Fuzzy Feedback Control Research Articles

Hyperparameter Control Using Fuzzy Logic: Evolving Policies for Adaptive Fuzzy Particle Swarm Optimization Algorithm.

Heuristic optimization methods such as Particle Swarm Optimization depend on their parameters to achieve optimal performance on a given class of problems. Some modifications of heuristic algorithms aim at adapting those parameters during the optimization process. We present a novel approach to design such adaptation strategies using continuous fuzzy feedback control. Fuzzy feedback provides a simple interface where probes are sampled in the optimization process and parameters are fed back to the optimizer. The probes are turned into parameters by a fuzzy process optimized beforehand to maximize performance on a training benchmark. Utilizing this framework, we systematically established 127 different Fuzzy Particle Swarm Optimization algorithms featuring a maximum of 7 parameters under fuzzy control. These newly devised algorithms exhibit superior performance compared to both traditional PSO and some of its best parameter control variants. The performance is reported in the single-objective bound-constrained numerical optimization competition of CEC 2020. Additionally, two specific controls, highlighted for their efficacy and dependability, demonstrated commendable performance in real-world scenarios from CEC 2011.

Inversion-based fuzzy adaptive control with prespecifiable tracking accuracy for uncertain hysteretic systems

Inversion-based control strategies have the outstanding effectiveness in the compensation for hysteresis nonlinearities. However, when the plant is driven by smart material-based actuator with the information absence of hysteresis output, there still exist technical gaps in the construction of hysteresis inverse controller. Therefore, this study aims to address this gap by proposing a novel control scheme. Technically, a novel hysteresis inverse algorithm, significantly reducing computational burden, has been proposed based on a novel adaptive estimation method for the unknown parameter (density function) in the Preisach-typed hysteresis nonlinearity. Furthermore, the inverse compensation error, the nonlinearities and uncertainties appeared in the controlled plant are accommodated by the innovative adaptive fuzzy feedback controller. With our scheme, it can ensure that the closed-loop stability, predefined steady-stated tracking performance and the convergence of algorithms including inverse algorithm and adaptive updating laws. In addition to theoretical analysis, we have validated the effectiveness of the proposed control scheme through simulation comparisons and experimental results conducted by the real-life piezoelectric actuator.

Sound Feedback Fuzzy Control for Optimizing Bone Milling Operation During Robot-Assisted Laminectomy

Sound Feedback Fuzzy Control for Optimizing Bone Milling Operation During Robot-Assisted Laminectomy

Quantized Fuzzy Feedback Control for Electric Vehicle Lateral Dynamics

Quantized Fuzzy Feedback Control for Electric Vehicle Lateral Dynamics

Performance-Driven Fault Detection for Uncertain Takagi–Sugeno Fuzzy Feedback Control Systems

This paper mainly studies the performance-driven fault detection for general uncertain Takagi-Sugeno fuzzy feedback control systems, where the infinite-horizon quadratic index is introduced to describe the concerned system performance. Different from the existing output-driven fault detection methods, the proposed approach aims to detect the performance degradations induced by faults. For this purpose, the performance index embedded with the information of system dynamics is reformulated for linear feedback control systems with reference inputs. The performance residual, which is adopted as the evaluation function, is derived based on the Bellman equation and further represented into an explainable form. Then, for uncertain linear feedback control systems, the boundaries of the performance residual are analyzed via the linear matrix inequality technique and an advantaged threshold setting scheme is proposed with the aid of randomized algorithm. Concerning the main objective, a novel approximation method which combines the fuzzy blend of local performance indices and the radial basis function neural network is developed to approximate the global performance index for Takagi-Sugeno fuzzy systems. Based on the result in linear case, the performance residual is constructed along the closed-loop system trajectory and the threshold is determined for the fuzzy control systems with reference inputs and model uncertainties. Finally, simulation studies are provided to show the effectiveness of the proposed theoretical results.

Output feedback fuzzy model predictive control with multiple objectives

We present a dynamic output feedback fuzzy model predictive control (FMPC) approach for the Takagi–Sugeno model with bounded disturbance in this paper. The proposed approach relies on the formulation, at each sampling instant, of a lexicographic optimization problem that takes into account multiple objectives including improving the control performance, enlarging the region of attraction (ROA) and tightening the estimation error set (EES). Since the controller parameters and Lyapunov matrices are kept as degrees of freedom for the lower-ordered optimization problems, larger ROA and less conservative EES are obtained. As a result, the control performance can be greatly improved with respect to previous works. Furthermore, we prove the recursive feasibility of the approach and guarantee the stability of the closed-loop system. The result is verified through a numerical example.

Design of Fuzzy Observers and Output Feedback Fuzzy Controllers for Takagi–Sugeno Discrete Systems Via the Matrices Norms Approach

Design of Fuzzy Observers and Output Feedback Fuzzy Controllers for Takagi–Sugeno Discrete Systems Via the Matrices Norms Approach

Fuzzy Lyapunov exponents placement for chaos stabilization

This paper presents a novel chaos stabilization method using fuzzy Lyapunov exponents placement. An adaptive neuro-fuzzy inference system (ANFIS) learning method is used to estimate the unknown parameters of the chaotic system, and a nonlinear fuzzy feedback controller is designed to place the Lyapunov exponents of the system in the desired location to remove chaotic conduct. The proposed approach is robust to external disturbances and provides a higher performance than the current chaos stabilization methods, as demonstrated by numerical examples.

Robust stability and fuzzy controller synthesis for a class of hybrid re-entrant manufacturing systems

This paper addresses the problem of stability analysis and state feedback fuzzy control design for multi-line re-entrant manufacturing systems (RMSs) with impulse-type state jumps. Under the impulse effect, the RMS is modeled as a hyperbolic impulsive partial differential equation (IPDE) that possesses hybrid characteristics. Then, a matrix inequality condition depending on the discrete dynamic information is presented to deal with the jump behavior of the IPDE at impulse instants. Moreover, a robust fuzzy controller based on the parallel distributed compensation scheme is derived to render the RMS to a desired producing mode with steady feeding and output rates. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed robust fuzzy controller.

Simulation Analysis Of Vehicle Four-Wheel Steering Control

In order to study the handling stability of four-wheel steering vehicle, the vehicle is simplified into a linear two-degree-of-freedom model. The fuzzy control technology is used to study the proportional feedforward + fuzzy feedback control strategy of four-wheel steering vehicle, and the fuzzy control toolbox in Matlab/Simulink is used to establish the four-wheel steering control system. The simulation results show that the fuzzy control can effectively improve the response of rear wheel steering, improve the maneuverability of the vehicle at low speed, reduce the turning radius, and improve the handling stability of the vehicle at high speed.

Membership-function-dependent memory controller design for interval type-2 fuzzy systems under fading channels

This paper studies the problem of membership-function-dependent memory output feedback (MOF) controller design for interval type-2 (IT2) fuzzy systems under fading channels. A time-varying stochastic process is introduced to model the unpredictable channel fading phenomenon. Taking the impact of channel fading into consideration, a non-parallel distribution compensation (non-PDC) strategy is employed to construct a class of memory output feedback fuzzy controllers. By fully exploiting the membership functions, the membership-function-dependent performance analysis and controller synthesis results have been obtained, such that the closed-loop system can be guaranteed with mean-square exponential stability and performance γ. Finally, simulation studies have been presented to demonstrate the effectiveness of the proposed approach.

Trajectory tracking control strategy of the gantry welding robot under the influence of uncertain factors

In this paper, aiming at the problem of high-precision trajectory tracking of the gantry welding robot system under the influence of uncertain factors, a composite adaptive fuzzy compensation controller based on the computed torque control strategy has been proposed. The controller is composed of an adaptive fuzzy feedback control strategy and a dead zone adaptive fuzzy control strategy, which realizes high-precision trajectory tracking of the gantry welding robot, improves the solving speed of the control algorithm, and its tracking errors has been reduced. Screw theory and Lie group lie algebra are used to solve the problem of high algorithm complexity of controllers in multi-degree-of-freedom robot systems due to Newton-Euler dynamics modeling. Based on the computed torque control, the adaptive fuzzy feedback control strategy is adopted to compensate for the modeling errors and external disturbances in the gantry welding robot system, and the dead zone adaptive fuzzy control strategy is designed to compensate for the nonlinear dead zone structure with unknown parameters. The Lyapunov equation is introduced to prove the stability of the controller. Finally, the controller designed in this paper is compared with the conventional controller through simulation and experiment on the gantry welding robot platform, which verifies the effectiveness and superiority of the controller.

A T-S fuzzy state observer-based model predictive reset control for a class of fuzzy nonlinear systems with event-triggered mechanism

In this study, the problem of observer-based control for a class of nonlinear systems using Takagi-Sugeno (T-S) fuzzy models is investigated. The observer-based model predictive event-triggered fuzzy reset controller is constructed by a T-S fuzzy state observer, an event-triggered fuzzy reset controller, and a model predictive mechanism. First, the proposed controller utilizes the T-S fuzzy model and is constructed based on state observations and discrete sampling output, which can greatly reduce the occupation of communication resources. Then, the model predictive strategy for reset law design is designed in this paper. With a reasonable reset of the controller state at certain instants, the performance of the reset control systems is improved. Finally, the validity of the proposed method is illustrated by simulation. The merits of the proposed controller in improving transient performance and reducing the communication occupation are demonstrated by comparing its results with the output feedback fuzzy controller and the first-order fuzzy reset controller.

Event-triggered output feedback dissipative control of nonlinear systems under DoS attacks and actuator saturation

This paper presents a novel event-triggered dynamic output feedback dissipative control of nonlinear systems under intermittent denial-of-service (DoS) attacks and actuator saturation. Firstly, based on attack information, a secure event-triggered mechanism (ETM) is introduced, which not only saves systems resources but also is Zeno-free and resilient to DoS attacks. Secondly, a switched T–S fuzzy closed-loop system model is built, which unifies the parameters of nonlinear plant, noises, ETM, DoS attacks, switched output feedback fuzzy controller, and actuator saturation all in one framework. Thirdly, low conservative exponential stability criteria are derived while guaranteeing strict , , -dissipativity, and hence the relationships between system performance and factors such as DoS attacks, secure ETM, noises and actuator saturation are established. Further, sufficient conditions are given for the co-design of the switched output-based fuzzy controller and the secure ETM. Finally, the effectiveness of the proposed method is confirmed by numerical examples, achieving over 92% system resources.

Study of the Angular Positioning of a Rotating Object Based on Some Computational Intelligence Methods

The paper presents the result of a study that can be included in the broader field of research aimed at increasing the performance of automatic motion control systems. The main contribution of the article is the comparative study of three control methods from the domain of computational intelligence—state feedback fuzzy control, neural predictive control, and neural model reference control—and three linear control methods—error feedback control, digital control, and state feedback control, in the case of positioning a rotating object around a central axis. The developed control structures were modeled and simulated using MATLAB/Simulink. The paper presents the chosen control structures; how to dimension them; the parameters of the linear, fuzzy, and neural regulators; the training parameters of the neural networks; and the characteristics of the variables of the control systems in the transient regime and the steady-state regime. Transient characteristics obtained for the six control structures are compared from the point of view of their control efficiency criteria. The differences in performance criteria between the control methods studied are small. All these studied methods make the regulated system to be carried on various state trajectories, short response times are obtained with aperiodic and asymptotic behavior, and the differences between the values of the efficiency indicators are small.

Takagi–Sugeno State Delayed Feedback and Integral Control for PV Systems: Modeling, Simulation, and Control

The integration of the large-scale photovoltaic systems has experienced significant growth, which is similarly expected to occur with small-scale photovoltaic systems. Since small-scale systems must be simple in cost-effective components, control strategies must be implemented in low complexity circuits. However, current maximum power point tracking (MPPT) algorithms are generally complex and require electronic components to support variable control gains for different irradiance conditions, preventing simple MPPT implementations suitable for small-scale photovoltaic systems. This paper proposes a new control strategy to tackle the power tracking problem of the power systems. First, a dynamic model of the photovoltaic system is described and converted into a Takagi–Sugeno (T-S) model. Then, an MPPT scheme is proposed in series with a fixed integral and a fuzzy gain state delay feedback controller, which avoids the need for a variable control gain, simplifying the electronic implementation of the control strategy. New delay-dependent stabilization conditions based on the Lyapunov-Krasovskii functional are proposed in terms of a convex optimization problem, where the delayed feedback and integral gains are designed simultaneously. Simulation results using Matlab and Simulink are used to validate the proposed method.

Study of the Angular Positioning of a Rotating Object with Fuzzy Control by State Feedback

The study in the paper is placed in the broad context of research for increasing the efficiency of motion control. The purpose of the paper is to make a comparative analysis of the state feedback fuzzy control with the linear control for angular positioning of mechanical parts. The structure of the fuzzy control system and its design is presented. Transient characteristics obtained are compared from the point of view of their control efficiency criteria. The differences in performance criteria between the control methods studied are small

Angular Positioning Based on State Feedback Fuzzy Control

The study in the paper is placed in the broad context of research for increasing the efficiency of motion control. The purpose of the paper is to make a comparative analysis of the state feedback fuzzy control with the linear control for angular positioning of mechanical parts. The structure of the fuzzy control system is presented. Transient characteristics obtained are compared from the point of view of their control efficiency criteria. The differences in performance criteria between the control methods studied are small

Observer-based fuzzy feedback control for nonlinear systems subject to transmission signal quantization

This paper investigates the observer-based quantized output feedback control for a kind of nonlinear discrete-time systems. The system studied in this paper is denoted by a Takagi–Sugeno (T–S) fuzzy model. Under digital communication channels, all transmitted signals between the system and the actuator (including the controller and the observer) will be quantized by the dynamic quantizers in the closed-loop system. Taking into consideration the design of the controller, observer, and dynamic parameters of quantizers, an effective matrix inequality decoupling method is presented to handle the problem. One is shown that the proposed design conditions of the controller, observer, dynamic parameters of quantizers are summarized in a matrix inequality, which can be synthesized synchronously. The resulting design ensures that the quantized closed-loop system can meet the prescribed H∞ performance. Finally, the availability and the feasibility of the presented design method are demonstrated by a mechanical motion system.

Summarisation, Simulation and Comparison of Nine Control Algorithms for an Active Control Mount with an Oscillating Coil Actuator

With the further development of the automotive industry, the traditional vibration isolation method is difficult to meet the requirements for wide frequency bands under multiple operating conditions, the active control mount (ACM) is gradually paid attentions, and the control algorithm plays a decisive role. In this paper, the ACM with oscillating coil actuator (OCA) is taken as the object, and the comparative study of the control algorithms is performed to select the optimal one for ACM. Through the modelling of ACM, the design of controller and the system simulations, the force transmission rate is used to compare the vibration isolation performance of the nine control algorithms, which are least mean square (LMS) adaptive feedforward control, recursive least square (RLS) adaptive feedforward control, filtered reference signal LMS (FxLMS) adaptive control, linear quadratic regulator (LQR) optimal control, H2 control, H∞ control, proportional integral derivative (PID) feedback control, fuzzy control and fuzzy PID control. In summary, the FxLMS adaptive control algorithm has the better performance and the advantage of easier hardware implementation, and it can apply in ACMs.