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This article aims at designing a robust wide-area damping controller (WADC) to address the problems associated with communication failures. The proposed controller is a state feedback-based controller, which uses generators' states as the controller inputs. The structure of the proposed controller's feedback gain matrix is a combination of two feedback gain matrices to achieve adequate robust performance over different disturbances and communication losses. These two feedback gain matrices are; full-scale feedback gain matrix and decentralized scale feedback gain matrix. Both models are designed by considering all generators' states are available at the controller input side. However, both matrices' structures are different from each other. The conventional LQR state feedback control technique is used to design the full-scale matrix. On the other hand, the decentralized scale matrix is computed using a structurally constrained H 2 -norm optimization technique to acquire the required structure. The full-scale model gives robust performance under different faults. Whereas, the decentralized scale matrix will result robust performance against communication failures. Therefore, to achieve superior performance under all conditions, the proposed controller can be used. To show the efficacy of the proposed controller, it is compared with different existing controllers and the results are illustrated. Simulation studies are conducted on well-known IEEE 39-bus and 68-bus test systems to validate the robustness of the proposed controller considering different faults and communication failures. [ABSTRACT FROM AUTHOR]

In this paper, the vibration control of the multivariable model of rotor bearing systems is considered for investigation. Some simply structured controllers that can suppress vibrational disturbances are tested for their robustness via the H∞ optimality criteria. Initially, intelligent optimisation techniques are used to minimize the H∞ mixed-sensitivity norm of the Linear Fractional Transformation (LFT) of the simple two-term PI controllers acting on the rotor system models. This results in some controllers that can suppress the vibration but with a slow oscillatory response. After this, an appropriate interpretation of the Bode plot singular values of the combined sensitivity and control effort matrix is used to explain the performance shortcomings of this controller. Moreover, the existing simply structured controllers in the literature exhibiting a faster performance are examined by using singular value plots. It is shown that when the maximum singular value of the control effort matrix drops below the 0 db line, the performance will be boosted. Finally, the H∞ controllers are designed by using the robust control toolbox in MATLAB. This resulted in rapid disturbance rejection, with the vibration amplitude diminishing to zero after 0.3 s due to double-step disturbances. However, these controllers in the frequency domain have an order of eight and may not be realizable to be implemented in practice. It is concluded that examining the Bode plot of the maximum singular value of the control effort matrix is a useful tool for evaluating performance in the frequency domain. However, designing robust controllers by toolboxes in the time domain can lead to superb performance with higher-order controllers. [ABSTRACT FROM AUTHOR]

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]

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]

In the development of automated manipulators for fruit and vegetable picking technologies, the challenge of ensuring an efficient, stable, and loss-free picking process has been a complex problem. In such an environment, manipulators require the most efficient and robust control for effective operations. In this paper, a serial 9-DOF redundant manipulator (1P8R) is proposed with various controllers for trajectory tracking problems in agricultural applications. The dynamic analysis of redundant manipulator has been carried out using the Recursive Newton-Euler method. The joint configurations of the robot are determined using optimization techniques for specific Task Space Locations (TSLs) by avoiding obstacles. The process of generating joint trajectories has been implemented by considering the cubic polynomial function. For the task of controlling the robot trajectory tracking in the virtual agricultural environment, different combinations of Proportional (P), Integrative (I), Derivative (D), and Feed-Forward (FF) controllers are employed, and a comparative analysis has been performed among these controllers. To verify the performance of the manipulator, simulations are carried out in a virtual environment using Simulink software. Results show that the robot is able to reach specific TSL accurately with better control and it is found that the implementation of Feed-Forward and PID-CTC controllers has better performance in a complex environment. [ABSTRACT FROM AUTHOR]

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton–Jacobi Inequality (HJI) approach. WRK dynamics are established using the Lagrange approach at the outset of the analysis. Afterwards, the L 2 gain technique is applied to enhance the control motion solutions and provide the main features of the designed WRK control systems. To prove the stability of the controlled system, the HJI approach is investigated next using optimization techniques. The synthesized RBF NN algorithm supports the easy implementation of the adaptive controller, as well as ensuring the stability of the WRK system. An analysis of the numerical simulation results is performed in order to demonstrate the robustness and effectiveness of the proposed tracking control algorithm. The results showed the ability of the suggested controller of this study to find a solution to uncertainties. [ABSTRACT FROM AUTHOR]

In case an analytical approach to the selection of any weighting function is not possible, the selection process is usually a random and time-consuming process. In robust H∞ control theory, the selection of scalar, time, or frequencydependent weighting functions is the main issue to shape the amplitude-frequency characteristic curve of the feedback controller. Therefore, we propose a robust H∞ control approach which utilises the multi-objective grey wolf optimisation algorithm (MOGWO) to obtain the optimal performance weighting functions in the presence of right halfplane zeros and limited bandwidth constraints. A trade-off design flowchart is proposed, providing Pareto optimal solutions to choose the optimal configuration of the robust feedback controller. The control method is structured by combining the robust H∞ optimal technique and the multiobjective algorithm. The effectiveness of the approach is compared with the non-convex single-objective heuristic solutions like the multi-verse optimisation algorithm (MVO), whale optimisation algorithm (WOA), and grey wolf optimisation algorithm (GWO). The focus of this design is to track and stabilise the output voltage of the DC-DC converter in the presence of external disturbances and parameter uncertainties. The optimised controllers are implemented using a digital signal processor (DSP) on a 200 W interleaved boost converter. The simulation results and experimental findings show that the proposed control method provides supreme disturbance rejection along with maintaining the stability of the system. [ABSTRACT FROM AUTHOR]

This paper reviews real-time optimization from a reinforcement learning point of view. The typical control and optimization system hierarchy depend on the layers of real-time optimization, supervisory control, and regulatory control. The literature about each mentioned layer is reviewed, supporting the proposal of a benchmark study of reinforcement learning using a one-layer approach. The multi-agent deep deterministic policy gradient algorithm was applied for economic optimization and control of the isothermal Van de Vusse reactor. The cooperative control agents allowed obtaining sufficiently robust control policies for the case study against the hybrid real-time optimization approach. [ABSTRACT FROM AUTHOR]

Control inputs of fully constrained cable-driven parallel robots (CDPRs) are constrained by the positiveness of the cables tension, as cables merely apply tensile forces. The positive tension distribution (PTD) in CDPRs is usually guaranteed with iterative optimization techniques, utilizing the redundant actuation of the CDPR. The iterative nature of the conventional PTD limits their real-time application since the worst-case computation time of the iterative methods is not predictable. In addition, optimization methods are prone to model uncertainties. This article addresses the PTD problem in the fully constrained CDPRs with a control viewpoint. In the proposed approach, the PTD algorithm is an integral part of the controller, which explicitly generates positive values for the cables tension. To this aim, a saturation-type function is coupled with the controller, and its effect is compensated using a nonlinear disturbance observer. The stability of the proposed control scheme is also investigated in detail through Lyapunov’s second method, considering a nonsingular terminal sliding mode controller. Furthermore, the performance of the proposed methodology is compared with the conventional method for a six-degrees-of-freedom CDPR, in the presence of uncertainties. Finally, the effectiveness of the proposed control scheme is investigated through experiments. [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]

This paper deals with a robust multiobjective optimization problem involving nonsmooth/nonconvex real-valued functions. Under an appropriate constraint qualification, we establish necessary optimality conditions for weakly robust efficient solutions of the considered problem. These optimality conditions are presented in terms of Karush-Kuhn-Tucker multipliers and convexificators of the related functions. Examples illustrating our findings are also given. [ABSTRACT FROM AUTHOR]

Background and problem statement: Model-free or learning-based control, in particular, reinforcement learning (RL), is expected to be applied for complex robotic tasks. Traditional RL requires that a policy to be optimized is state-dependent, that means, the policy is a kind of feedback (FB) controllers. Due to the necessity of correct state observation in such a FB controller, it is sensitive to sensing failures. To alleviate this drawback of the FB controllers, feedback error learning integrates one of them with a feedforward (FF) controller. RL can be improved by dealing with the FB/FF policies, but to the best of our knowledge, a methodology for learning them in a unified manner has not been developed. Contribution: In this paper, we propose a new optimization problem for optimizing both the FB/FF policies simultaneously. Inspired by control as inference, the proposed optimization problem considers minimization/maximization of divergences between trajectories, one is predicted by the composed policy and a stochastic dynamics model, and others are inferred as optimal/non-optimal ones. By approximating the stochastic dynamics model using variational method, we naturally derive a regularization between the FB/FF policies. In numerical simulations and a robot experiment, we verified that the proposed method can stably optimize the composed policy even with the different learning law from the traditional RL. In addition, we demonstrated that the FF policy is robust to the sensing failures and can hold the optimal motion. [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]

In this article, a risk-aware motion control scheme is considered for mobile robots to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknown. We propose a novel model-predictive control (MPC) method for limiting the risk of unsafety even when the true distribution of the obstacles’ movements deviates, within an ambiguity set, from the empirical distribution obtained using a limited amount of sample data. By choosing the ambiguity set as a statistical ball with its radius measured by the Wasserstein metric, we achieve a probabilistic guarantee of the out-of-sample risk, evaluated using new sample data generated independently of the training data. To resolve the infinite-dimensionality issue inherent in the distributionally robust MPC problem, we reformulate it as a finite-dimensional nonlinear program using modern distributionally robust optimization techniques based on the Kantorovich duality principle. To find a globally optimal solution in the case of affine dynamics and output equations, a spatial branch-and-bound algorithm is designed using McCormick relaxation. The performance of the proposed method is demonstrated and analyzed through simulation studies using nonlinear dynamic and kinematic vehicle models and a linearized quadrotor model. The simulation results indicate that, even when the sample size is small, the proposed method can successfully avoid randomly moving obstacles with a guarantee of out-of-sample risk, while its sample average approximation counterpart fails to do so. [ABSTRACT FROM AUTHOR]

This paper presents an optimal decentralized control system for an isolated, networked dc microgrid with multiple sources and composite loads. The key feature of the proposed controller is that it requires only locally measurable states for controlling the local generation while achieving global stability. The controller is designed to minimize a performance index accounting for voltage regulation and branch current regulation with improved dynamics and stability. Decentralized control guarantees high reliability and modularity of the microgrid enabling plug-and-play operations of new units with little modifications. The solution of optimal decentralized control is obtained through constrained parameter optimization technique. The designed controller ensures stability over extended operating conditions of load and droop changes. This leads to a unique robust control law for the dc microgrid that obviates the need for frequent change of controller settings for any change in system parameters. Modal analysis and time-domain simulation of the controlled system substantiate robustness and effectiveness of the proposed decentralized control system in improving the system dynamic performance and optimal set point tracking with a unique controller configuration. [ABSTRACT FROM AUTHOR]

In general, unexpected failures in sensorless brushless DC (BLDC) motors can result in production downtime, costly repairs, and safety concerns. BLDC motors are commonly used in home appliances, the medical sector, aerospace, small‐scale, and large‐scale industries under uncertain operating conditions. Therefore, the fault detection and diagnosis (FDD) of BLDC motor drives can play a very important role in increasing their performance, reliability, robustness control, and operational safety under uncertain operating conditions in critical real‐time applications. To satisfy these issues of hall effect sensor, misplacement of a hall‐effect sensor, inverter IGBT open‐switch fault diagnosis, failure of hall effect sensor, lack of robustness speed control of BLDC motor, which has received substantial interest in academic and industry sectors to establish the proposed work optimization techniques approach FDD strategy for speed control of sensorless BLDC motor under uncertain operating conditions. The proposed optimization techniques such as Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) approach FDD strategies for BLDC motor drives. These FDD strategies simulated by the above optimization techniques on a sensorless BLDC motor with numerical Matlab/Simulink 2020a simulation results are verified. From the simulation results, out of three optimization techniques, the WOA‐based FDD strategies are very effective for both bearing and stator winding faults detection and diagnosis in sensorless BLDC motor drives. [ABSTRACT FROM AUTHOR]

This article presents a systematic robust current control design approach for three-phase voltage-source converters. Robustness is guaranteed by combining intrinsic passive properties of the impedance uncertainty at the point of common coupling together with stability results from a passivity-based control theory. This approach ensures stability against typical uncertainty sources at mid and high frequencies, such as cable resonances or other converters interaction, with significant less conservative performances than the obtained with the traditional robust control theory. The approach uses multiobjective controller synthesis formulation that allows us to logically combine robustness requirements with performance objectives avoiding heuristic iteration over the control structure and parameters. The controller synthesis is performed by means of a nonsmooth $\mathcal {H}_{\infty }$ optimization technique that tunes all free parameters of a vector-based controller function, which constraints its structure. This results in a synthesized controller with lower order than those obtained with convex optimization definitions of the $\mathcal {H}_{\infty }$ control problem. The design methodology is validated in time and frequency domain by means of theoretical analysis and experimental results with three usual grid filters: L, LCL, and LLCL. [ABSTRACT FROM AUTHOR]

Optimization of set-valued mappings has become an important subbranch of optimization. In set optimization, one of the main tools to compare sets is given by set relations, which are binary relations among sets. The main two set relations known in the literature are the upper and lower set relation, which are somewhat counterparts of each other and are used to model robust and optimistic solutions in uncertain (vector) programming, respectively. In this paper, we consider the convex case and propose a new set relation which is able to act as a weighting function between these two important set relations and therefore balances out possible gaps that can occur in modeling set optimization problems. [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]

In this paper, we suggest an improvement to our previously undertaken approach. Briefly, this approach consisted of applying the robust fractional predictive control (RFPC) for a class of constrained fractional systems implementing the min–max optimization technique. The RFPC controller requires resolution of a non-convex min–max optimization problem. The resolution of this problem, however, can only conduce to local solutions. The reason is simple: the objective function to be optimized is non-convex due to the presence of uncertainties. In the present work, we propose a global optimization-based RFPC controller for an uncertain fractional order system. A determinist global optimization method, namely, generalized geometric programming (GGP), is proposed to solve this problem for the uncertain fractional order system. The GGP method consists of converting a non-convex problem into a convex one via the application of variable changes. The technique of the convexification of this method is applied in line with the objective function to be optimized. Consequently, we obtained a new convex criterion and a convex problem. From an experimental point of view, we applied the proposed RFPC to a real thermal system using an STM32 microcontroller in order to control our thermal system. [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]