Mohammad Hassan Khooban

Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties

This paper proposes a simple fuzzy sliding mode control to achieve the best trajectory tracking for the robot manipulator. In the core of the proposed method, by applying the feedback linearization technique, the known dynamics of the robots manipulator is removed; then, in order to overcome the remaining uncertainties, a classic sliding mode control is designed. Afterward, by applying the TS fuzzy model, the classic sliding mode controller is converted to fuzzy sliding mode controller with very simple rule base. The mathematical analysis shows that the robot manipulator with the new proposed control in tracking the robot manipulator in presence of uncertainties has the globally asymptotic stability. Finally, to show the performance of the proposed method, the controller is simulated on a robot manipulator with two degrees of freedom as case study of the research. Simulation results demonstrate the superiority of the proposed control scheme in presence of the structured and unstructured uncertainties.

Adaptive fuzzy sliding mode control for synchronization of uncertain non-identical chaotic systems using bacterial foraging optimization

This paper introduces a novel hybrid control strategy, namely an optimal adaptive fuzzy sliding mode OAFSM control scheme, to realize the synchronization of general uncertain chaotic systems in master-slave configuration. The proposed controller not only guaranties the stability and robustness against the lumped uncertainties caused by unmodeled dynamics and external disturbances, but also significantly reduces the control chattering inherent in conventional sliding mode control. The chaos synchronization is obtained by optimal proper choice of the control parameters including the sliding surface and the reaching law parameters. To achieve this, a bio-mimetic algorithm namely bacterial foraging optimization algorithm BFOA is employed. An illustrative example is given to demonstrate the validity and confirm the performance of the proposed scheme.

T–S fuzzy model predictive speed control of electrical vehicles

This paper proposes a novel nonlinear model predictive controller (MPC) in terms of linear matrix inequalities (LMIs). The proposed MPC is based on Takagi-Sugeno (TS) fuzzy model, a non-parallel distributed compensation (non-PDC) fuzzy controller and a non-quadratic Lyapunov function (NQLF). Utilizing the non-PDC controller together with the Lyapunov theorem guarantees the stabilization issue of this MPC. In this approach, at each sampling time a quadratic cost function with an infinite prediction and control horizon is minimized such that constraints on the control input Euclidean norm are satisfied. To show the merits of the proposed approach, a nonlinear electric vehicle (EV) system with parameter uncertainty is considered as a case study. Indeed, the main goal of this study is to force the speed of EV to track a desired value. The experimental data, a new European driving cycle (NEDC), is used in order to examine the performance of the proposed controller. First, the equivalent TS model of the original nonlinear system is derived. After that, in order to evaluate the proficiency of the proposed controller, the achieved results of the proposed approach are compared with those of the conventional MPC controller and the optimal Fuzzy PI controller (OFPI), which are the latest research on the problem in hand.

A new and robust control strategy for a class of nonlinear power systems: Adaptive general type-II fuzzy

In this article, a robust direct adaptive general type-2 fuzzy logic controller is introduced for a class of nonlinear power systems. The proposed controller uses the advantages of general type-2 fuzzy logic system in handling dynamic uncertainties to approximate unknown nonlinear actions. Implementing general type-2 fuzzy system is computationally costly; however, by using a recently introduced α-plane representation, general type-2 fuzzy logic system can be seen as a composition of several interval type-2 fuzzy logic systems with a corresponding level of α for each. Linguistic rules are directly incorporated into the controller. In addition, an H ∞ compensator is corrupted to attenuate external disturbance and fuzzy approximation error. General type-2 fuzzy adaptation laws are also derived using Lyapunov approach. It is worth noting that mathematical analysis proves the stability of the closed-loop system. In order to evaluate the performance of the proposed controller, the results are compared with those obtained by direct adaptive type-1 fuzzy logic controller, a direct adaptive interval type-2 fuzzy logic controller and adaptive proportional–integral–derivative, which are the latest researches in the problem in hand. The proposed controller is applied to a chaotic power system as a case study. Simulation reveals the effectiveness of the proposed controller in presence of dynamic uncertainties and external disturbances.

Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network

In restructured markets where transactions process is competitive, forecasting of electricity price is inevitably an important available tool for market participants. Due to the sensitivity of forecasting issues in markets performance, and high prediction error resulted from the behavior of price series, nowadays probabilistic forecasting highly attracted participants’ attention. In this paper, a probabilistic approach for the hourly electricity price forecasting is presented. In the proposed method, the uncertainty of predictor model is considered as the uncertainty factor. The bootstrapping technique is used to implement the uncertainty and since the method is needed to be fast and of low computational cost in the daily forecasting, a generalized learning method is applied, which has high accuracy and speed. This newly presented learning method is based on generalized extreme learning machine approach to be used for improved wavelet neural networks. Also in order to reach more accommodation, the predictor model with the changes of price time series, the wavelet preprocessing is used. Effective performance of the proposed model is validated by testing on data of Ontario and Australian electricity markets.

LMI-based stability analysis and robust controller design for a class of nonlinear chaotic power systems

Abstract This paper proposes novel linear matrix inequality (LMI) stability analysis and controller design conditions for nonlinear chaotic power systems. The proposed approach is based on the non-quadratic Lyapunov function (NQLF), non-parallel distributed compensation (non-PDC) schematic and Takagi–Sugeno (TS) fuzzy modeling. Utilizing NQLF causes membership functions (MFs) and their time derivative to appear in the design conditions. To solve this problem, an augmented state vector is proposed which results in removing the MFs and their time derivatives from the design conditions. Moreover, structural constraints on Lyapunov matrices are eliminated. The proposed approach provides relaxed stability analysis and controller design conditions due to the framework that is considered during the formulation derivation. Finally, two practical power systems that exhibit chaotic behaviors are considered to evaluate the proposed approach. Simulation results show advantages of the proposed method compared to the recently published works.

A robust adaptive load frequency control for micro-grids

The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α-plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller.

Adaptive PI controller to voltage regulation in power systems: STATCOM as a case study

Static synchronous compensator (STATCOM) provides the means to improve quality and reliability of a power system as it has the functional capability to handle dynamic disturbances, such as transient stability and power oscillation damping as well as to providing voltage regulation. In this paper, a robust adaptive PI-based optimal fuzzy control strategy is proposed to control a STATCOM used in distribution systems. The proposed intelligent strategy is based on a combination of a new General Type-II Fuzzy Logic (GT2FL) with a simple heuristic algorithm named Teaching Learning Based Optimization (TLBO) Algorithm. The proposed framework optimally tunes parameters of a Proportional-Integral (PI) controller which, similar to most of other researchers regarding control of STATCOM, are in charge of controlling the device. The proposed controller guaranties robustness and stability against uncertainties caused by external disturbances or ever-changing nature of the power systems. The TLBO optimizes the parameters of the controller as well as the input and output membership functions. To validate the efficiency of the proposed controller, the obtained simulation results are compared with those of the two most recent researches applied in this field, namely, conventional Proportional Integral (PI) controller and Optimal Fuzzy PI (OFPI) controller. Results demonstrate the successfulness and effectiveness of the proposed online-TLBO General Type-2 Fuzzy PI (OGT2FPI) controller and its superiority over conventional approaches.

A time-varying general type-II fuzzy sliding mode controller for a class of nonlinear power systems

In this paper, in order to control a class of nonlinear uncertain power systems, a new simple indirect adaptive general type-II fuzzy sliding mode controller (IDAGT2FSMC) is proposed. For handling dynamic uncertainties, the proposed controller utilizes the advantages of general type-2 fuzzy logic systems (GT2FLS) to approximate unknown nonlinear actions and noisy data. Implementing general type-2 fuzzy systems is computationally costly; therefore, to decrease computational burden, the proposed method uses a recently introduced -plane representation so that GT2FLS can be seen as a composition of several interval type-2 fuzzy logic systems (IT2FLS) with a corresponding level of for each. The globally asymptotic stability of the closed-loop system is mathematically proved. To evaluate the superiority of the proposed controller, performance of the proposed method is compared with those of Indirect Adaptive type-1 Fuzzy Sliding Mode (IDAFSM) controller, Indirect Adaptive Interval Type-II Fuzzy Sliding Mode (IDAT2FSM) controller, conventional Sliding Mode controller (SMC) and PID controller results which are all among the most recent methods applied to the issue in question. Finally, the proposed method is applied to an uncertainly chaotic power system as a case study. Simulation indicates the effectiveness of the proposed controller while facing of dynamic uncertainties and external disturbances.

Probabilistic electricity price forecasting by improved clonal selection algorithm and wavelet preprocessing

Probabilistic forecasting is an appropriate tool that helps electricity markets participants to improve their decision making. Due to changes in electricity prices, the point forecasting accuracy cannot be guaranteed. Hence, participants are more interested in results of probabilistic forecasting methods such as prediction intervals method. In this paper, a hybrid approach for probabilistic electricity price forecasting is presented. This model is based on using improved clonal selection algorithm and extreme learning machine for neural networks training process and wavelet preprocess. The wavelet is utilized to decompose data into well behaved subsets, which increases accuracy of the model. Also, due to the high required computational time for training the neural networks, autocorrelation function is used to reduce the number of neural networks inputs. Finally, in order to evaluate the proposed probabilistic forecasting method, the Ontario and Australian electricity markets data are used.