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This paper has discussed the study of the performance of dynamic voltage restorer (DVR) taking different voltage sag conditions in the supply voltage of the distribution system for linear and non-linear load. DVR is employed to mitigate the voltage sag. Proportional and integral (PI) controller is used for the control of DVR to mitigate the voltage sag. Synchronous reference frame theorybased control algorithm has been implemented for generating reference voltages. In this paper, an efficient improved particle swarm optimisation technique for optimizing the gain parameters of the PI controller has been proposed. The performance and suitability of DVR is validated through MATLAB simulation results and use of Sim Power Systems Blocksets. Integral squared error is implemented to check the performance and suitability of DVR. Extensive results of the proposed method are presented and compared with conventional Ziegler Nichols and genetic algorithm methods of tuning gain parameters of PI controllers. [ABSTRACT FROM AUTHOR]

Summary: Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In this paper, we demonstrate the capabilities of a combined L1 adaptive control and iterative learning control (ILC) framework to achieve high‐precision trajectory tracking in the presence of unknown and changing disturbances. The L1 adaptive controller makes the system behave close to a reference model; however, it does not guarantee that perfect trajectory tracking is achieved, while ILC improves trajectory tracking performance based on previous iterations. The combined framework in this paper uses L1 adaptive control as an underlying controller that achieves a robust and repeatable behavior, while the ILC acts as a high‐level adaptation scheme that mainly compensates for systematic tracking errors. We illustrate that this framework enables transfer learning between dynamically different systems, where learned experience of one system can be shown to be beneficial for another different system. Experimental results with two different quadrotors show the superior performance of the combined L1‐ILC framework compared with approaches using ILC with an underlying proportional‐derivative controller or proportional‐integral‐derivative controller. Results highlight that our L1‐ILC framework can achieve high‐precision trajectory tracking when unknown and changing disturbances are present and can achieve transfer of learned experience between dynamically different systems. Moreover, our approach is able to achieve precise trajectory tracking in the first attempt when the initial input is generated based on the reference model of the adaptive controller. [ABSTRACT FROM AUTHOR]

Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly. [ABSTRACT FROM AUTHOR]

Background: Although Pneumatic Artificial Muscle (PAM) has a promising future in rehabilitation robots, it's difficult to realize accurate position control due to its highly nonlinear properties.Objective: This paper deals with position control of PAM.Methods: To describe the hysteresis inside PAM, a polynomial based phenomenological function is developed. Based on the phenomenological model for PAM and analysis of pressure dynamics within PAM, an adaptive cascade controller is proposed. Both outer loop and inner loop employ BP Neural Network tuned PID algorithm. The outer loop is to handle high nonlinearities and unmodeled dynamics of PAM, while the inner loop is responsible for nonlinearities caused by pressure dynamics.Results: Experimental results show high tracking accuracy as compared with a convention PID controller.Conclusion: The proposed controller is effective in improving performance of PAM and will be implemented in a rehabilitation robot. [ABSTRACT FROM AUTHOR]

The wind generator system is a large inertia system, so the use of the PI controller in pitch control will lead to the system instability. The theory of variable pitch wind turbines is analyzed in this paper. The mathematical model of the aerodynamic part, the drive part, the operative part and the whole unit are established. An algorithm is proposed to allow the generator to track the maximum power points of the wind turbine system by PD controller. The proposed algorithm is verified by simulation re-sult, and good control results are achieved. [ABSTRACT FROM AUTHOR]

Automatic generation control (AGC) is an important service used for the secure and reliable operation of a power system. In this paper, a suitable AGC scheme for a deregulated environment has been studied for various load perturbation scenarios. A proportional integral derivative (PID) controller has been utilized to control the real power output of the generators. The optimal parameters of the PID controller have been determined using the big bang big crunch (BBBC) algorithm, (BBBC-PID). The performance of the BBBC-PID controller has been checked on three different sized multiarea power systems. The results obtained with the applied algorithm have also been compared with the results of other algorithms, namely imperialistic competition algorithm (ICA) and harmony search (HS). MATLAB/Simulink was used as a simulation tool for this study. The results show the superiority of the BBBC-PID over the others. [ABSTRACT FROM AUTHOR]

Developing fast calibration algorithms is an important step towards the online simulation of hydraulic water networks. The ob- jective of this paper is to report a new demand calibration approach by using algorithms from control theory. We propose a multivariable PID control where the control parameters are identified by a system identification approach and the system matrix is decoupled. In this control approach the measured flow variables are regarded as dynamic set-points. The controllers use the demand values as manipulated variables in order to control the flow variables to the measured flow. The concept was applied successfully using a small test-network. [ABSTRACT FROM AUTHOR]

This paper introduces a control system model framework built into EPANET simulator. The control system model extends the current possibilities offered by EPANET to model the exact, dynamic behavior of the water distribution system working under complex or high-level control system. Besides the control system model, a novel method for modelling parallel pumping stations in EPANET was developed. The new methodology allows varying the control type (pressure, flow) and setting to dynamically change during the simulation. The new pump battery component was utilized in the control system modelling. The new modelling tools were demonstrated in a case study. [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]

This paper aims at the PID control system design for multivariable input and multivariable output (MIMO) processes. An improved version of a particle swarm optimization (PSO) algorithm is utilized to design PID control gains in MIMO control systems. In addition to the individual best and the global best particles, the velocity updating formula of the developed algorithm includes a new factor, the best particle of each sub-population, to enhance the search capacity. Based on the improved particle swarm optimization (IPSO), a complete design strategy is proposed for MIMO PID control systems. All control gains will be evolved to the optimal values by minimizing the system performance criterion. To show the efficiency of the proposed design method, a multivariable chemical process system with two inputs and two outputs is illustrated. Some experiment results, including different algorithm parameter settings and comparisons with other methods, are given. Numerical simulations indicate that the proposed method is superior to other optimal methods. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear fuzzy PID control algorithm, whose membership function (MF) is adjustable, is universal, and has a wide adjustable range. Appling this function to fuzzy control theory will increase system's tunability. The continuity of this function is proved. This method was employed in the simulation and HIL experiments. Effectiveness and feasibility of this function are demonstrated in the results. [ABSTRACT FROM AUTHOR]

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: A new PID controller for resistant differential control against load disturbance is introduced that can be used for load frequency control (LFC) application. Parameters of the controller have been specified by using imperialist competitive algorithm (ICA). Load disturbance, which is due to continuous and rapid changes of small loads, is always a problem for load frequency control of power systems. This paper introduces a new method to overcome this problem that is based on filtering technique which eliminates the effect of this kind of disturbance. The object is frequency regulation in each area of the power system and decreasing of power transfer between control areas, so the parameters of the proposed controller have been specified in a wide range of load changes by means of ICA to achieve the best dynamic response of frequency. To evaluate the effectiveness of the proposed controller, a three-area power system is simulated in MATLAB/SIMULINK. Each area has different generation units, so utilizes controllers with different parameters. Finally a comparison between the proposed controller and two other prevalent PI controllers, optimized by GA and Neural Networks, has been done which represents advantages of this controller over others. [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]

This paper presents a novel smart MEMS accelerometer which employs a hybrid control algorithm and an estimator. This scheme is realized by adding a sliding-mode controller to a conventional PID closed loop system to achieve higher stability and higher dynamic range and to prevent pull-in phenomena by preventing finger displacement from passing a maximum preset value as well as adding an adaptive nonlinear observer to a conventional PID closed loop system. This estimator is used for online estimation of the parameter variations for MEMS accelerometers and gives the capability of self testing to the system. The analysis of convergence and resolution show that while the proposed control scheme satisfies these criteria it also keeps resolution performance better than what is normally obtained in conventional PID controllers. The performance of the proposed hybrid controller investigated here is validated by computer simulation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we develop an algorithm to determine the entire set of stabilising PID parameters for the integrator plus dead-time process. Our method is a combination of the traditional D-partition technique and graphical technique. We first apply the D-partition technique to address the stability regions in the plane of proportional and integral gains, which depend on the steady-state gain and the time delay. Next for fixed proportional gain, again using the D-partition technique we obtain a series of critical straight lines in the plane of integral and derivative gains and then choosing the derivative gain as a parameter, we investigate how the derivative gain affects the distribution of the roots of the corresponding characteristic equation of the closed-loop control system, which provide the stability regions in the plane of integral and derivative gains. On the base of the results obtained in the previous two steps, an efficient algorithm for determining the entire set of stabilising PID parameters is also proposed. Finally, numerical simulations are given to support our theoretical results. [Copyright &y& Elsevier]

Abstract: This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system. [Copyright &y& Elsevier]

The crux in designing the PID controller lies in determining its gain values, which play a major role in deciding its performance. The gains are fed as inputs to the controller and are to be decided before its run. On the other hand, the effectiveness of the biped walk purely depends on the performance of the PID controller. Initially, the upper and lower body gaits of the two-legged robot are determined using the concept of inverse kinematics. Further, the dynamics of the biped robot is derived by using Lagrange–Euler formulation. The main objective of the present research is to decide the gains of the torque-based PID controller with the help of a neural network trained by using nature-inspired optimization algorithms, namely MCIWO and PSO. The adaptiveness of the algorithm lies in modifying the gains of the controller based on the magnitude of the error in the angular displacement received at the input to the NN. Once the controller is developed, its effectiveness is tested in computer simulations. Finally, the optimum controlled gait angles obtained by the best approach are tested on a real biped robot. [ABSTRACT FROM AUTHOR]

A novel control scheme based on multiple model predictive functional control (MMPFC) is proposed to solve the cumbersome and time-consuming parameters tuning of the speed controller for a marine diesel engine. It combines the MMPFC with traditional PID algorithm. In each local linearization, a first-order plus time delay (FOPTD) model is adopted to be the approximate submodel. To overcome the model mismatches under the load disturbance conditions, we introduce a method to estimate the open-loop gain of the speed control model, by which the predictive multimodels are modified online. Thus, the adaptation and robustness of the proposed controller can be improved. A cycle-detailed hybrid nonlinear engine model rather than a common used mean value engine model (MVEM) is developed to evaluate the control performance. In such model, the marine engine is treated as a whole system, and the discreteness in torque generation, the working imbalance among different cylinders, and the cycle delays are considered. As a result, more reliable and practical validation can be achieved. Finally, numerical simulation of both steady and dynamic performances of the proposed controller is carried out based on the aforementioned engine model. A conventional well-tuned PID with integral windup scheme is adopted to make a comparison. The results emphasize that the proposed controller is with stable and adaptive ability but without needing complex and tough parameters regulation. Moreover, it has excellent disturbance rejection ability by modifying the predictive multimodels online. [ABSTRACT FROM AUTHOR]

The optimum tuning rules for various integrating systems with time delay in terms of the process model parameters using Differential Evolution (DE) algorithm are proposed. The objective function is integral performance index (integral time-weighted absolute error, ITAE) for both servo and regulatory problems. The performance and robustness of the proposed optimal controllers are compared with the controllers designed by recently reported methods. Simulation results on various transfer function models are given to show the efficiency of the proposed optimal controllers. [ABSTRACT FROM PUBLISHER]

In this work, a feedback control algorithm was developed based on geometric control theory. A nonisothermal seeded continuous crystallizer model was used to test the algorithm. The control objectives were the stabilization of the third moment of the crystal size distribution (μ3) and the crystallizer temperature (T); the manipulated variables were the stirring rate and the coolant flow rate. The nonlinear control (NLC) was tested at operating conditions established within the metastable zone. Step changes of magnitudes ±0.0015 and ±0.5°C were introduced into the set point values of the third moment and crystallizer temperature, respectively. In addition, a step change of ±1°C was introduced as a disturbance in the feeding temperature. Closed-loop stability was analyzed by calculating the eigenvalues of the internal dynamics. The system presented a stable dynamic behavior when the operation conditions maintain the crystallizer concentration within the metastable zone. Closed-loop simulations with the NLC were compared with simulations that used a classic PID controller. The PID controllers were tuned by minimizing the integral of the absolute value of the error (IAE) criterion. The results showed that the NLC provided a suitable option for continuous crystallization control. For all analyzed cases, the IAEs obtained with NLC were smaller than those obtained with the PID controller. [ABSTRACT FROM AUTHOR]

The current increment in energy consumption has renewed the interest in the development of alternatives to fossil fuels. In this regard, the interest in solving the different control problems existing in nuclear fusion reactors like Tokamaks has been intensified. The aim of this manuscript is to show how the ASTRA code, which is used to simulate the performance of Tokamaks, can be integrated into the Matlab-Simulink tool in order to make easier the development of suitable controllers for Tokamaks. As a demonstrative case study to show the feasibility and the goodness of the proposed integration, a modified anti-windup PID-based controller coupled with an optimization algorithm for the loop voltage has been implemented. This integration represents an original and innovative work in the Tokamak control area and it provides new possibilities for the development and application of advanced control schemes to the standardized ASTRA code. [ABSTRACT FROM AUTHOR]