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The formation control problem for a group of first-order agents with model uncertainty and actuator saturation is investigated in this manuscript. An algorithm-and-observer-based formation controller is developed to ensure the semi-global boundedness of the formation tracking error with actuator saturation. First, a fully local-error-related cooperative weight tuning procedure is proposed for the adaptive uncertainty estimation of each agent. The effect of actuator saturation on both the cooperative adaptive estimation and the controller design part is then analysed and discussed. A three-layer neural-based observer is further constructed to achieve finite-time uncertainty approximation with actuator saturation. Besides, the reverse effect led by coupled and saturated control inputs is defined and a new control input distribution algorithm is presented to attenuate the potential oscillation in system states. Finally, comparative simulations based on a multiple omnidirectional robot system are conducted to illustrate the performance of the proposed formation controllers and the new algorithm.

This paper presents a new technique for stabilising a Takagi–Sugeno (T-S) fuzzy system with time-delay and uncertainty. A robust fuzzy functional observer is employed to design a controller when the system states are not measurable. The model uncertainty is norm bounded, and the time-delay is time-varying but bounded. The parallel distributed compensation method is applied for defining the fuzzy functional observer to design this controller. The proposed procedure reduces the observer order to the dimension of the control input. Improved stability conditions are provided for the observer compared with the existing results of functional observer-based stabilisation of T-S fuzzy models. Lyapunov–Krasovskii functionals are used to construct delay-dependent stability conditions as linear matrix inequalities. The solution of these inequalities is used for calculating the observer parameters. The sensitivity of the estimation error to the model uncertainty is reduced by minimising the $$L_2$$ gain. The new design method developed is illustrated and verified using two examples.

This paper studies the problem of mean square asymptotical synchronization and $$H_\infty $$ synchronization for coupled stochastic reaction–diffusion systems (SRDSs) via boundary control. Based on the deduced synchronization error dynamic, we design boundary controllers to achieve mean square asymptotical synchronization. By virtue of Lyapunov functional method and Wirtinger’s inequality, sufficient conditions are obtained for ensuring mean square asymptotical synchronization. When coupled SRDSs are subject to external disturbance, mean square $$H_\infty $$ synchronization is investigated and corresponding criterion is presented under a designed boundary controller. In addition to focusing on systems with Neumann boundary conditions, we also briefly study coupled SRDSs with mixed boundary conditions and sufficient conditions are provided to achieve the desired performance. Numerical examples are used to verify the effectiveness of our theoretical results.

A signal processing method of coherent Doppler lidar (CDL) for wind measurement is proposed in this paper, which combines the H-infinity control and coherent integration. This method can improve the signal-to-noise ratio and reduce the interference; the measured wind speed will be more exact. In this paper, the basic theory of signal processing method is proposed. That includes the main idea of how to combine the two different methods and the detailed information of this two methods. Then, the simulation by MATLAB is done, and the results are given to confirm the practicality of the new idea. The wind speed deviation before and after doing the signal processing is simulated and compared. It confirms that the accuracy has improvement by using this method mentioned in this paper.

In this paper, we propose an inventory model under two-level trade credit policy, where the supplier offers quantity discounts and allows the retailer to choose the period of delay in payments that determines the unit purchasing cost, and so retailer's unit purchasing cost is a function of both ordering quantity and his/her allowed credit period. Furthermore, the demand function is relevant to the credit period offered by the retailer to his/her customers. Our objective here is to maximize the retailer's total profit per unit time by solving the optimal decision on ordering quantity and two credit periods. An effective algorithm is developed to determine the optimal solution to the problem. Numerical examples are provided to illustrate the impact of the model parameters on the optimal solutions.

Genetic evolutionary algorithms are effective and optimal test generation methods. However, the methods to select the algorithm parameters are often ad hoc, relying on empirical data. We used a Markov-based method to model the genetic evolutionary test generation process, parameterise the process characteristics, and derive analytical solutions for selecting the optimisation parameters. The method eliminates preliminary test generation calibration and experimentation effort needed to select these parameters, which are used in current practice.

An efficient computational scheme for solving a general class of linear time optimal control problems, where the target set is a compact and convex set with nonempty interior in the state space, is presented. The scheme is applied to solve the ship steering control problem, and excellent results are obtained.