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Nowadays, the power systems are getting more and more complicated because of the delays introduced by the communication networks. The existence of the delays usually leads to the degradation and/or instability of power system performance. On account of this point, the traditional load frequency control (LFC) approach for power system sketches a destabilizing impact and an unacceptable system performance. Therefore, this paper proposes a new LFC based on adaptive integral second-order sliding mode control (AISOSMC) approach for the large-scale power system with communication delays (LSPSwCD). First, a new linear matrix inequality is derived to ensure the stability of whole power systems using Lyapunov stability theory. Second, an AISOSMC law is designed to ensure the finite time reachability of the system states. To the best of our knowledge, this is the first time the AISOSMC is designed for LFC of the LSPSwCD. In addition, the report of testing results presents that the suggested LFC based on AISOSMC can not only decrease effectively the frequency variation but also make successfully less in mount of power oscillation/fluctuation in tie-line exchange.

A model predictive controller to perform a lane-merging task is proposed. We consider a hierarchical control structure that consists of an inner and outer control loop. The inner loop is an adaptiv...

In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the multi-area power system (MAPS) with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the linear matrix inequality (LMI) technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.

A typical driver spends a lot of the driving time on roads shared with pedestrians and bicyclists. Unlike highway driving, when there are pedestrians and cyclists using the same space as cars, controlling the car is more complicated. This is due to the fact that the behaviors of such agents does not follow strict rules like the cars in a closed highway. Their trajectories can be expressed better with multiple probabilistic functions than deterministic ones. We suggest a scenario-based stochastic model predictive control (MPC) framework to handle this. We consider multiple pedestrian trajectories with their respective probabilities according to an Interacting Multiple-Model Kalman Filter (IMM-KF). The car dynamics and non linear constraints are considered to avoid collision. A sample-based method is used to solve this optimization problem. The control situation was simulated using MATLAB. The proposed controller is observed to give a very natural control behavior for shared road driving compared to a deterministic single scenario MPC.

To provide a more practical method of controlling the frequency and tie-line power flow of a multi-area interconnected power system (MAIPS), a state observer based on sliding mode control (SOboSMC) acting under a second-order time derivative is proposed. The proposed design is used to study load frequency control against load disturbance, matched and mismatched uncertainty and parameter measurement difficulties of power systems that exist in the modern power plant, such as multi-area systems integrated with wind plants. Firstly, the state observer is designed to optimally estimate system state variables. The estimated states are applied to construct the model of the MAIPS. Secondly, a SOboSMC is designed with an integral switching surface acting on the second-order time derivative to forcefully drive the dynamic errors to zero and eliminate chattering, which can occur in the first-order approach to sliding mode control. In addition, the stability of the total power system is demonstrated with the Lyapunov stability theory based on a new linear matrix inequality (LMI) technique. To extend the validation of the proposed design control for practical purposes, it was tested in a New England system with 39 bus power against random load disturbances. The simulation results confirm the superiority of the proposed SOboSMC over other recent controllers with respect to overshoot and settling time.

This paper centers on the design of highly robust observer sliding mode (HROSM)-based load frequency and tie-power control to compensate for primary frequency control of multi-area interconnected power systems integrated with renewable power generation. At first, the power system with external disturbance is model in the state space form. Then the state observer is used to estimate the system states which are difficult or expensive to measure. Secondly, the sliding mode control (SMC) is designed with a new single phase sliding surface (SPSS). In addition, the whole system asymptotic stability is proven with Lyapunov stability theory based on the linear matrix inequality (LMI) technique. The new SPSS without reaching time guarantees rapid convergence of high transient frequency, tie-power change as well as reduces chattering without loss of accuracies. Therefore, the superiority of modern state-of-the-art SMC-based frequency controllers relies on good practical application. The experimental simulation results on large interconnected power systems show good performance and high robustness against external disturbances when compared with some modern state of art controllers in terms of overshoots and settling time.

The implementation of the sliding mode control (SMC) for load frequency control of power networks becomes difficult due to the chattering phenomenon of high-frequency switching. This chattering problem in SMC is extremely dangerous for actuators used in power systems. In this paper, a continuous control strategy by combining a second-order mode and integral siding surface is proposed as a possible solution to this problem. The proposed second-order integral sliding mode control (SOISMC) law not only rejects chattering phenomenon in control input, but also guarantees the robustness of the multi-area power network, which has an effect on parametric uncertainties such as the load variations and the matched or mismatched parameter uncertainties. Moreover, the reporting of the simulation indicates that the proposed controller upholds the quality requirement by controlling with operating conditions in the larger range, rejects disturbance, reduces the transient response of frequency, eliminates the overshoot problem, and can better address load uncertainties compared to several previous control methods. The simulation results also show that the proposed SOISMC can be used for practical multi-area power network to lessen high parameter uncertainties and load disturbances.

One of the difficult tasks associated with driving in suburban or urban roads is the interactions with pedestrians. One often finds it hard to judge the chances of a pedestrian or bicycle suddenly turning onto the driving path. These leads to a natural slow down response by the drivers. Since these responses are based on the risk feeling of driver, they are probabilistic in nature. This study shares a scenario-based model predictive control algorithm considering probabilistic constraint (SMPC-P) to handle such pedestrian interactions. An Interacting Multiple-Model Kalman Filter (IMM-KF) is used to predict the pedestrian path as multiple trajectories of independent probabilities. The task is formulated into a nonlinear MPC problem. We use a non-linear optimization solver named Interior Point OPTimizer(IPOPT). We introduce a modified form of inverse square root unit function to represent the collision probability into a deterministic function that is compatible with IPOPT. Having simulated it in MATLAB, the controller gives a very natural control behaviour for shared road driving compared to single scenario deterministic MPC.

This research presents a nonlinear gain-scheduled flight controller design method via a stable manifold theory in order to handle the nonlinearities of the F-18 High Alpha Research Vehicle due to the change in the aerodynamic characteristics at different angles of attack and the airspeed variation. The designed longitudinal flight control system consists of a nonlinear gain-scheduled stabilization augmentation system which is designed using the stable manifold method, and a linear gain-scheduled control augmentation system which consists of proportional and integral gains. The nonlinear longitudinal flight controller is verified in a 6 degree-of-freedom simulator.

In this article, a sliding mode control (SMC) is proposed to deal with the frequency deviation problem in interconnected time-delay power systems (ITDPS) with two source power generations. First, the proportional and integral switching surface is used for each area to guarantee the frequency deviation reach zero in normal operating conditions. Then, the stability of the system is ensured with a new Linear Matrix Inequality (LMI) via Lyapunov stability theory. In addition, the SMC law is designed to guarantee the finite time eachability of the system. Finally, impacts of certain physical constraints affecting dynamic performance of the power network such as time-delay is proposed to consider the signal delay in the controller. Effectiveness of the suggested method is validated by simulation studies on the load frequency control under time-delays in the two-area, the step load disturbance and the mismatched uncertainty.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

In this letter, based on the theory developed, a nonlinear optimal controller is designed via stable manifold theory making use of Lagrange multipliers to take into account a class of system constraints. A feature of this method is that a discontinuous Hamiltonian system has to be solved. The existence and uniqueness of solutions of the Hamiltonian system are proven in this letter with a discussion on the stability of the designed controller. The effectiveness of the method is experimentally validated on a Quanser magnetic levitation system. The result shows that the designed nonlinear controller is able to handle the acceleration constraints with better control performance compared with the linear optimal one.

This paper proposes a controller design method for preventing pilot-induced-oscillation (PIO) based on nonlinear optimal output regulation theory and center stable manifold method. The type of PIO considered is due to actuator rate limiting and the proposed controller assures C ⁎ handling quality by the optimal control when actuator works in the linear region. The simulation result shows that the proposed nonlinear controller has better tracking performance in comparison with the linear optimal output regulation controller. The robustness of the designed controller against parametrization error, noise and time-delay is also verified.

A successful fault detection and diagnosis (FDD) strategy for chillers is highly dependent on three core aspects, an accurate reference model, a sensitive fault detection technique and an effective fault identification method. An accurate model plays an important role in the performance of chiller FDD strategy. In this study, the accuracies of three models, MLR (multiple linear regression), KRG (Kriging) and RBF (radial basis function), are compared under various modeling criteria. The EWMA (exponentially-weighted moving average) residual control chart, which is a more powerful tool in detecting small shifts than t-statistics based method, is adopted as a sensitive fault detection technique. The most sensitive feature parameter method is improved to effectively identify faults. Seven common faults in typical centrifugal chillers are considered. Three strategies (MLR-EWMA, KRG-EWMA and RBF-EWMA), which are the combinations of three reference models with EWMA and the most sensitive feature parameter method, are validated and compared by using two sample datasets with different sizes from ASHRAE RP-1043. Through the comparisons of diagnosis performance, the most accurate chiller reference model is found and suggested to detect and diagnose faults in chiller systems. The comparison results show that the RBF-based strategy has the best diagnosis performances among the three strategies.

Development of the fault detection and diagnosis (FDD) for chiller systems is very important for improving the equipment reliability and saving energy consumption. The results of FDD performance are strongly dependent on the accuracy of chiller models. Since the accuracy of the chiller models depends on the indefinite model parameters which are normally chosen by experiments or experiences, an accurate chiller model is difficult to build. Therefore, optimization of model parameters is very useful to increase the accuracy of chiller models. This paper presents a new FDD strategy for centrifugal chillers of building air-conditioning systems, which is the combination between the nonlinear least squares support vector regression (LSSVR) based on the differential evolution (DE) algorithm and the exponentially weighted moving average (EWMA) control charts. In this strategy, the nonlinear LSSVR, which is a reformulation of SVR model with better generalization performances, is adopted to develop the reference feature parameter models in a typical non-linear chiller system. The DE algorithm which is a real-coding optimal algorithm with powerful global searching capacity is employed to enhance the accuracy of LSSVR models. The exponentially weighted moving average (EWMA) control charts are introduced to improve the fault detection capability as well as to reduce the Type II errors in a t-statistics-based way. Six typical faults of the chiller from the real-time experimental data of ASHRAE RP-1043 project are chosen to validate proposed FDD methods. Comprehensive comparisons between the proposed method and two similarly previous studies are performed. The comparison results show that the proposed method has achieved significant improvement in accuracy and reliability, especially at low severity levels. The proposed DE-LSSVR-EWMA strategy is robust for fault detection and diagnosis in centrifugal chiller systems.

This paper proposes a model predictive controller to perform the lane merging task. We consider a hierarchical control structure which consists of an inner control loop and an outer one. The inner loop is an adaptive cruise controller which gives the acceleration command to the autonomous vehicle to follow the designated speed and keep a relative distance with the preceding vehicle while satisfying constraints. The outer loop is to determine the vehicle in the main lane that the autonomous one should follow to minimize the entropy in the decision making of the human drivers in the main lane. It is verified in the simulation that the proposed controller can complete the lane merging task under uncertainties and modeling errors.

This paper presents a detailed aerodynamic modeling technique along with a fuzzy switching multi-model guidance and control strategy for a custom blended wing-body tilt-rotor unmanned aerial vehicle (UAV). The tilt-rotor configuration affects the overall aerodynamic characteristics significantly and hence, a comprehensive mathematical model is required. Thus, after a configuration selection and design process, a 6DOF mathematical model including a thrust model, propeller effects on the wings and free-stream aerodynamics is developed in this work. Due to a higher tendency of inherently unstable motion with increasing rotor angles and rapidly changing aerodynamic characteristics, multiple models are generated to cover the whole tilting regime. For the control of tilting motion, gain-scheduled flight controllers are preferred. While switching between the models, a fuzzy logic based algorithm is employed. The proposed technique is able to successfully control the aircraft for a full flight envelope from hover to landing. It is also evident from the results that, in the same tilting duration, the fuzzy switching algorithm helps the UAV to show a superior tracking performance when compared to the linear switching technique at a fixed tilting rate.

This research develops a control design methodology that combines the analytical method and randomized model predictive control one. By using the analytical approach, one can consider the optimization in the infinite time horizon and propose several nonlinear optimal control input candidates. Then, by using the randomized model predictive control method, one can determine the "probably true" optimal one. The effectiveness and robustness of the proposed method are verified in simulations.

Software-Defined Networking (SDN) has emerged as a novel network architecture for facilitating and simplifying network control and management. The main fundamental of SDN is the separation of the control and data planes that allows to rapidly, simply manage and configure network operations. However, because of the logically centralized control plane, SDN brings many security challenges, especially to be the victim of Controller-aimed Distributed Denial of Service (DDoS) attacks. In this paper, we proposed a solution to detect and mitigate this dangerous threat to protect SDN controllers. Our proposal consists of two components including a network application for supporting in decision making and a network function for enforcing the detection and mitigation tasks. We also contribute a multi-layer attack detection mechanism and a three-phase mitigation approach to treat with the attacks. Our methodology is implemented on OpenDayLight controller and evaluated using a high-speed test-bed network. The results show that our solution is able to detect attacks after 40 milliseconds on average, and the accuracy of detection process is around 95%. Moreover, it also can effectively, efficiently mitigate attacks to reduce CPU Utilization from high (approx. 90%) to remain average (approx. 20%).

This study investigates load frequency control based generalized extended state observer (GESO) for interconnected power systems subject to multi-kind of the power plant. First, the mathematical model of the interconnected power system is proposed based on the dynamic model of thermal power plant with reheat turbine and hydropower plant. Second, the GESO is designed to estimate the system states and disturbances. In addition, the problem of unmeasurable system states in the interconnected power network due to lack of sensor has been solved by using the proposed load frequency control based GESO. The numerical experiments are carried out by using MATLAB/ SIMULINK simulation. The simulation results point out that the proposed control approach has the capacity to handle the uncertainties and disturbances in the interconnected power system with better transient performances in comparison with the existing control approach. The relevant dynamic models have already been used for the simulation of the physical constraints of the governor dead band (GDB) and generation rate constraint (GRC) effect in the power plants. It is evident that the robustness of the suggested controller in terms of stability and effectiveness of the system. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

In multi-area interconnected power systems (MAIPS), the measurement of all system states is difficult due to the lack of a sensor or the fact that it is expensive to measure. In order to solve this limitation, a new load frequency controller based on the second-order sliding mode is designed for MAIPS where the estimated state variable is used fully in the sliding surface and controller. Firstly, a model of MAIPS integrated with disturbance is introduced. Secondly, an observer has been designed and used to estimate the unmeasured variables with disturbance. Thirdly, a new second-order sliding mode control (SOSMC) law is used to reduce the chattering in the system dynamics where slide surface and sliding mode controller are designed based on system states observer. The stability of the whole system is guaranteed via the Lyapunov theory. Even though state variables are not measured, the experimental simulation results show that the frequency remains in the nominal range under load disturbances, matched and mismatched uncertainties of the MAIPS. A comparison to other controllers illustrates the superiority of the highlighted controller designed in this paper.

In this paper, we propose a general framework for constrained optimal control based on stable manifold method. We design a nonlinear optimal controller via stable manifold approach introducing Lagrange multiplier to take into account various constraints such as control input magnitude as well as control input-state constraints. The effectiveness of the method is verified by designing a stabilizing controller for a control moment gyroscope apparatus. The result shows that the designed controller can effectively handle the nonlinearities due to constraints.