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This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT. [ABSTRACT FROM AUTHOR]

Highlights • This paper model and discuss the performance of a hybrid energy distributed power system (HEDPS) coordinated with reheat thermal power plant. • Dragonfly algorithm optimized 3DOF-PID controller is proposed to improve the frequency and power profiles of the modeled HEDPS. • Results obtained using Dragonfly algorithm is compared with classical and some recently published evolutionary algorithms. • Time varying step load perturbation, random wind perturbation, and parameter variations are considered to confirm feasibility and superiority of the designed controller. • Probability density function and statistical results are shown to validate the robustness of the proposed method. Abstract The objective of this paper is to study the dynamic stability of a hybrid energy distributed power system (HEDPS) subject to load and wind power variations. A three degree-of-freedom (3-DOF) proportional-integral-derivative (PID) controller is designed and implemented in the HEDPS to stabilize frequency and power fluctuations after the perturbation. For enhancing system dynamics, the parameters of the 3-DOF PID controller are optimized by using dragonfly algorithm (DA). The results are compared with the results obtained by Zeigler–Nichols tuning and some other well-known meta-heuristic algorithms. The efficacy of proposed DA over different reported algorithms is established in terms of convergence rate, minimum fitness value and dynamic performance of the system. The robustness of the 3-DOF PID-controller is ascertained with time-varying step load perturbation, random wind power perturbation, and under system parameter variation. The robust performance of proposed DA has also been established by performing statistical analysis. [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neuro-fuzzy PID controller based on twin delayed deep deterministic policy gradient (TD3) algorithm for nonlinear systems. In this approach, the observation of the environment is embedded with information of a multiple input single output (MISO) fuzzy inference system (FIS) and have a specially defined fuzzy PID controller in neural network (NN) formation acting as the actor in the TD3 algorithm, which achieves automatic tuning of gains of fuzzy PID controller. From the control perspective, the controller combines the merits of both FIS and PID controller and utilizes reinforcement learning algorithm for optimizing parameters. From the reinforcement learning point of view, embedding the prior knowledge into the fuzzy PID controller incorporated in the actor network helps reduce the learning difficulty in the training process. The proposed method was tested on the cart-pole system in simulation environment with comparison of a linear PID controller, which demonstrates the robustness and generalization of the proposed approach. [ABSTRACT FROM AUTHOR]

• A model-free reinforcement learning algorithm for adaptive low-level PID control. • Double Q-learning provides excelent performance with low computational cost. • Incremental state and action spaces discretization for fast reinforcement learning. • The Double Q-PID algorithm is effectively applied to multiple robotic platforms. • The proposed algorithm effectively improves the adaptability of the PID controllers. Many expert systems have been developed for self-adaptive PID controllers of mobile robots. However, the high computational requirements of the expert systems layers, developed for the tuning of the PID controllers, still require previous expert knowledge and high efficiency in algorithmic and software execution for real-time applications. To address these problems, in this paper we propose an expert agent-based system, based on a reinforcement learning agent, for self-adapting multiple low-level PID controllers in mobile robots. For the formulation of the artificial expert agent, we develop an incremental model-free algorithm version of the double Q -Learning algorithm for fast on-line adaptation of multiple low-level PID controllers. Fast learning and high on-line adaptability of the artificial expert agent is achieved by means of a proposed incremental active-learning exploration-exploitation procedure, for a non-uniform state space exploration, along with an experience replay mechanism for multiple value functions updates in the double Q -learning algorithm. A comprehensive comparative simulation study and experiments in a real mobile robot demonstrate the high performance of the proposed algorithm for a real-time simultaneous tuning of multiple adaptive low-level PID controllers of mobile robots in real world conditions. [ABSTRACT FROM AUTHOR]

• Adaptive time-stepping (ATS) control algorithm developed for transient analyses of MSRs. • ATS algorithm with PID controller was implemented into the PROTEUS-NODAL code. • Performance of ATS was tested for rapid, moderate, slow, and localized transients. • Test results show over 20 times speedups for certain transients relative to reference solution. • Set of recommended values of controller parameters for different transients were provided. This paper presents an adaptive time-stepping (ATS) control algorithm developed for transient analyses of Molten Salt Reactors (MSRs) with flowing fuel. To enhance the computational efficiency while maintaining a targeted accuracy, an ATS algorithm with the proportional-integral-derivative (PID) controller and error monitoring was developed and implemented into the PROTEUS-NODAL code. The performance of the ATS algorithm was tested for a wide range of the Molten Salt Fast Reactor (MSFR) numerical benchmark and the Molten Salt Reactor Experiments (MSRE). The test results show over 20 times speedups for very rapid transients and very slow, long-time transients relative to the reference solution obtained with a small time-step size. In addition, extensive parametric studies were performed on the local error estimates and controller parameters and derived a set of recommended values of initial time step, tolerance and gain for four different transient categories. [ABSTRACT FROM AUTHOR]

Highlights • An adaptive Fuzzy Logic PID approach is proposed to control steam turbines. • A CSPP has been modeled focusing on power loop with variable steam conditions. • Design of FPID controller through the knowledge acquired during simulation phase. • FL allows to control steam turbines in off-design points with optimal performance. • Control algorithm with reduced design and implementation time on PLC platforms. Abstract In Concentrated Solar Power Plants, steam turbines controlled with standard Proportional Integrative Derivative (PID) methods may suffer from performance downgrading in power generation when the steam conditions deviate from nominal ones. An enhancement of standard steam turbine controller can be the key to achieve optimal performance also in non-nominal steam conditions. This paper presents the improvement of the PID control concept by exploiting Fuzzy Logic, an artificial intelligence technique that allows taking into account the human experience and knowledge on the system behavior. A real Concentrated Solar Power Plant has been modeled focusing on generated power control loop, its stability and performance analysis, knowledge useful to design a Fuzzy Inference System. A fuzzy logic controller is proposed to continuously adapt the PID parameters, to improve the steam turbine governor action. Its performance is compared to the classical PID tuned according to three different approaches. The fuzzy logic PID controller extends the simplicity of PID and adapts the control action to actual operating condition by providing the system with a sort of “decision-making skill”. The possibility to design implementable algorithms on a Programmable Logic Controller, which have stringent computational speed and memory requirements, has been explicitly taken into account in the developed work, through the minimization of the controller complexity with a reduced number of fuzzy sets and fuzzy rules within the fuzzy inference system. [ABSTRACT FROM AUTHOR]

This paper presents the design of proportional integral derivative (PID) based load frequency control (LFC) scheme effectively optimized through novel combination of bacteria foraging oriented particle swarm optimization (BFO-PSO) technique for a hydro dominating energy system model. The design control are implemented for 1% load disturbance in area-1 and compared with classical PID, Pessen integral rule, some overshoot, no overshoot as well as with recently published bacteria foraging optimization algorithm (BFOA) in terms of computed gains and inverse time multiplied absolute error (ITAE). The system performance for hydro energy system is sluggish and oscillatory. Hence, the further enhancement with proposed design are observed by considering the combination of unified power flow control (UPFC) in series with the tie-line and hydrogen aqua electrolyzer units installed at terminal of area-2. The system performance of the designed control is evaluated under various system investigations considering the dead-band and generation rate constraint (GRC) non-linearity and the applications results are presented to show the superiority of the proposed work. [ABSTRACT FROM AUTHOR]

The nonlinear sliding-mode (SLM) controller based on the maximum jerk and time-optimal step response has been developed for the control of servo-solenoid valves, which exhibits its performance advantages when compared to traditional PID controller. However, the experiments shows the responses of the SLM control under only jerk constraint will largely vary with different damping and loads, leading to the large overshoot and breakdown under some working conditions (e.g., large viscous damping coefficient). Thus, an improved SLM controller is developed in this paper to deal with the existing drawbacks, where a novel nonlinear SLM surface is proposed by taking into account both spool’s acceleration and jerk limitation. Firstly, the nominal model of servo-solenoid valve is built by linearizing the dynamics on the null position, and the system identification is carried out to achieve the model parameters and their variation ranges. Subsequently, the valve constraints under the power limitation are analyzed through the frequency response of the identified model, which comes out the maximum available velocity, acceleration and jerk. Finally, the improved SLM control algorithm is proposed, where the novel SLM surface considers the maximum plus jerk, the maximum minus jerk and the maximum acceleration. Experimental studies are conducted and the results show that the improved SLM controller under both acceleration and jerk constraints can achieve the continuous and stable sliding mode state, realize the time-optimal step response of the valve, and exhibit strong disturbance rejection abilities. [ABSTRACT FROM AUTHOR]

Controller design aspect for automatic generation control (AGC) of multi-area power system is often regarded as an optimization problem with different conflicting objectives. The controller must fulfill these objectives to produce a feasible set of solution as per the requirements of the designer. This paper explores the design and performance analysis of a proportional-integral-derivative (PID) controller employing Jaya algorithm for AGC of an interconnected power system. A filter with derivative term is used with PID controller to minimize the effect of noise in the input signal. The different performance measures considered are integral-time-multiplied-absolute-error (ITAE), the minimization of peak overshoots and the settling times of the deviations in the frequencies and tie-line power. Furthermore, these performance measures i.e. objectives are combined to form a single objective problem using analytic hierarchy process (AHP). Recently proposed Jaya algorithm, which is free from algorithm-specific parameters, is used for minimizing this single objective. The two-area interconnected linear power system model is considered for the simulation process. The simulation studies are carried out under five different cases with diverse sets of disturbances. The efficacy and superiority of the proposed Jaya algorithm based PID controller is shown by comparing simulation results with other algorithms like particle swarm optimization (PSO), differential evolution (DE), Nelder-Mead simplex (NMS), elephant herding optimization (EHO) and teacher learner based optimization (TLBO). Time-domain simulations and statistical analysis are presented to validate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel design method for determining the optimal fuzzy PID-controller parameters of active automobile suspension system using the particle swarm optimization (PSO) reinforcement evolutionary algorithm is presented. This paper demonstrated in detail how to help the PSO with Q-learning cooperation method to search efficiently the optimal fuzzy-PID controller parameters of a suspension system. The design of a fuzzy system can be formulated as a search problem in high-dimensional space where each point represents a rule set, membership functions, and the corresponding system’s behavior. In order to avoid obtaining the local optimum solution, we adopted a pure PSO global exploration method to search fuzzy-PID parameter. Later this paper explored the improved the limitation between suspension and tire deflection in active automobile suspension system with nonlinearity, which needs to be solved ride comfort and road holding ability problems, and so on. These studies presented many ideas to solve these existing problems, but they need much evolution time to obtain the solution. Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance. [Copyright &y& Elsevier]

Voltage and frequency regulation is one of the most vital issues in autonomous microgrids to ensure an acceptable electric power quality supply to customers. In this paper, a real-time control structure including power, voltage, and current control loops is proposed for microgrid inverters to restore voltage and frequency of the system after the initiation and load changes. The Proportional-Integral (PI) gains of the voltage controller are optimized in a real-time basis after a perturbation in the microgrid to have a fast and smooth response and a more stable system. The current controller produces Space Vector Pulse Width Modulation command signals to be fed into the three-leg inverter. The multi-objective optimization problem has objective functions of voltage overshoot/undershoot, rise time, settling time, and Integral Time Absolute Error (ITAE). The modified Multi-Objective Hybrid Big Bang-Bing Crunch (MOHBB-BC) algorithm is employed as one of efficient evolutionary algorithms in order to solve the optimization problem. The MOHBB-BC method obtains a set of Pareto optimal solutions; a fuzzy decision maker is used to pick up the most preferred Pareto solution as the final solution of the problem. Results from testing the control strategy on a case study are discussed and compared with previous works; according to them, the proposed method is able to obtain dynamic PI regulator gains to have a more appropriate response. [ABSTRACT FROM AUTHOR]

Abstract: Model-based predictive control (MPC) is a widely used advanced control algorithm in process industries, such as chemical plants and oil refineries. Quadratic dynamic matrix control (QDMC) is one of the popular MPC strategies. In this paper, the QDMC controller is used to control a simulated reverse-osmosis (RO) water desalination system with spiral wound element (SWM). A cascaded control system was designed with the QDMC controller and a PID controller for the desalination process, where the QDMC controller optimizes the set point of the PID controller and directly controls one output. In order to make the implementation more convenient, this paper also studies the implementation of QDMC control system based on a field programmable gate array (FPGA) chip. A support software is developed to help engineers adjust the parameters of the QDMC controller. Since the QDMC controller is implemented in an embedded system, the overall system cost is reduced, which is helpful to the application of RO desalination system. [Copyright &y& Elsevier]

Abstract: The paper presents a PI tuning strategy for air handler discharge air temperature control in building comfort application. It recommends using a 5min settling time with no overshoot as the design criterion to select PI gain values, and recommends that a PI controller should be retuned when the settling time is larger than 20min. This tuning strategy avoids unnecessary tuning while providing acceptable control performance for building comfort control. The paper further concludes that PI tuning can be done by adjusting proportional gain only and keeping the reset time constant for air handler discharge air temperature control. The proportional gain can be calculated based on the system sum of time constant and delay, and system static gain in typical applications. Inclusion of the time constant in the equation is recommended for uncommon applications where the delay is large. Regression equations are given based on simulation results for both typical applications and uncommon applications. The PI tuning strategy proposed in the paper can be reliably used in PI auto tuning since it uses less model information and adjusts less gain values than existing tuning algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, a simple method to find the minimum output variance (MOV) with proportional–integral-derivative (PID) controller is proposed. An assessment of improved achievable PID performance is important as PID controllers are the most commonly used in industries. The restriction on the controller structure e.g. PID (the case in this paper) results worst output variance compared to minimum variance (MV) benchmark. The problem in assessing the PID-MOV rises a non-convex problem and for non-convex problem, no direct and simple solution is possible. In this paper the non-convex problem is solved with simple ring of iterations. Several simulation examples are incorporated to show the usefulness of proposal algorithm. [Copyright &y& Elsevier]

Abstract: The utilization of motor-in-wheel in the electric vehicle (EV) is a very important research topic. In order to simplify the control of EV, a new system is proposed by Guilin Tao, which changes the traditional configuration and control strategy of direct-wheel-driven EV. In this system two permanent-magnet-brushless dc motors (PMBDCMs) are connected directly in serial, in order to cut down the differential algorithm thoroughly in velocity control strategy. This structure can realize the differential automatically when the EV runs on the smooth road at low speed. But this structure considers the characters of the motor only, which is an ideal system. EV is a complex electromechanical system, and its work condition is also complex. The variation of the external condition and the work condition of operation can affect the operation of motor, so the state of the EV will change. In this paper, according to the change of the work condition and road condition, combined with the dynamics of EV, this problem was well studied. The neural network PID control mode was used in this paper, and by means of the Matlab/Simulink and the experimental verification, it reveals that this method can enhance the safety and stability of this system. [Copyright &y& Elsevier]

Abstract: Evolutionary algorithms (EAs) refer to a broad class of optimization algorithms, which take some inspiration from evolutionary systems in the natural world. In recent years, a new class of EAs called estimation of distribution algorithms (EDAs) has emerged based on probabilistic modeling of the search space. Instances of EDAs include population based incremental learning (PBIL) algorithm. The PBIL algorithm is an easy to understand heuristic optimization technique that is inspired by the genetic algorithm and the competitive learning paradigm. PBIL includes many of the features from the genetic algorithm such as binary string representation, the notion of individuals, fitness measures and mutations. Contrary to the genetic algorithm it does not maintain a population of individuals but instead PBIL contains a probability vector. At each generation a new population of individuals is sampled according to the probabilities specified in the probability vector. The population is evaluated and the probability vector is updated by dragging it towards the best individual in the population. In recent years, the investigation of synchronization of chaotic systems has attracted much attention of researchers. Chaos synchronization has been applied in many fields such as secure communication, chemical, engineering, and biological systems, among others. This paper presents the synchronization of two identical discrete chaotic systems subject the different initial conditions by designing a proportional–integral–derivative (PID) controller. In addition, the tuning of the PID controller based on a modified PBIL algorithm using similarity analysis is also investigated in this paper. Simulation results show the good performance of the modified PBIL algorithm for synchronization of chaotic systems. [Copyright &y& Elsevier]

Abstract: This paper considers the optimal design of mechatronic systems with configuration-dependent dynamics. An optimal mechatronic design requires that, among the structural and control parameters, an optimal choice has to be made with respect to design specifications in the different domains. Two main challenges are treated in this paper: the non-convex nature of the optimization problem and the difficulty in modeling serial machines with flexible components and their embedded controllers. The optimization problem is treated using the direct design strategy which considers simultaneously structural and control parameters as variables and adopts non-convex optimization algorithms. Linear time-invariant and gain-scheduling PID controllers are addressed. This methodology is exploited for the multi-objective optimization of a pick-and-place assembly robot with a gripper carried by a variable-length flexible beam. The resulting design tradeoffs between system accuracy and control efforts demonstrate the advantage of an integrated design approach for mechatronic systems with configuration-dependent dynamics. [Copyright &y& Elsevier]

In modern era, the switched reluctance motors (SRM) are gaining attraction due to its inherent features such as robustness, low cost, simple, rugged structure, excellent fault tolerance and temperature withstanding capability. With these specialized advantages, the accurate and efficient energy analysis is still a challenging one since they operated at variable reluctance and oscillating excitation characteristics resulting in nonlinear characteristics. This paper focused on the energy improvement and performance analysis inclusive of smooth control in speed and minimized torque ripples of SRM by introducing the novel Bio- inspired methodology named Bacterial Foraging Optimization Algorithm (BFOA) for the selection of optimal parameters of the PID speed and current controllers. By minimizing the torque ripples, it increases the average torque which in turn increases the energy conversion. The performance of the SRM is measured in terms of speed, current, power and efficiency and BFOA efficiency has been verified with genetic algorithm (GA) and conventional PID controller using Euler forward approximation method. This paper discusses the modeling, controlling strategy using BFOA, GA and Euler forward approximation method and a comprehensive analysis of energy using optimized selection of controlling parameters. The performance evaluation objectives in this work, is to minimize Integral Square Error of both current and speed controller and also the reduction of torque ripples. The results obtained in this method are also compared with the Genetic Algorithm and Euler forward approximation-based controllers. The proposed algorithm employs individual and social intelligences, which in turn search the responses between the local optima along with global optimums of the problem adaptively. The outmost dynamic response increased average torque and minimized current ripple can be obtained when the parameters of PID controllers are optimized using BFOA. [ABSTRACT FROM AUTHOR]

This paper presents an improved version of the Krill Herd optimization algorithm. The proposed algorithm has been applied to determine coefficients of PID controller to achieve desired system response. For this purpose, a cost function based on weighted sum of step response characteristics is considered to be minimized. Simulation results compare performance of the ICKH algorithm with many other optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel Teaching Learning Based Optimization (TLBO) algorithm with 2-Degree Freedom of Proportional–Integral–Derivative (2-DOF PID) controller is proposed for Automatic Generation Control (AGC). Initially a widely used two area thermal system is considered and the gains of the PI/PID/2-DOF PID controller are optimized employing a TLBO algorithm. The supremacy of the proposed 2-DOF PID controller has been shown by comparing the results with recently published technique such as conventional ZN, GA, BFOA, DE and hBFOA-PSO based PI controllers for the same system. Additionally, the proposed approach is further extended to multi source power system such as thermal, hydro and gas power plant. The advantage of the proposed approach is demonstrated by comparing the results with some recently published approaches. Sensitivity analysis is performed which demonstrates the ability of the proposed approach to wide changes in system parameters. Finally the proposed approach is investigated under random load variation. [ABSTRACT FROM AUTHOR]

BAT algorithm is proposed in this paper for optimal tuning of PI controllers for load frequency controller (LFC) design. The problem of robustly tuning of PI based LFC design is formulated as an optimization problem according to time domain objective function that is solved by BAT algorithm to find the most optimistic results. To demonstrate the effectiveness of the proposed method, a two-area interconnected power system is considered as a tested system. To ensure robustness of the proposed control strategy to stabilize frequency oscillations, the design process takes a wide range of operating conditions and system nonlinearities into account. The simulation results are given to detect the superiority of BAT algorithm over Simulated Annealing (SA) in tuning PI controller parameters through different indices. Results evaluation show that the proposed algorithm achieves good robust performance for wide range of system parameters and load changes compared with SA. [ABSTRACT FROM AUTHOR]

With increasing wind or solar farm and pump storage plant integrations, more and more large-scale renewable energy integration systems are taking shape gradually, which brings up challenges to optimized control these systems. Especially, due to the reversible properties of pumped storage hydro unit (PSHU), it is apt to fall into the ‘S’ characteristic area which can lead to a wide range of unit rotational speed oscillation under no-load condition at middle-low water head. In order to solve these unstable running problems effectively, an adaptively fast fuzzy fractional order PID (AFFFOPID) control method for PSHU is proposed in this paper. A combination of fuzzy logic controller and fractional order PID is adopted by the proposed control method, and error tracking element of PSHU guide vane opening is added to improve the regulation rate. Then, an accurate mathematical model of pump-turbine with complete characteristic curves is established for the PSHU regulating system (PSHURS). Meantime, a novel bacterial-foraging chemotaxis gravitational search algorithm (BCGSA) is used for optimized parameters selection of AFFFOPID method. The BCGSA which is based on the standard gravitational search algorithm accelerates convergence speed with a combination of the P best – G best -guided strategy and adaptive elastic-ball method. Chemotaxis operator of bacterial foraging algorithm is also added into the BCGSA which is devised to step out the local optimal with a certain probability. Furthermore, simulation tests under different running conditions are utilized to demonstrate the feasibility and robustness of the AFFFOPID. The results are analyzed in detail, which demonstrates the pump-turbine model and the proposed AFFFOPID method are practicable. Comparison with other methods clearly shows that AFFFOPID using BCGSA outperforms the others in most cases. [ABSTRACT FROM AUTHOR]

This paper presents a new modified Perturbation and Observation (P&O) maximum power point tracking algorithm with adaptive duty cycle step using PID controller based on genetic algorithm. The classical P&O MPPT algorithm is widely used in several applications due to its simplicity; however, P&O prone to failure especially when high changes in irradiance, oscillation around the MPP and the convergence speed. To face this challenge and in order to overcome the drawbacks of the classical P&O MPPT, a new method based on variable-step size of modified P&O MPPT method using PID controller tuned by genetic algorithm is presented. The efficiency of the proposed method has been studied successfully using a boost converter connected to a Solarex MSX-60 model. Analysis and comparison with the classical fixed step size P&O and that developed genetic variable step size are presented. The efficiency and improvements of the proposed algorithm in transient, steady-state and dynamic responses, especially under rapidly changing atmospheric conditions, related to ripple, overshoot and response time have been demonstrated. Algorithm robustness was verified using different schemes for temperature and insolation proving its ability to track the maximum power point in case of random and fast changing atmospheric conditions. [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by the flickering behavior of fireflies. Due to its excellent performance, it has been widely used in engineering field.​ However, FA is a population-based algorithm, which needs to occupy a lot of running memory. It is adverse for some small wind turbines or scenarios with limited memory Therefore, this paper proposes an improved firefly algorithm, called parallel compact firefly algorithm (PCFA). The compact technique helps FA save operation memory, which is advantageous for some usage scenarios limited memory. And the parallel technique helps compact FA achieve better solutions and faster convergence. The proposed PCFA was tested on 28 benchmark functions and applied to the proportional–integral–derivative (PID) parameter tuning of the variable pitch wind turbine. Results demonstrate that (1) PCFA is superior to common compact optimization algorithms not only on less memory consumption, but also on more competitive solutions and faster convergence. (2) PCFA shows the better applicability in the PID parameter tuning of variable pitch wind turbine. It can availably smooth the power output of wind turbine under less memory consumption. [ABSTRACT FROM AUTHOR]

Fuzzy logic control has been used frequently in tuning network control system (NCS) due to its on-line dynamic static non-linear match and several remarkable fractional-order controllers have achieved satisfactory control performance when applied to NCS in present years, therefore, in this paper, a novel fractional fuzzy logic controller which combined the fractional algorithm and fuzzy logic control together has been proposed to deal with fixed and random network induced delays in closed-loop feedback systems. The comparisons of set-point tracking performances of fractional fuzzy logic PID controller (FFuzzyPID), conventional fuzzy logic PID controller(FuzzyPID), fractional optimal PID controller (FOPID), and optimal PID controller(OPID) on a representative plant with fixed and random network delays have been shown with simulations. The simulation results indicate that fractional fuzzy logic controller has higher capability to handle network delays compared with other controllers in most cases. [ABSTRACT FROM AUTHOR]

Proportional-integral-derivative (PID) being the most simple and the widely deployed controller in the industrial drives is not quite amenable to the solution for high performance drives as these drives are subjected to the parametric uncertainty, unmodeled dynamics and variable load conditions during operation. In order to expand the robustness and adaptive capabilities of conventional PID controller, a neural network based PID (NNPID) like controller which is tuned when the controller is operating in an on line mode for high performance permanent magnet synchronous motor (PMSM) position control is proposed in this paper. The NN based PID like controller is composed of a mixed locally recurrent neural network and contains at most three hidden nodes which form a PID like structure. A novel training algorithm for the PID controller gain initialization based upon the minimum norm least square solution is proposed. An on line sequential training algorithm based on recursive least square is then derived to update controller gains in an on line manner. The proposed controller is not only easy to implement but also requires least number of parameters to be tuned prior to the implementation. The performance of the proposed controller is evaluated in the presence of parametric uncertainties and load disturbances also the result outcomes are compared with the conventional PID controller, optimized using Cuckoo search based optimization method. [ABSTRACT FROM AUTHOR]

This paper discusses the development of model predictive control algorithm which accounts for the input and state constraints applied to the parabolic partial differential equations (PDEs) system describing the axial dispersion chemical reactor. Spatially varying terms arising from the nonlinear PDEs model are accounted for in model development. Finite-dimensional modal representation capturing the dominant dynamics of the PDEs system is derived for controller design through Galerkin's method and modal decomposition technique. Tustin's discretization and Cayley transform are used to obtain infinite-dimensional discrete-time dynamic modal representations which are used in subsequent constrained controller design. The proposed discrete-time constrained model predictive control synthesis is constructed in a way that the objective function is only based on the low-order modal representation of the PDEs system, while higher-order modes are utilized only in the constraints of the PDEs state. Finally, the MPC formulations are successfully applied, via simulation results, to the PDEs system with input and state constraints. [ABSTRACT FROM AUTHOR]

• We propose the usage of the Equilibrium Optimizer (EO) algorithm for the determination of the optimal values of the Proportional – Integral – Derivative (PID) controller parameters of an Automatic Voltage Regulation (AVR) system. • We propose a novel objective function for PID parameter determination. • EO is found to be superior for PID parameters determination in the terms of the quality of the system response, in the time required for one iteration and whole optimization process, as well as in terms of the convergence speed in comparison with literature known methods for AVR PID parameters design. In this paper, a novel design method for the determination of the optimal values of the Proportional – Integral – Derivative (PID) controller parameters of an Automatic Voltage Regulation (AVR) system is proposed. This method is based on the usage of the Equilibrium Optimizer (EO) algorithm. In order to demonstrate the applicability and efficiency of the proposed algorithm, the literature review of papers dealing with AVR PID design has been considered first. After that, time responses of the AVR system (including PID controller optimized by EO algorithm) with and without different kinds of disturbances have been compared with corresponding results determined for different literature approaches. Furthermore, the comparisons in terms of the accuracy, requested time for one iteration, the total execution time of the algorithm, and convergence characteristics are performed, too. The results show that the EO algorithm significantly outperforms other techniques for all considered criteria. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Abstract: Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced to initialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjust the network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods. [Copyright &y& Elsevier]

Abstract: This paper describes a hierarchical lane keeping assistance control algorithm for a vehicle. The proposed control strategy consists of a supervisor, an upper-level controller and a lower-level controller. The supervisor determines whether lane departure is intended or not, and whether the proposed algorithm is activated or not. To detect driver′s lane change intention, the steering behavior index has been developed incorporating vehicle speed and road curvature. To validate the detection performance on the lane change intention, full-scale simulator tests on a virtual test track (VTT) are conducted under various driving situations. The upper-level controller is designed to compute the desired yaw rate for the lane departure prevention, and for the guidance with ride comfort. The lower-level controller is designed to compute the desired yaw moment in order to track the desired yaw rate, and to distribute it into each tire′s braking force in order to track the desired yaw moment. The control allocation method is adopted to distribute braking forces under the actuator’s control input limitation. The proposed lane keeping assistance control algorithm is evaluated with human driver model-in-the-loop simulation and experiments on a real vehicle. [Copyright &y& Elsevier]

Abstract: In this paper global stabilisation of a network of dynamical systems is attained by local decentralised static output feedback control. The proposed synthesis procedure guarantees an upper bound on the achievable performance in the presence of disturbances. The synthesis of such a controller is posed as an iterative LMI optimisation problem. The algorithm is guaranteed to achieve a local minimum as a result of the iterations. The synthesis of the controller is independent of the number of nodes in the network and depends only on the size of the node level dynamics. A randomly generated academic example with 12 nodes is considered to demonstrate the efficacy of the proposed methodology. [Copyright &y& Elsevier]

Abstract: This paper presents a method of using a CPLD to generate a PWM trigger pulse to a full bridge inverter and a chopper circuit. This method results in a very good high power and low voltage large current pulse plating power supply. A single-chip microcomputer is the core of the feedback control system. A fuzzy PID algorithm with SCM and CPLD complexes precisely controls the output voltage, allowing it to maintain a constant value. The system contains a protection circuit that detects output current and output voltage and can correct the system if it enters an over-current abnormal state, ensuring that the driving circuit can effectively drive the IGBT. The circuit is also protected by setting the inverter frequency and dead time of a digital PWM chip. This method for bidirectional pulse plating power supply digital control was verified to be correct and practicable by a Matlab software simulation. [Copyright &y& Elsevier]

Abstract: This paper presents maximum-power-point-tracking (MPPT) based control algorithms for optimal wind energy capture using radial basis function network (RBFN) and a proposed torque observer MPPT algorithm. The design of a high-performance on-line training RBFN using back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller for the sensorless control of a permanent magnet synchronous generator (PMSG). The MPSO is adopted in this study to adapt the learning rates in the back-propagation process of the RBFN to improve the learning capability. The PMSG is controlled by the loss-minimization control with MPPT below the base speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. Then the observed disturbance torque is feed-forward to increase the robustness of the PMSG system. [Copyright &y& Elsevier]

Abstract: To reduce contour error in contour-following tasks, a common approach is to design a controller based on the contour error information. Hence, real-time contouring error estimation plays an important role in contour-following control. However, the available second-order estimation formulas only apply to biaxial motion systems, and cannot be generalized to handle arbitrary contours tracked by multi-axis motion systems. In this paper, a point-to-curve distance function is defined, and its properties are investigated, especially, its second-order Taylor approximant is derived. On this basis, a novel second-order approach for calculating contour errors of arbitrary contours in real time is proposed. The inter-correlations between the present approach and four commonly used ones are classified. Simulation and experimental results demonstrate the effectiveness of the proposed contour error estimation algorithm. [Copyright &y& Elsevier]

Abstract: This study aims to develop a PD control system based on the neural network algorithm to reduce the ship rolling motion in the desired track through rudder and fin actions. The mathematical model including sea-keeping and maneuvering characteristics is developed in the present paper and the nonlinear time history of ship motions is solved by the fourth order Runge–Kutta method. In order to achieve the purpose of roll reduction and track keeping, the rudder and fin stabilizer are used as the control tools for the ship advancing in the seaway. In addition, the PD controller based on the self-tuning neural network algorithm is applied to achieve the goals of multi-input and multi-output in the control system. Four different types of control modes on a container ship model are studied and the performances are investigated in different sea states. The results indicate that the PD controller based on the self-tuning neural network algorithm applying to the stabilizer fin control for roll reduction and the rudder control for track keeping in the seaway would be suggested. [Copyright &y& Elsevier]

Abstract: Nowadays, a variety of controllers used in process industries are still of the proportional–integral–derivative (PID) types. PID controllers have the advantage of simple structure, good stability, and high reliability. A relevant issue for PID controllers design is the accurate and efficient tuning of parameters. In this context, several approaches have been reported in the literature for tuning the parameters of PID controllers using evolutionary algorithms, mainly for single-input single-output systems. The systematic design of multi-loop (or decentralized) PID control for multivariable processes to meet certain objectives simultaneously is still a challenging task. This paper proposes a new chaotic firefly algorithm approach based on Tinkerbell map (CFA) to tune multi-loop PID multivariable controllers. The firefly algorithm is a metaheuristic algorithm based on the idealized behavior of the flashing characteristics of fireflies. To validate the performance of the proposed PID control design, a multi-loop multivariable PID structure for a binary distillation column plant (Wood and Berry column model) and an industrial-scale polymerization reactor are taken. Simulation results indicate that a suitable set of PID parameters can be calculated by the proposed CFA. Besides, some comparison results of a genetic algorithm, a particle swarm optimization approach, traditional firefly algorithm, modified firefly algorithm, and the proposed CFA to tune multi-loop PID controllers are presented and discussed. [Copyright &y& Elsevier]

Abstract: In this paper, a constant output force control system for giant magnetostrictive actuator which is disturbed by external force continuously was developed. Based on the giant magnetostrictive actuator model related to the combined action of magnetic field and external force, a controller is designed. The controller is composed of giant magnetostrictive actuator model control algorithm and PID control algorithm to realize the output force reaching the constant force target quickly and finely adjustment. Control system’s hardware and related software were designed and software package was developed. Experiment results show that the control system has perfect response performance and stability, and favorable controllability. It can effectively realize the giant magnetostrictive actuator which is disturbed by external force continuously keeping outputting a constant force through adjusting the working current. It lays a certain theoretical foundation for giant magnetostrictive actuator being used in the fields of cutting with invariableness cutting force. [Copyright &y& Elsevier]

Abstract: Controllers of HVAC systems are expected to be able to manipulate the inherent nonlinear characteristics of these large scale systems that also have pure lag times, big thermal inertia, uncertain disturbance factors and constraints. In addition, indoor thermal comfort is affected by both temperature and humidity, which are coupled properties. To control these coupled characteristics and tackle nonlinearities effectively, this paper proposes an online tuned Takagi–Sugeno Fuzzy Forward (TSFF) control strategy. The TS model is first trained offline using Gauss–Newton Method for Nonlinear Regression (GNMNR) algorithm with data collected from both building and HVAC system equipments. The model is then tuned online using the gradient algorithm to enhance the stability of the overall system and reject disturbances and uncertainty effects. As control objective, predicted mean vote (PMV) is adopted to avoid temperature–humidity coupling, thermal sensitivity and to save energy at the same time. The proposed TSFF control method is tested in simulation taking into account practical variations such as thermal parameters of buildings, weather conditions and other indoor residential loads. For comparison purposes, normal Takagi–Sugeno fuzzy and hybrid PID Cascade control schemes were also tested. The results demonstrated superior performance, adaptation and robustness of the proposed TSFF control strategy. [Copyright &y& Elsevier]

Abstract: This paper presents implementation of particle swarm optimization (PSO) algorithm as a C-Mex S-function. The algorithm is used to optimize a 9-rule fuzzy logic controller (FLC) for maximum power point tracking (MPPT) in a grid-connected photovoltaic (PV) inverter. The FLC generates DC bus voltage reference for MPPT. A digital PI current control scheme in rotating dq-reference frame is used to regulate the DC bus voltage and reactive power. The proposed technique simplifies optimal controller design and ensures fast simulation speeds due to seamless integration with the simulation platform. Validity of the proposed method was verified using co-simulation in PSIM and MATLAB/Simulink. Simulation results show that the optimized FLC gives a better performance compared to fixed-step MPPT. [Copyright &y& Elsevier]

Abstract: In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency. [Copyright &y& Elsevier]

Abstract: In this paper, the design of decentralized PID controller for interactive and delay time processes is proposed. Direct controller synthesis approach is used to obtain parameters of the multi-loop controllers. A detuning factor for each loop is specified based on the closed-loop time constant. Then appropriate controller settings are determined using the Maclaurin series expansion and the model of the process. The success of the proposed algorithm is based on the detuning parameter thus the selection of the desired closed-loop time constant is an important factor. The proposed method has no limitations regarding systems weak or strong interactions, multiple delays, and its operating behaviors such as monotonic or oscillatory. The proposed algorithm is simpler and improved performance of various processes are achieved as compared to some of the prevalent techniques. Simulation examples demonstrate the usefulness of the proposed method. [Copyright &y& Elsevier]

Abstract: A discretization algorithm is proposed by Haar wavelet approximation theory for the fractional order integral. In this paper, the integration time is divided into two parts, one presents the effect of the past sampled data, calculated by the iterative method, and the other presents the effect of the recent sampled data at a fixed time interval, calculated by the Haar wavelet. This method can reduce the amount of the stored data effectively and be applied to the design of discrete-time fractional order PID controllers. Finally, several numerical examples and simulation results are given to illustrate the validity of this discretization algorithm. [Copyright &y& Elsevier]

Abstract: In an optical network, the optical signal transmitted along the lightpath may need to travel through a number of cross connect switches (OXCs), optical amplifiers, and fiber segments. While the signal propagates toward its destination, the optical components would continuously degrade the signal quality by inducing impairments. When the signal degradation is so severe that the received bit-error rate (BER) becomes unacceptably high, the lightpath would not be able to provide good service quality to a connection request. Such a lightpath, which has poor signal quality due to transmission impairments in the physical layer, should not be used for connection provisioning in the network layer. This paper presents an adaptive PID controller based on the power compensation of BP neural network to restrict the influence of the impairment power for a networked control system (NCS) with the presence of controller time-delay and power compensation at amplifiers'' node firstly. Control algorithms continuously adjust their channel powers in response to dynamic information from the network links. And the controller could achieve the on-line adaptive power compensation without changing the parameters of PID controller. The results of simulation show that the proposed controller could adjust better channel power at the transmitter sites and achieve channel optical signal-to-noise ratio (OSNR) optimization with controller''s time-delay. [Copyright &y& Elsevier]

Abstract: This paper investigates a neuro-wavelet control (NWC) system to address the problem of synchronization control of uncertain chaotic systems. In this NWC system, a wavelet neural network (WNN) controller is the principal tracking controller designed to mimic the perfect control law and an auxiliary compensation controller is used to recover the residual approximation error so that the favorable synchronization can be achieved. Moreover, the proportional-integral (PI) training algorithms of the control system are derived from the Lyapunov stability theorem, which are utilized to update the adjustable parameters of WNN controller on-line for further assuring system stability and obtaining a fast convergence. In addition, to relax the requirement of unknown uncertainty bound, a bound estimation law is derived to estimate the uncertainty bound. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control strategy. The simulation results demonstrate that the proposed NWC with PI training algorithms can synchronize the chaotic systems more accurately than the other control strategies. [Copyright &y& Elsevier]

Abstract: In this paper a new auto-tuning algorithm for PI and PID controllers based on relay experiments is proposed to minimize the load disturbance integral error (IE) by maximizing the integral gain, subject to a desired phase margin, and a minimum required gain margin constraint. The main advantage of the proposed auto-tuning algorithm with respect to previous works is that it leads, for most of the processes, to PID tuning with close loop performance similar to PID designed using off-line numerical optimization. Moreover the algorithm is applicable to any linear model structure, including dead time and non-minimum phase systems. [Copyright &y& Elsevier]

Abstract: In this paper, the results of a research project aimed at deriving simple, high-performance, auto-tuning, robust control algorithms for evaporators controlled by means of EEVs (Electronic Expansion Valves) are reported. Control design is performed by resorting to a detailed virtual prototyping environment described in . The proposed control scheme consists of two nested loops. In the inner loop, the plant is connected in feedback to a PID controller. It is assumed that the structure of the process model is given but its parameters are unknown. The outer loop is composed of a parameter estimator and an adaptation algorithm that updates the parameters of the PID controller on the basis of the result of a system identification procedure. Performance of the proposed controller is evaluated in the virtual prototyping environment by means of simulations. [Copyright &y& Elsevier]

Abstract: In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed. Besides the individual best position and the global best position, a nominal average position of the swarm is introduced in IPSO. The performance of IPSO is compared to different PSO variants with five well-known benchmark functions. The experimental results show that the proposed IPSO algorithm improves the searching performance on the benchmark functions. And then, IPSO, as well as other PSO variants, is applied to optimal tuning of Proportional–Integral–Derivative (PID) gains for a typical PID control system of water turbine governor. The computer simulation results of an actual hydro power plant in China show that IPSO algorithm has stable convergence characteristic and good computational ability, and it is an effective and easily implemented method for optimal tuning of PID gains of water turbine governor. [Copyright &y& Elsevier]