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This study aims to develop a robust control for the quadrotor slung-load system that efficiently follows a reference trajectory. A fractional-order robust sliding mode control has been chosen to control the quadrotor's altitude, position, and attitude. An anti-swing controller was also installed to limit the swing angle of the suspended load. It was created via delayed feedback, in which the quadrotor's position reference trajectory is changed by the difference of the load angles within a specific delayed value. Designing an adaptive FOSMC would control the system when the system uncertainties do not know the bounds. Moreover, the control parameters and the anti-swing controller for the FOSMC can be obtained using some optimization techniques to increase the controllers' accuracy. [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]

This paper aims to determine a time-varying dilution rate of the feed medium which maximizes mean productivity while minimizing both system sensitivity and control cost in glycerol continuous culture. A multi-objective optimal control problem subjected to a nonlinear control system and its auxiliary system is proposed and transformed into a finite-dimensional large-scale multi-objective optimization problem via discretization and time-scaling transformation. A multi-objective competitive swarm optimization algorithm is constructed to solve this transformed problem based on pairwise competition, mutation operation and environment selection. The Pareto front obtained shows the distribution of the optimal target value which indicates the rationality and feasibility of the control system under the approximate optimal strategy. Numerical simulations verify the robustness of the optimal control system and the effectiveness of our numerical solution method. The value of IGD shows the good accuracy and distribution of the proposed multi-objective optimization algorithm. • We propose a multi-objective optimal control problem of glycerol continuous culture. • The multi-objective optimal control problem is approximated by a parameter optimization problem. • A multi-objective competitive swarm optimization algorithm is developed. • Numerical experiments show the effectiveness and applicability of the approach proposed. [ABSTRACT FROM AUTHOR]

Advanced control systems are tuned using dynamic models and optimization techniques. This approach frequently involves satisfying multiple conflicting objectives. Tuning robust controllers requires considering a framework that represents the system uncertainties, and its definition is not a trivial task. When dealing with a nonlinear model with many parameters, a high-quality representation requires a massive sampling of variations. In many cases, this represents an inaccessible computational cost for the optimization process. This work presents a new methodology for parameter uncertainty modeling that is oriented to tuning robust controllers based on multiobjective optimization techniques. The uncertainty modeling formulated represents a feasible computational cost and leads to robust solutions without attributing excessive conservatism. The novelty of this process consists in using the multiobjective space to define a set of scenarios with highly representative properties of the global uncertainty framework that formulate the control problem under a predefined minimization strategy. To demonstrate the effectiveness of this methodology, we present a temperature control design in a micro-CHP system under worst-case minimization. Based on the results, particular interest is given to verifying the appropriate formulation of the uncertainty modeling, which represents a 92.8% reduction of the computational cost involved in solving the robust optimization problem under a global uncertainty framework. • Tuning robust controllers requires considering system uncertainty. • Consider feasible computational cost and conservatism. • A new methodology is proposed to define highly relevant models. • The novelty consists of using the multi-objective space to set a characterization. • This process formulates the robust control under a realistic worst case. [ABSTRACT FROM AUTHOR]

The need for renewable energy sources is increasing while the use of non-environmentally energy sources is decreasing day by day. At this point, solar energy which is a great natural energy source comes to the fore. In this study, MPPT control is performed based on sliding mode control as a robust control method. A super-twisting sliding mode controller has been developed and type 2 fuzzy set has been adapted to the system to reduce the chattering problem. The developed MPPT control algorithm is applied to a solar PV system and tested under variable irradiance conditions. In addition, the parameters of super twisting sliding mode and type 2 fuzzy set are optimized. The efficiency of the proposed MPPT algorithm is presented with tables and graphics that contains sliding mode MPPT, super twisting sliding mode MPPT and super twisting sliding mode-type 2 fuzzy MPPT methods. The results show the MPPT efficiency of proposed system with a robust structure. [ABSTRACT FROM AUTHOR]

In this work, an efficient robust control scheme is proposed for position control of induction motors based on field-oriented control (FOC). The proposed control scheme consists of three interior loops. In the outer loop, the central controller based on the nonsingular fast terminal sliding mode control (NFTSM) is designed. The objective of this loop is to provide the required stator current in the q-axis for regulating the electromagnetic torque. In the inner loops, two fractional-order PI (FOPI) controllers are designed to provide the stator voltages. Besides, since induction motors have a complex mathematical model, time-varying dynamics, and non-linear structure, to deal with these challenges in the controller design, the quantum neural network (QNN) is presented with a novel framework and employed to estimate the lumped uncertainties. This makes the main contribution of the paper. In addition, to further improve system performance, the parameters of the FOPI controllers are tuned by employing the artificial bee colony (ABC) optimization algorithm. The stability of the proposed control approach is proved by Lyapunov's stability theory. According to the results of the comparative simulation, the superiority of this approach is confirmed. • Addressing the position control of the IM using an effective control approach. • Presenting the QNN with a novel framework. • Estimating the lumped uncertainties using the adaptive QNN. • Combining the merits of the FOPI controllers, and NFTSMC. • Tuning the FOPI controllers using the ABC optimization algorithm. [ABSTRACT FROM AUTHOR]

The task of designing a robust control system using structured H ∞ algorithm involves several issues: choosing the system topology, the controller structure and tuning parameters to achieve robust stability and performance. Its application can lead to a trial-and-error approach at some stage. In order to minimize that effort, a methodology is proposed. A step-by-step guide is provided and includes modelling an uncertain system and designing roll-off, notch and setpoint filters aided by singular value analysis. The step of choosing a suitable topology for the control system and the controller structure is accomplished by RGA matrix analysis. Using a mixed-sensitivity approach, the design of weighting functions to achieve desired frequency-based performance is carried out considering plant restrictions. Application of the structured H ∞ optimization algorithm is commented and results are validated by means of sensitivity function analysis and nonlinear simulation. A successful study case of control system for tight formation flight is presented which includes nonlinear disturbances, uncertainties, measurement noise, input saturation and restrictions due to RHP poles. Results are compared with those obtained with other robust control techniques. [ABSTRACT FROM AUTHOR]

This paper presents novel approach for utilizing deployable fin as a static control surface along with aircraft primary controls for flat-spin recovery. Comparative analysis of with and without deployment of the fin has been thoroughly examined. To demonstrate the present approach, a nonlinear constrained optimization technique based optimal fin deflection is determined, followed by aircraft response in a flat-oscillatory-stable-left-spin flight at optimal fin setting angle, which provides spin attribute compensation. Thereafter, sliding-mode based robust control is designed for commanded departure of the aircraft from flat-spin to steady-straight-level flight. It is shown that finite-time direct spin recovery is achieved without any timescales based separation in dynamics. The present work novelty lies in the proposed strategy of using deployable fin, wherein dynamics of the fin is modeled and incorporated in the standard equations of motion of aircraft, subsequently, robust control law is developed. It is found from closed-loop simulation results that the proposed controller not only reduces the number of turns about spin axis and altitude loss but also minimizes spin recovery time when the fin is deployed. Additionally, rudder control power requirement is reduced, and effectiveness of other aerodynamic control surfaces are regained earlier when the fin is used for spin recovery. The outcome of the present study promises its practical implementation since the proposed solution is simple and compatible to integrate with the existing aircraft. [ABSTRACT FROM AUTHOR]