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In an electric power grid, Load-Frequency Control (LFC) plays a crucial role as it aims to maintain the system frequency at a nominal value, 50 or 60 Hz, by minimizing the effects of load changes. However, a modern power system is currently characterized by a huge number of nonlinearities and uncertainties, making control methodologies much more challenging. Among them, the nonlinear features of Governor Dead-Band (GDB) and Generation Rate Constraint (GRC) strongly affect the accuracy and performance of LFC applied to a power network. This study focused on designing an applicable and efficient LFC by proposing a novel Sliding-Mode Control (SMC) scheme. The traditional SMC can successfully solve several nonlinear control problems, and in case of having a reasonable adjustment, it is completely suitable to design the LFC strategy. The modified SMC, integrated with an effective optimization technique, i.e., Particle Swarm Optimization (PSO), can dramatically improve the performance of LFC. This paper presents numerical simulation results implemented in MATLAB/Simulink to demonstrate the feasibility and effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

• A novel nonlinear impulsive time-delay system in fed-batch process is proposed. • The robust parameter identification problem is presented to determine the optimal parameters. • A parallel particle swarm optimization algorithm is developed to solve the identification problem. In this paper, taking the feeding process as an impulsive form and considering the time-delay in fermentation process, we propose a nonlinear impulsive time-delay system to formulate the fed-batch culture in microbial 1,3-propanediol production. For this system, we discuss some important properties of the solution. To determine the optimal time-delay and kinetic parameters in the system, we then present a robust parameter identification model subject to continuous state inequality constraints. By the constraint transcription technique and a penalty method, we transform the robust parameter identification problem to a sequence of optimization problems with only box constraints. On this basis, we propose a parallel Particle Swarm Optimization algorithm to solve the resulting problem. Finally, the numerical results show that the impulsive time-delay system can well describe the fed-batch process and the developed algorithm is highly effective. [ABSTRACT FROM AUTHOR]

The hydraulic turbine governing system (HTGS) is a complex nonlinear system that regulates the rotational speed and power of a hydro-generator set. In this work, an incremental form of an HTGS nonlinear model was established and the Takagi–Sugeno (T-S) fuzzy linearization and mixed H2/H∞ robust control theory was applied to the design of an HTGS controller. A T-S fuzzy H2/H∞ controller for an HTGS based on modified hybrid particle swarm optimization and gravitational search algorithm integrated with chaotic maps (CPSOGSA) is proposed in this paper. The T-S fuzzy model of an HTGS that integrates multiple-state space equations was established by linearizing numerous equilibrium points. The linear matrix inequality (LMI) toolbox in MATLAB was used to solve the mixed H2/H∞ feedback coefficients using the CPSOGSA intelligent algorithm to optimize the weighting matrix in the process so that each mixed H2/H∞ feedback coefficients in the fuzzy control were optimized under the constraints to improve the performance of the controller. The simulation results show that this method allows the HTGS to perform well in suppressing system frequency deviations. In addition, the robustness of the method to system parameter variations is also verified. [ABSTRACT FROM AUTHOR]