS) is introduced. Using the estimated states and AS, the dynamic surface control (DSC) strategy is developed based on backstepping method. Under the DSC scheme, the tracking control problem is reduced to optimal regulation problem of an affine system. Subsequently, an adaptive dynamic programming (ADP) based optimal control scheme is proposed. The stability of the overall system is guaranteed by Lyapunov theory. Finally, guidance simulation is made to confirm the effectiveness of the developed control method.

“…The thought is succeeded for intercepting high-speed manoeuvring targets of interceptor missiles with output and input constraints [24] , and stimulate the choice of optimal ADP zero-sum control strategy for the tracking error dynamics in nonlinear strict-feedback systems.…”

Section: Introductionmentioning

confidence: 99%

Event-Driven-Modular Adaptive Backstepping Optimal Control for Strict-Feedback Systems Through Zero-Sum Differential Games

Zhou

Bai

2020

This paper addresses the event-driven-modular optimal tracking control problem for nonlinear strict-feedback systems with external disturbances. Through the backstepping feedforward control, the optimal tracking problem is transformed into an equivalent optimal regulation problem of affine tracking error system. Subsequently, adaptive dynamic programming technique is introduced to generate the optimal feedback controller, and solve the optimization problem of two-player zero-sum differential game. A single critic neural network is constructed to evaluate the associated cost function online, where the novel weight updating law is derived based on the gradient-descent technique. The resulting event-triggered closed-loop system, modeled as an impulsive system, is proved to be asymptotically stable by Lyapunov theory. Finally, the reliability and effectiveness of the theoretical results is validated by numerical simulation examples.