In this paper, a novel robust adaptive dynamic programming (RADP)-based control strategy is presented for the optimal control of a class of output-constrained continuous-time unknown nonlinear systems. Our contribution includes a step forward beyond the usual optimal control result to show that the output of the plant is always within user-defined bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system, whose asymptotic stability guarantees both the asymptotic stability and the satisfaction of the output restriction of the original system. Furthermore, RADP algorithms are developed to solve the transformed nonlinear optimal control problem with completely unknown dynamics as well as a robust design to guarantee the stability of the closed-loop systems in the presence of unavailable internal dynamic state. Via small-gain theorem, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Finally, comparison results demonstrate the merits of the proposed control policy.

As wind energy is increasingly exploited worldwide, optimisation of wind farm layout becomes more crucial. To guarantee the economic efficiency and profit of a wind farm, the deployment of wind turbines has to be optimised before operation. Traditional methods usually assume that identical type of wind turbines are utilised in a layout design. In this study, multiple types of turbines are considered in wind farm layout optimisation in complex terrain, namely mixed‐installation. By utilising different power generation characteristics, hub heights and rotor diameters, and cost models of different types of turbines, the efficiency of a wind farm can be further improved. A single‐objective optimisation problem is firstly established by modelling all aforementioned factors, and the objective is to achieve a minimum cost per unit of energy. Subsequently, after using computational fluid dynamics to simulate the wind flow over complex terrain, a genetic algorithm‐particle swarm optimisation optimisation algorithm is then proposed to determine the position and type of every individual turbine simultaneously. Eventually, extensive simulation studies are present to verify the feasibility of this scheme.

Transit-time multipath ultrasonic flow meters (TM-UFMs) have been widely employed for measuring flowrate of gas and liquid. However, its applicability is still dragged by the difficulties to describe the physical model by precise mathematical expressions and to reduce the deviations led by the simplified principle models. Besides, fault-tolerance, including fault diagnosis and measure maintaining method, namely, rectification, is of great value in practical engineering, but there are few effective methods provided ever since to provide a solution for faulty UFMs. Therefore, data integration for TM-UFMs is one of the complications to obtain the accurate measurements, except for precise transit-time detection and improved transducers and circuits. This paper proposes a novel data integration method for TM-UFM calibration, as well as fault diagnosis and rectification based on particle swarm optimized support vector machines (PSO-SVM). Besides, extensive experiments have been conducted on a platform of TM-UFMs, and the results have illustrated the effectiveness of this method. The PSO-SVM-based models trained by the proposed method lead to a decrease of deviations to ±1% (full scale) in normal state for data integration, compared with ±2% deviations when a traditional method is adopted. When there exist one or two dysfunctional acoustic paths, the method diagnoses the faulty paths and maintains the measurement with deviations below ±2%.