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1.
The interconnected large-scale power systems are liable to performance degradation under the presence of sudden small load demands, parameter ambiguity and structural changes. Due to this, to supply reliable electric power with good quality, robust and intelligent control strategies are extremely requisite in automatic generation control (AGC) of power systems. Hence, this paper presents an output scaling factor (SF) based fuzzy classical controller to enrich AGC conduct of two-area electrical power systems. An implementation of imperialist competitive algorithm (ICA) is made to optimize the output SF of fuzzy proportional integral (FPI) controller employing integral of squared error criterion. Initially the study is conducted on a well accepted two-area non-reheat thermal system with and without considering the appropriate generation rate constraint (GRC). The advantage of the proposed controller is illustrated by comparing the results with fuzzy controller and bacterial foraging optimization algorithm (BFOA)/genetic algorithm (GA)/particle swarm optimization (PSO)/hybrid BFOA-PSO algorithm/firefly algorithm (FA)/hybrid FA-pattern search (hFA-PS) optimized PI/PID controller prevalent in the literature. The proposed approach is further extended to a newly emerged two-area reheat thermal-PV system. The superiority of the method is depicted by contrasting the results of GA/FA tuned PI controller. The proposed control approach is also implemented on a multi-unit multi-source hydrothermal power system and its advantage is established by Correlating its results with GA/hFA-PS tuned PI, hFA-PS/grey wolf optimization (GWO) tuned PID and BFOA tuned FPI controllers. Finally, a sensitivity analysis is performed to demonstrate the robustness of the proposed method to broad changes in the system parameters and size and/or location of step load perturbation.  相似文献   

2.
This article presents a novel tuning design of Proportional-Integral-Derivative (PID) controller in the Automatic Voltage Regulator (AVR) system by using Cuckoo Search (CS) algorithm with a new time domain performance criterion. This performance criterion was chosen to minimize the maximum overshoot, rise time, settling time and steady state error of the terminal voltage. In order to compare CS with other evolutionary algorithms, the proposed objective function was used in Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for PID design of the AVR system. The performance of the proposed CS based PID controller was compared to the PID controllers tuned by the different evolutionary algorithms using various objective functions proposed in the literature. Dynamic response and a frequency response of the proposed CS based PID controller were examined in detail. Moreover, the disturbance rejection and robustness performance of the tuned controller against parametric uncertainties were obtained, separately. Energy consumptions of the proposed PID controller and the PID controllers tuned by the PSO and ABC algorithms were analyzed thoroughly. Extensive simulation results demonstrate that the CS based PID controller has better control performance in comparison with other PID controllers tuned by the PSO and ABC algorithms. Furthermore, the proposed objective function remarkably improves the PID tuning optimization technique.  相似文献   

3.
The current paper addresses the fuzzy adaptive tracking control via output feedback for single-input single-output (SISO) nonlinear systems in strict-feedback form. Under the situation of system states being unavailable, the system output is used to set up the state observer to estimate the real system states. Furthermore, the estimation states are employed to design controller. During the control design process, fuzzy logic systems (FLSs) are used to model the unknown nonlinearities. A novel observer-based finite-time tracking control scheme is proposed via fuzzy adaptive backstepping and barrier Lyapunov function approach. The suggested fuzzy adaptive output feedback controller can force the output tracking error to meet the pre-specified accuracy in a fixed time. Meanwhile, all the closed-loop variables are bounded. Compared to some existing finite-time output feedback control schemes, the developed control strategy guarantees that the settling time and the error accuracy are independent of the uncertainties and can be specified by the designer. At last, the effectiveness and feasibility of the proposed control scheme are demonstrated by two simulation examples.  相似文献   

4.
The incessant swell in size, complexity, nonlinearity and structural variations in modern electric power systems, as well as rise in power demand has entailed the use of intelligent control strategies for the real-time satisfactory operation of power system. Hence, in this paper, a novel fuzzy PID with filter plus double integral (FPIDF-II) controller is proposed for automatic generation control (AGC) of two-area interconnected power systems. Initially, a well accepted two-area non-reheat thermal system is considered and the output scaling factors (SF) of FPIDF-II controller are optimized using imperialist competitive algorithm (ICA) employing an integral squared error (ISE) criterion. The supremacy of the proposed approach is demonstrated by contrasting the results with recently published optimal and various modern heuristic optimization techniques based controllers. To demonstrate the efficacy and scalability of the approach over other prevalent intelligent control techniques, the study is further extended to two-area non-reheat thermal system with governor deadband nonlinearity, two-area reheat thermal system, recently appeared two-area photovoltaic (PV)-reheat thermal system and two-area multi-source hydrothermal system. Finally, a sensitivity analysis is carried out to demonstrate the robustness of the proposed controller under broad variations in the system parameters from their nominal values.  相似文献   

5.
A new and systematic method to design digital controllers for uncertain chaotic systems with structured uncertainties is presented in this paper. Takagi-Sugeno (TS) fuzzy model is used to model the chaotic dynamic system, while the uncertainties are decomposed such that the uncertain chaotic system can be rewritten as a set of local linear models with an additional disturbed input. Conventional control techniques are utilized to develop the continuous-time controllers first. Then, the digital controllers are obtained as the digital redesign of the continuous-time controllers using the state-matching approach. The performance of the proposed controller design is illustrated through numerical examples.  相似文献   

6.
In the present study, a novel technique is suggested for the adaptive non-linear model predictive control based on the fuzzy approach in three stages. In the presented approach, in the first stage, the prediction and control horizons are obtained from a fuzzy system in each control step. Another fuzzy system is employed to determine the weight factors before the optimization stage of developing new controller. The proposed controller gives the parameters of the model predictive control (MPC) in each control step in order to improve the performance of nonlinear systems. The proposed control scheme is compared with the traditional MPC and Generic Model Control for controlling MED-TVC process. The performances of the three proposed controllers have been investigated in the absence and presence of disturbance in order to evaluate the stability and robustness of the proposed controllers. The results reveal that the novel adaptive controller based on fuzzy approach performs better than the two other controllers in set-point tracking and disturbance rejection with lower IAE criteria. In addition, the average computational time for the adaptive MPC exhibits a decline of 34% in comparison with the traditional MPC.  相似文献   

7.
This paper addresses the adaptive fuzzy event-triggered control (ETC) problem for a class of nonlinear uncertain systems with unknown nonlinear functions. A novel ETC approach that exhibits a combinational triggering (CT) behavior is proposed to update the controller and fuzzy weight vectors, achieving the non-periodic control input signals for nonlinear systems. A CT-based fuzzy adaptive observer is firstly constructed to estimate the unmeasurable states. Based on this, an output feedback ETC is proposed following the backstepping and error transformation methods, which ensures the prescribed dynamic tracking (PDT) performance. The PDT performance indicates that the transient bounds, over-shooting and ultimate values of tracking errors are fully determined by the control parameters and functions chosen by users. The closed-loop stability is guaranteed under the framework of impulsive dynamic system. Besides, the Zeno phenomenon is circumvented. The theoretical analysis indicates that the proposed scheme guarantees control performance while considerably reducing the communication resource utilization and controller updating frequency. Finally, the numerical simulations are conducted to verify the theoretical findings.  相似文献   

8.
In this paper, an adaptive fuzzy decentralized control method is proposed for accommodating actuator faults for a class of uncertain nonlinear large-scale systems. The considered faults are modeled as both loss of effectiveness and lock-in-place. With the help of fuzzy logic systems to approximate the unknown nonlinear functions, the novel adaptive fuzzy faults-tolerant decentralized controllers are constructed by combining the backstepping technique and the dynamic surface control (DSC) approach. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop systems are bounded and the tracking errors converge to a small neighborhood of zero. Simulation results are provided to show the effectiveness of the control approach.  相似文献   

9.
This study carries out the problem of adaptive backstepping fuzzy tracking control for a class of full state constrained uncertain nonlinear system with unknown control directions. Based on Nussbaum-type functions and tan-type Barrier Lyapunov functions, a novel adaptive fuzzy tracking controller is proposed to guarantee that the system output tracking error asymptotically converges to zero, while the constraints on the states of system will not be violated during operation. Compared with the existing results, a better convergence effect is obtained for this class of systems. Stability analysis of the proposed closed-loop control system is supported by the Lyapunov stability theory. Finally, a simulation example is presented to illustrate the effectiveness of the proposed control strategy.  相似文献   

10.
Due to the unknown system structure of the froth flotation process and frequent fluctuations in production conditions, design of control strategy is a challenging problem. As a result, manual operation is still widely applied in practice by observing froth image features. However, since the manual observation is subjective and the production conditions are time-varying, the manual operation cannot make decisions quickly and accurately. In this paper, a data-driven-based adaptive fuzzy neural network control strategy is developed to implement the automatic control of the antimony flotation process. The strategy is composed of fuzzy neural network (FNN) controllers, a data-driven model, and an on-line adaptive algorithm. The FNN is constructed to derive the control laws of the reagent dosages. The parameters of the FNN controllers are tuned by gradient descent algorithm. To obtain the real-time error feedback information, the data-driven model is established, which integrates the long short term memory (LSTM) network and radial basis function neural network (RBFNN). The LSTM network is utilized as a primary model, and the RBFNN is used as an error compensation model. To handle the challenges of the frequent fluctuations in the production conditions, the on-line adaptive algorithm is proposed to tune the parameters of the FNN controllers. Simulations and experiments are carried out in a real-world antimony flotation plant in China. The results demonstrate that the proposed adaptive fuzzy neural network control strategy produces better control performance than the other two existing methods.  相似文献   

11.
This paper investigates the tracking control problem for output constrained stochastic nonlinear systems under quantized input. The main challenge of considering such dynamics lies in the fact that theirs have both input and output constraints, making the standard backstepping technique fail. To address this challenge, the introduction of nonlinear mapping transforms the constrained nonlinear systems into unconstrained nonlinear systems, which not only avoids the emergence of feasibility conditions but also simplifies the structure of designed controller. The obstacle caused by quantized input is successfully resolved by exploiting the decomposition of hysteresis quantizer. Additionally, the uncertain nonlinearities are approximated by fuzzy logic systems during the control design process. Under the proposed quantized tracking control scheme, the output tracking error converges to an arbitrarily small neighborhood of origin and all signals in the closed-loop system remain bounded in probability. Simultaneously, it can make sure that the output constraint isn’t violated. Ultimately, both a numerical example and a practical example are provided to clarify the effectiveness of the control strategy.  相似文献   

12.
This paper proposes a robust adaptive control strategy for a class of state-constrained uncertain nonlinear systems with prescribed transient and steady-state behavior. The prescribed tracking performance can be characterized by constraints on an output tracking error. Both state and output constraints are achieved by bounding integral barrier Lyapunov functions in the backstepping procedure. A robust adaptive term is designed to compress auxiliary system uncertainties without the knowledge of their bounds. The satisfaction of control constraints and tracking error convergence are verified by theoretical analysis and are illustrated by simulation results.  相似文献   

13.
In the rapidly expanding size and complexity of the electricity network, automatic generation control (AGC) is contemplated to be the most remarkable option for offering good quality electric power supply to the end users. An AGC system entails highly vigorous, competent and intelligent control technique to deliver a healthy power under stochastic nature of consumers’ power demand. Hence, in this paper, a hybrid fuzzy fractional order proportional integral-fractional order proportional derivative (FFOPI-FOPD) controller is proposed as a new expert control technique to tackle AGC profitably in isolated and interconnected multi-area power systems. A recently developed imperialist competitive algorithm (ICA) is utilized for the optimization of the output gains (KP/KP1/KI/KD) and other parameters such as order of integrator (λ) and differentiator (μ) of FFOPI-FOPD controller exercising integral of squared error criterion. The proposed technique is firstly implemented on 1-area thermal system, then to express its potential and extensibility, the work is extended to 2-area hydro-thermal and 3-area thermal power systems widespread in the literature. The eminence of the method is betokened by comparing the results with the various newly published control methodologies and FPI/FFOPI controller designed in the study via ICA in terms of minimum values of various error criteria and undershoots/overshoots/settling times of frequency and tie-line power deviations following a sudden load demand in an area. The sensitivity analysis substantiates that the suggested controller is robust and performs staunchly under the wide variations in the system parameters, random load pattern and in the company of physical constraints to produce more clean electricity.  相似文献   

14.
In this paper a tuning procedure is proposed for event based PI controllers with Regular Quantization with Hysteresis (RQH) sampling law. The RQH is a generalization of Symmetric Send on Delta (SSOD) strategy which decreases the robustness requirements to avoid limit cycle oscillations and reduce the number of events needed for control, improving the overall performance of PI controllers in networked control systems. The tuning procedure takes into account not only classical robustness margins but also takes advantage of some specific robustness measures to avoid limit cycle oscillations induced by the sampler. As the robustness analysis depends on the Describing Function (DF) method, a study evaluating the effect of high order harmonics is provided, showing the validity of the tuning procedure. Some examples are included in which the usefulness of the tuning procedure is shown.  相似文献   

15.
In this paper, an adaptive finite-time funnel control for non-affine strict-feedback nonlinear systems preceded by unknown non-smooth input nonlinearities is proposed. The input nonlinearities include backlash-like hysteresis and dead-zone. Unknown nonlinear functions are handled using fuzzy logic systems (FLS), based on the universal approximation theorem. An improved funnel error surface is utilized to guarantee the steady-state and transient predetermined performances while the differentiability problem in the controller design is averted. Using the Lyapunov approach, all the adaptive laws are extracted. In addition, an adaptive continuous robust term is added to the control input to relax the assumption of knowing the bounds of uncertainties. All the signals in the closed-loop system are shown to be semi-globally practically finite-time bounded with predetermined performance for output tracking error. Finally, comparative numerical and practical examples are provided to authenticate the efficacy and applicability of the proposed scheme.  相似文献   

16.
In this paper, an adaptive output feedback fault tolerant control (FTC) based on actuator switching is proposed for a class of single-input single-output (SISO) nonlinear systems with uncertain parameters and possible actuator failures, for which a set of healthy actuators are available as backups. While high-gain K-filters are utilized to estimate the unmeasured states, an adaptive control law is designed to compensate for the parameter uncertainties and certain actuator failures, an actuator switching strategy based on a set of appropriately designed monitoring functions (MFs) is proposed to tackle those serious actuator failures, make tracking error satisfy prescribed transient and steady-state performance and guarantee closed-loop signal boundedness.  相似文献   

17.
In this paper, an iterative learning control strategy is presented for a class of nonlinear pure-feedback systems with initial state error using fuzzy logic system. The proposed control scheme utilizes fuzzy logic systems to learn the behavior of the unknown plant dynamics. Filtered signals are employed to circumvent algebraic loop problems encountered in the implementation of the existing controllers. Backstepping design technique is applied to deal with system dynamics. Based on the Lyapunov-like synthesis, we show that all signals in the closed-loop system remain bounded over a pre-specified time interval [0,T]. There even exist initial state errors, the norm of tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity and the learning speed can be easily improved if the learning gain is large enough. A time-varying boundary layer is introduced to solve the problem of initial state error. A typical series is introduced in order to deal with the unknown bound of the approximation errors. Finally, two simulation examples show the feasibility and effectiveness of the approach.  相似文献   

18.
非线性不确定系统的模糊自适应 输出反馈跟踪   总被引：2，自引：0，他引：2  
本文研究了非仿射非线性系统的模糊自适应 输出反馈跟踪。在非仿射非线性模型存在不确定的情况下，使用模糊自适应控制器对系统进行控制，并基于Lyapunov稳定性定理得出自适应律。通过解一个代数Riccati方程实现了 跟踪性能。估计状态通过引入高增益观测器得到，实现了系统的输出反馈控制。最后，通过对一个数值例子的仿真验证了算法的有效性。  相似文献   

19.
This paper investigates the adaptive fuzzy output feedback fault-tolerant tracking control problem for a class of switched uncertain nonlinear systems with unknown sensor faults. In this paper, since the sensor may suffer from an unknown constant loss scaling failure, only actual output can be used for feedback design. A failure factor is employed to represent the loss of effectiveness faults. Then, an adaptive estimation coefficient is introduced to estimate the failure factor, and a state observer based on the actual output is constructed to estimate the system states. Fuzzy logic systems are used to approximate the unknown nonlinear functions. Based on the Lyapunov function method and the backstepping technique, the proposed control scheme with average dwell time constraints can guarantee that all states of the closed-loop system are bounded and the tracking error can converge to a small neighborhood of zero. Finally, two simulation examples are given to illustrate the effectiveness of the proposed scheme.  相似文献   

20.
In this study, we addressed the problem of design of high-performance tracking controller for uncertain systems described by the Euler–Lagrange formulation. The main objective was to combine the advantages of the robust integral of the sign of the error (RISE) controller with those of the prescribed performance (PP) controller. In particular, we aimed to obtain asymptotic tracking for the uncertain systems through a continuous control command while ensuring the transient performance. Two controllers were developed. First, the PP property was injected into the RISE controller assuming no constraint on the actuation amplitude existed, and then this property was incorporated into the saturated RISE controller. The performance of the proposed controllers was validated through experimental and simulation tests.  相似文献   

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