Robust Fuzzy Control Research Articles

Fuzzy Supervisory Passivity-Based High Order-Sliding Mode Control Approach for Tidal Turbine-Based Permanent Magnet Synchronous Generator Conversion System

Higher efficiency, predictability, and high-power density are the main advantages of a permanent magnet synchronous generator (PMSG)-based hydro turbine. However, the control of a PMSG is a nontrivial issue, because of its time-varying parameters and nonlinear dynamics. This paper suggests a novel optimal fuzzy supervisor passivity-based high order sliding-mode controller to address problems faced by conventional techniques such as PI controls in the machine side. An inherent advantage of the proposed method is that the nonlinear terms are not canceled but compensated in a damped way. The proposed controller consists of two main parts: the fuzzy gain supervisor-PI controller to design the desired dynamic of the system by controlling the rotor speed, and the fuzzy gain-high order sliding-mode control to compute the controller law. The main objectives are feeding the electrical grid with active power, extracting the maximum tidal power, and regulating the reactive power and DC voltage toward their references, whatever the disturbances caused by the PMSG. The main contribution and novelty of the present work consists in the new robust fuzzy supervisory passivity-based high order sliding-mode controller, which treats the mechanical characteristics of the PMSG as a passive disturbance when designing the controller and compensates it. By doing so, the PMSG tracks the optimal speed, contrary to other controls which only take into account the electrical part. The combined high order sliding-mode controller (HSMC) and passivity-based control (PBC) resulted in a hybrid controller law which attempts to greatly enhance the robustness of the proposed approach regardless of various uncertainties. Moreover, the proposed controller was also validated using a processor in the loop (PIL) experiment using Texas Instruments (TI) Launchpad. The control strategy was tested under parameter variations and its performances were compared to the nonlinear control methods. High robustness and high efficiency were clearly illustrated by the proposed new strategy over compared methods under parameter uncertainties using MATLAB/Simulink and a PIL testing platform.

A fuzzy adaptive fractional order-PID controller for frequency control of an islanded microgrid under stochastic wind/solar uncertainties

Frequency control is the key factor in a renewable energy-based microgrid system. The inherent dynamics and low inertia of most power generating units of microgrids lead to frequency oscillations in the system. This manuscript well addresses on a robust fuzzy adaptive Fractional Order-PID (fuzzy FO-PID) controller and this is used in an AC microgrid model for achieving frequency control under various disturbances. Since a microgrid is devised with a lot of low inertia and large dynamics-based power-generating units, conventional controllers are found to be inferior while controlling the frequency of a microgrid. In the light of this, this work employs a robust fuzzy adaptive Fractional Order-PID controller to achieve improved frequency performance in the microgrid. The proposed fuzzy FO-PID controller acts as a secondary frequency control loop to amend necessary control actions over vibrant frequency under different uncertainties like load dynamics, wind fluctuation and solar intensity variation. The proposed controller is governed by a novel teaching–learning-based optimization (TLBO) technique to obtain optimal parameters under various disturbances. An assignment on a comparative study has been synthesised to justify the supremacy of the proposed TLBO algorithm in regard to the frequency control of a microgrid system.

Quadrotor UAV attitude stabilization using fuzzy robust control

In this paper, a robust controller for attitude stabilization of a small quadrotor helicopter is developed. The TS (Takagi-Sugeno) fuzzy model approach and the [Formula: see text] robust control are combined to produce the proposed algorithm. Besides, disturbances and parametric uncertainties are considered. First, the nonlinear model of the vehicle is linearized around several operating points to obtain the representation of a TS fuzzy model, which represents the nonlinearity of the system dynamics. Then, a robust fuzzy controller is synthesized which guarantees desired control performances. The given controller is designed using numerical tools such as linear matrix inequalities (LMI). Finally, simulation results and real-time experiments are presented to validate the performance of the proposed scheme to robustly stabilize the quadrotor dynamics at the desired reference.

Fuzzy Robust Constrained Control for Nonlinear Systems With Input Saturation and External Disturbances

This article proposes a high-order disturbance observer (HODO) and dynamic surface control (DSC) technique-based adaptive fuzzy control scheme for nonlinear systems subjected to input saturation and external time-varying disturbances. First, based on a Sigmoid function, the saturation input is tackled by utilizing a well-defined nonlinear smooth function. Furthermore, HODO and fuzzy logic systems are used to estimate the external disturbances and to handle the lumped unknown functions, respectively. Then, by using the backstepping method and DSC technique, a HODO-based adaptive fuzzy tracking control scheme is proposed for nonlinear systems with saturation nonlinearity, uncertainties, and external disturbances. The Lyapunov analysis method is used to prove that all signals in the entire system are semiglobally uniformly ultimately bounded (SGUUB). In addition, the tracking error converges to a compact set with a tunable error bound determined by some design parameters. Finally, a numerical simulation of two-stage chemical reactor shows the effectiveness of the developed tracking control strategy.

Vehicle lateral motion control via robust delay-dependent Takagi-Sugeno strategy

Presented in this research paper is an integrated direct yaw moment control (DYC) and active front steering (AFS) for an uncertain vehicle lateral dynamics model considering network-induced communication delay, which is a time-varying continuous function with a known upper bound. Firstly, we consider tire cornering stiffness as a non-linear norm-bounded uncertain system that is modeled by fuzzy membership functions, and then vehicle lateral dynamics model is expressed by a set of linear Takagi-Sugeno (T-S) uncertain fuzzy models. Secondly, since the network-induced communication delay in vehicle control system is an inherent reason for stability and performance degradation, we derive a robust delay-dependent [Formula: see text] control methodology via the Lyapunov-Krasovskii functional for stability and performance conditions of the closed-loop system. For the synthesis, the robust control method is employed within the T-S fuzzy-model-based analysis framework and formulations are performed based on the solution of delay-dependent linear matrix inequalities (LMIs). The simulation study is presented using MATLAB/Simulink to show the achieved improvements in tracking variables via the designed robust fuzzy [Formula: see text] state-feedback controller. The proposed fuzzy robust delay-dependent controller is compared with a linear robust delay-dependent controller to clearly show the tracking improvements for different road conditions. Moreover, a performance-based analysis is carried out to demonstrate the advantage of the design with respect to different delay values. It is confirmed from the analysis results that the proposed fuzzy controller can successfully stabilize and possess improved tracking performance for vehicle lateral motion control.

Robust Fuzzy Model Predictive Control with time delays for Nonlinear Systems

In this work, a robust fuzzy model predictive control (RFMPC) is designed for uncertain time-delay systems under input constraints with external disturbances. The proposed controller is based on an augmented state space model that includes the state variables and output tracking error variable of system. As a result, the proposed control based on augmented state space model can guarantee the convergence and tracking performance of system. To ensure the stability of the closed loop constrained time-delays system, in the form of linear matrix inequalities (LMIs), the Lyapunov–Krasovskii (L-K) theory is employed to derive sufficient stability conditions where the L-K theory has the ability to reflect the system’s original state space and its advantages in controller synthesis and computation. As a result, less conservative stable conditions in terms of LMIs are given and used to ensure the asymptotically robust stability of closed-loop constrained system, and the controller construction procedure in shifted into a minimization problem subjected to certain constraints in terms of LMI. The robustness properties of the proposed RFMPC approach are compared with the results of a standard predictive control MPC applied to control a nonlinear system with parameter uncertainties and time-delays.

Adaptive Fuzzy Robust Control of a Bionic Mechanical Leg With a High Gain Observer

In this paper, an observer-based adaptive fuzzy robust controller is proposed for trajectory tracking control of a bionic mechanical leg (BML) with unmeasured system states, dynamic uncertainties and external disturbances. A high gain observer (HGO) is constructed to estimate the unavailable joint velocities using the joint position feedback signals, while an adaptive fuzzy logic system (AFLS) is employed to address the lumped uncertainties. The nonlinear robust controller is then synthesized via backstepping method to improve the position tracking performance. The stability of the closed loop system is mathematically demonstrated via the Lyapunov’s stability theory. It is proven that under the proposed controller all the closed-loop signals are bounded and the trajectory tracking errors converge to a small neighborhood of the origin with appropriate design parameters. The effectiveness of the proposed control scheme is illustrated by simulation studies.

Path planning and robust fuzzy output-feedback control for unmanned ground vehicles with obstacle avoidance

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.

Adaptive Robust Dynamic Surface Integral Sliding Mode Control for Quadrotor UAVs under Parametric Uncertainties and External Disturbances

A robust adaptive fuzzy nonlinear controller based on dynamic surface and integral sliding mode control strategy (ADSISMC) is proposed to realize trajectory tracking for a class of quadrotor UAVs. In this study, the composite factors including parametric uncertainties and external disturbances are added to controller design, which make it more realistic. The quadrotor model is divided into two subsystems of attitude and position that make the control design become feasible. The main contributions of the proposed ADSISMC strategy are as follows: (1) The combination of dynamic surface and integral sliding mode makes the system always in sliding stage by finding the appropriate initial position compared with the common sliding mode, and the complexity of explosion in backstepping method is eliminated. (2) By introducing the fuzzy system, the unknown functions and uncertainties can be approximated which significantly improves the robustness and the tracking performance. (3) The switching control strategy is utilized to compensate for the errors between estimated and ideal inputs; the tracking performance of the whole system has been significantly improved. The simulation results show the effectiveness of the proposed control method.

Combined indirect & direct adaptive fuzzy decentralised control of large-scale systems with strong interconnection and its application

To achieve better adaptation properties, the proposed controller is a weighted combination of indirect and direct adaptive fuzzy controls such that both fuzzy descriptions and control rules can be incorporated under a united controller design at the same time. By using fuzzy systems and robust control technique, the interconnections between subsystems are extended to general unknown nonlinear functions. No a priori knowledge of lower and upper bounds on lumped uncertainties is required to construct each local controller. The resulting closed-loop large-scale system is proved to be asymptotically stable. The controller design is applicable to an automated highway system, and simulation results confirm its practical usefulness.

Moving least squares approximation-based online control optimised by the team game algorithm for Duffing-Holmes chaotic problems

ABSTRACT In this paper, an online optimal adaptive robust fuzzy controller based on the Moving Least Squares (MLS) and Team Game Algorithm (TGA) is introduced to control uncertain chaotic nonlinear systems. At first, a robust supervisory stabiliser and a fuzzy adaptive PID controller are designed and combined to handle a Duffing-Holmes chaotic oscillator. Next, the TGA is utilised to optimise the parameters of the designed fuzzy adaptive robust controller for different values of system disturbances. Then, to adapt the optimum design parameters of the controller with the uncertain values of the external disturbances, the MLS approximation is implemented. In order to evaluate the performance of the proposed technique, it is applied on a Duffing-Holmes chaotic nonlinear oscillator with different initial conditions. The results and the analysis prove the efficiency of the proposed controller with regard to uncertain systems’ challenging external disturbances that impair the stability, accuracy and optimality of the system.

Fuzzy-Logic-Based Robust Attitude Control of Networked Spacecraft via Event-Triggered Mechanism

This article studies the robust fuzzy attitude control strategy for the networked spacecraft consisting of physically independent modules that interact through network communication. The effects of inertia uncertainties, delayed feedback, actuator saturation, and external disturbances are simultaneously taken into account when designing the attitude control law. Based on aperiodic sampling, an adaptive event-triggered scheme avoiding Zeno phenomenon naturally is introduced to reduce the communication burden of network. By representing spacecraft kinematics and dynamics with Takagi-Sugeno fuzzy model, a robust fuzzy controller with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -gain performance is newly developed to fulfill the attitude stabilization of networked spacecraft under complex constraints. Moreover, a novel Lyapunov functional is constructed to perform the stability analysis and controller synthesis of the resulting closed-loop system. The main results of this article are formulated in terms of linear matrix inequalities, which can be easily resolved by standard software. Finally, simulation results are presented to validate the effectiveness of the proposed attitude control strategy.

Energy management in photovoltaic battery hybrid systems: A novel type-2 fuzzy control

In this paper, a new robust fuzzy control approach is presented to power management in the photovoltaic (PV)-battery hybrid system. The stability and robustness of the developed control scheme is analyzed through the Lyapunov technique. The dynamics of the converters, battery and PV system are assumed to be fully unknown. Specifically, the uncertainties are online estimated by the adaptive interval type-2 fuzzy neural networks. It is shown that the implemented control technique results are in good performance in the presence of time-varying radiation, changes of temperature and output load variation in comparison with the other well-known control techniques.

Frequency regulation of an electric vehicle-operated micro-grid under WOA-tuned fuzzy cascade controller

ABSTRACT The present manuscript highlights on the frequency regulation of an islanded AC micro-grid under different uncertainties. The advancement in battery-operated vehicles (electric vehicles) keeps environment clean and healthy due to carbonless gas emissions. However, charging of electric vehicles creates frequency instability issues in the micro-grid system. Additionally, the incorporation of low inertia and large uncertainties of the micro-grid system puts adverse effect over frequency stabilisation. To obtain multi-beneficial (green environment and nominal frequency) scenario in micro-grid systems, the manuscript proposes a robust fuzzy cascade PD-PI controller for this study. A nature-inspired whale optimisation algorithm (WOA) algorithm is suggested to optimally design the proposed fuzzy cascade PD-PI controller. In order to defend the viability of the proposed approaches few comparative analyses have been synthesised in this study. Finally, it is revealed the proposed WOA-optimised fuzzy cascade PD-PI controller gracefully improves micro-grid dynamic performance under various operational conditions.

Fuzzy robust control applied to rotor supported by active magnetic bearing

The technology associated with active magnetic bearings has been widely used in the last years and can be considered as being one of the most promising solutions for several applications in rotating machinery. Lubricants are not necessary, and high rotation speeds are reached without any relevant heating. Active magnetic bearings are classified as mechatronic systems because they are composed of mechanical and electronic parts that are controlled by using dedicated software. In this context, the present work is devoted to the design of robust controllers applied to supercritical rotors supported by active magnetic bearings. For this aim, numerical and experimental tests were carried out. Different from previous studies reported in the literature, the present contribution proposes a novel design procedure to robustify the neuro-fuzzy controller of a rotor supported by active magnetic bearings based on optimal robust design. This optimal design procedure tunes the robust neuro-fuzzy controller taking into account the optimal balance between vibration attenuation performance and robustness, that is the increase in vibration attenuation implies the reduction in the robustness. The first stage of the controller synthesis is dedicated to the specification of all design requirements. Then, the adaptative neuro-fuzzy controller was obtained, starting from the determination of the plant dominant poles and finally performing the model-based analysis of the system stability and performance. Finally, the vibration control performance and robustness are optimally balanced by using a robust optimization procedure. The behavior of the controller was evaluated by investigating the unbalance response of the rotating system. The obtained results demonstrated the effectiveness of the conveyed approach.

Static-Output-Feedback Based Robust Fuzzy Wheelbase Preview Control for Uncertain Active Suspensions With Time Delay and Finite Frequency Constraint

This paper proposes a static-output-feedback based robust fuzzy wheelbase preview control algorithm for uncertain active suspensions with time delay and finite frequency constraint. Firstly, a Takagi-Sugeno (T-S) fuzzy augmented model is established to formulate the half-car active suspension system with consideration of time delay, sprung mass variation and wheelbase preview information. Secondly, in view of the resonation between human's organs and vertical vibrations in the frequency range of 4-8 Hz, a finite frequency control criterion in terms of $H_{\infty}$ norm is developed to improve ride comfort. Meanwhile, other mechanical constraints are also considered and satisfied via generalized $H_{2}$ norm. Thirdly, in order to maintain the feasibility of the controller despite of some state variables are not online-measured, a two stage approach is adopted to derive a static output feedback controller. Finally, numerical simulation results illustrate the excellent performance of the proposed controller.

Dynamic positioning of ships based on robust fuzzy observer

In this study, the robust fuzzy observer-based control scheme for the dynamic positioning of marine vessels is presented. The proposed controller assumes that the environmental disturbances are unknown but bounded with known upper limit. First, the authors apply the Takagi–Sugeno fuzzy model to approximate the ship model. Using formulated fuzzy model, the robust fuzzy observer is developed to approximate the unknown states. Then, by employing optimal H ∞ disturbance attenuation technique, the effects of the environmental disturbances can be attenuated to least value. Finally, the linear matrix inequality technique is hired for solving the optimal robust problem. Model ship is used for simulation, and results are given to prove the validity of the presented controller.

Robust Adaptive Fuzzy Tracking Control for Uncertain MIMO Nonlinear Nonminimum Phase System

Parameter uncertainty and unmodeled dynamics are inevitable for an actual nonlinear system, and are hard to deal with in control design, especially when the internal dynamics of this nonlinear system are unstable. The control design for a multiinput multioutput nonlinear nonminimum phase system with parameter uncertainty and unmodeled dynamics is discussed in this paper. The internal dynamics of nonminimum phase system are unstable, the control of this kind of system is challenging. Ideal internal dynamics (IID) based controller design method are utilized here, and a partially linearized model is constructed. A state tracking model is constructed on account of the partially linearized model and IID. Then the robust fuzzy controller design problem is discussed and a fuzzy logical system is utilized to identify the parameter uncertainty and unmodeled dynamics. A robust adaptive fuzzy controller is proposed for the stability of the nonlinear nonminimum phase system with parameter uncertainty and unmodeled dynamics. Finally, by simulations on vertical takeoff and landing aircraft, the availability of the presented adaptive fuzzy controller is validated.

Discrete-time robust fuzzy control synthesis for discretized and perturbed ship fin stabilizing systems subject to variance and pole location constraints

A discrete-time robust fuzzy controller design problem for the discretized and perturbed nonlinear ship fin stabilizing systems is studied in this paper. The discrete-time Takagi–Sugeno fuzzy model is used to represent the nonlinear ship fin stabilizing systems. The control performance considered in this paper includes stability requirement, pole location constraint, individual state variance constraint, and minimum output variance. Applying the Lyapunov theory, a discrete-time robust fuzzy controller is designed based on parallel distributed compensation technology and the relevant conditions are deduced in the form of linear matrix inequalities. By solving these conditions, a discrete-time robust fuzzy controller can be obtained to satisfy the above performance constraints. At last, a simulation for controlling a discrete-time nonlinear ship fin stabilizing system is provided to show the feasibility and applicability of the proposed robust fuzzy control method.

Robust fuzzy sampled-data attitude control of spacecraft with actuator saturation and persistent disturbance

This paper is concerned with robust fuzzy attitude control of spacecraft with aperiodically sampled measurements, actuator saturation, and persistent disturbance. Takagi-Sugeno (T-S) fuzzy model is employed to describe the kinematics and dynamics of spacecraft. Under the constraint of actuator saturation, a digital fuzzy controller consisting of discrete-time states and an exponentially decaying term is newly proposed to carry out exponential attitude stabilization of the considered spacecraft. Moreover, compared with H∞ performance most often encountered when considering robust attitude control of spacecraft, L∞-gain performance is designed to deal with persistent bounded disturbance in this study. Based on time-dependent Lyapunov functional and invariant set, the stability of closed-loop system is strictly proved. With the aid of convex optimization techniques, the desired sampled-data controller can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, illustrative simulation results are presented to validate the effectiveness of the theoretical developments shown in this paper.