Neuro-adaptive tracking control of a hypersonic flight vehicle with uncertainties using reinforcement synthesis

A reinforcement learning-based near-optimal hierarchical approach for motion control: Design and experiment

As a data-driven design method, model-free optimal control based on reinforcement learning provides an effective way to find optimal control strategies. The design of model-free optimal control is sensitive to system data because it relies on data rather than detailed dynamic models. A prerequisite for generating applicable data is that the system must be open-loop stable (with a stable equilibrium point), which restricts the data-based control design methods in actual control problems and leads to rare experimental studies or verification in the literature. To improve this situation and enrich its applications, we propose a pre-stabilized mechanism and apply it to the motion control of a mechanical system together with a reinforcement learning-based model-free optimal control method, which constitutes a so-called hierarchical control structure. We design two real-time control experiments on an underactuated system to verify its effectiveness. The control results show that the proposed hierarchical control is quite promising in controlling this mechanical system, even though it is open-loop unstable with unknown dynamics.

Particle swarm optimized neural networks based local tracking control scheme of unknown nonlinear interconnected systems

In this paper, a local tracking control (LTC) scheme is developed via particle swarm optimized neural networks (PSONN) for unknown nonlinear interconnected systems. With the local input-output data, a local neural network identifier is constructed to approximate the local input gain matrix and the mismatched interconnection, which are utilized to derive the LTC. To solve the local Hamilton-Jacobi-Bellman equation, a local critic NN is established to estimate the proper local value function, which reflects the mismatched interconnection. The weight vector of the local critic NN is trained online by particle swarm optimization, thus the success rate of system execution is increased. The stability of the closed-loop unknown nonlinear interconnected system is guaranteed to be uniformly ultimately bounded through Lyapunov's direct method. Simulation results of two examples demonstrate the effectiveness of the developed PSONN-based LTC scheme.

Fault estimation and compensation for hypersonic flight vehicle via type-2 fuzzy technique and cuckoo search algorithm

This study proposes a fault estimation and compensation scheme for hypersonic flight vehicle (HFV) attitude system with body flap fault. To achieve the fault compensation capability, an interval type-2 fuzzy estimator is designed to approximate the unknown nonlinear function caused by modeling uncertainties and the fault. The fault-tolerant controller via the terminal sliding mode and dynamic surface techniques is developed to ensure the tracking accuracy of attitude angles for HFV and the desired control torque can be achieved. Then, a novel control allocation scheme with a cuckoo search algorithm and linear programming method is proposed to distribute the desired torque to aerodynamic surfaces and reaction control system jets. The stability of the proposed scheme is analyzed using the Lyapunov stability theory. The validity of the method is verified by a series of comparisons on numerical simulation results.

Anti-disturbance backstepping control for air-breathing hypersonic vehicles based on extended state observer

In this paper, we propose an anti-disturbance backstepping control approach with extended state observer (ESO) for tracking control of air-breathing hypersonic vehicles. Considering the large uncertainties, the external disturbances, and especially the lack of aerodynamic knowledge, several ESOs are introduced in the backstepping controller. With the total disturbance estimation ability of ESOs, almost no aerodynamic knowledge is needed for the controller design. Meanwhile, ESOs are also used to estimate the derivatives of the virtual control signals. The problem of "explosion of terms" is avoided. A key strategy of the controller is that each step of backstepping is activated successively. Consequently, the closed-loop system has time-scale structure. Rigorous stability proof can be obtained. At last, compared simulation results verify the superior tracking performance of the proposed controller.