In this paper, a novel adaptive dynamic programming (ADP) algorithm is developed for the infinite-horizon (H _∞ ) optimal control problems with unknown continuous-time (CT) nonlinear systems subject to external disturbances. To facilitate the implementation of the algorithm, generalized fuzzy hyperbolic models (GFHMs) are utilized to establish an identifier-critic architecture, where the identifier is designed to reconstruct the unknown system dynamics, and the GFHM-based critic network is employed to approximate the value functions. The CT H _∞ optimal control issue is converted into a two-player zero-sum game and the corresponding Hamilton-Jacobi-Isaacs equation is derived. The learning procedure of the critic design is adaptively implemented with the help of the reconstructed model, thus the requirement of the complete system dynamics is relaxed. Furthermore, by the means of Lyapunov direct method, the uniform ultimate boundedness stability analysis of the closed-loop control system is explicitly provided. Finally, to compare the control performances and disturbance attenuation properties of the proposed method and the existing ADP algorithms, two numerical examples are given.

In order to suppress the swing of slung load during the flight of the quadrotor unmanned aerial vehicle (UAV), a new double closed-loop nonlinear control method Newton–Euler is proposed based on an energy function and the LaSalle’s invariance theorem. First, the forces of each part in the quadrotor-slung load system (QSLS) is analyzed separately, and the dynamics model of the QSLS is established by the Newton–Euler method. Then, an energy function is constructed based on the relative motion relationship between the UAV and the slung load, and a nonlinear position controller is designed based on the energy function to enable the UAV to suppress the swing angle of the slung load while moving to the desired position in three-dimensional space. The equilibrium point of the whole system is proved to be asymptotically stabilized by the Lyapunov technique and LaSalle’s invariance theorem. Finally, some comparisons are made with a nonlinear adaptive controller and the linear quadratic regulator (LQR) to verify the effectiveness and superiority of the controller proposed in this article. It is shown that for the QSLS, the proposed controller has faster convergence speed and better transient performance during the anti-swing control.

With the continuous improvement of micro-energy grid, in order to reduce carbon emissions and environmental pollution, a multi-objective optimization operation strategy considering the distributed energy generation uncertainty, environmental factors, and self-supply rate is proposed in this paper. First, to simulate the uncertainty of distributed energy, we used the three-parameter Weibull distribution to simulate the wind power. Then, for the load side, an innovative electric and thermal comprehensive demand response model is proposed. Different from the traditional micro energy network that only considers the heat energy balance, this paper also considers the thermal inertia of buildings to more accurately describe the characteristic of the heat load. Meanwhile, on the basis of economic factors, the abandon rate of distributed energy, environmental coefficient and self-sufficiency rate are also regarded as three objective functions. Finally, in order to obtaining a set of solutions at the Pareto front, the entropy-weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is introduced to obtain the global optimal solution. Simulation study of multiple scenarios shows the effectiveness of the proposed method. Compared with the method that considers only economic costs, the pollutant emissions are reduced by 50kg and self-power supply rate is increased by 20%.

With the development of energy Internet and renewable energy, the combined heat and power micro-energy grid (CHPMEG) has become an important carrier for the use of clean energy. In order to improve the living comfort of households and the utilization efficiency of wind power in winter, this paper proposes a coordinated optimal operation strategy of CHPMEG considering the load demand side response and the thermal inertia of the user side. First, the load demand side response is introduced into the optimal operation of CHPMEG, and a load demand response model is proposed based on reward mechanism. Then, the household thermal inertia is taken into account in the system operation, and a heat load model considering thermal inertia is established to describe the corresponding characteristics more exactly. On this basis, an optimization model is further developed and solved to minimize the operation cost of the system. Finally, the case simulations demonstrate the effectiveness of the optimal operation strategy of CHPMEG proposed in this paper.