Design Of Self-tuning Controller Research Articles

Self-tuning proportional-derivative pitch control for internal fault-tolerance in wind turbine benchmark

ABSTRACT The wind turbine (WT) dynamics are affected by wind variations and system parameter changes as well. To deal with the pitch control problem a Self-Tuning Controller (STC) with Proportional-Derivative (PD) structure has been proposed for the benchmark model (BM). This BM also models some sensors, actuators and system faults. The STC design herein is pivoted on a second-order auto-regressive exogenous (ARX) model. At first, three identification methods for the ARX model are presented: Recursive Least Square method (RLS), RLS with exponential forgetting (RLS-EF) and RLS with adaptive directional forgetting (RLS-ADF). In comparison, the performance of RLS-ADF is better than the rest two. The STC PD controller supersedes contemporary adaptive control approaches to fix up the generated power to its rating for above-rated wind. Secondly, on analysis, the proposed STC PD controller is fault-tolerant for nine types of faults. In the third stage, in extensive Monte Carlo Simulation (MCS), the sensitivity test to system parameter variation is carried out including the sensor faults. The result in the presence of a fault, as well as parameter variation, shows minimal deviation from normal conditions indicating the suitability of the STC PD controller to keep the system intact except in case of multiple faults.

Design and Evaluation of Self-Tuning Fuzzy PID Controller with Reference Model for Ball and Beam Mechanism

—Fuzzy Logic controller is one of the popular techniques in control system. However, the accuracy depends on numbers and the design of membership functions. Further, the output does not always fulfill specific design requirements. In this study, Fuzzy PID controller with first-order reference model had been designed and evaluated. The proposed new structure is able to guide fuzzy mechanism to produce desired output response as specified by the first-order model such as the time constant, rise time and settling time. The study was implemented on a ball and beam system to control the position of the ball while rolling on a beam. The control of a ball and beam system is challenging because it is an open-loop unstable system as the ball will continuously moves on the beam without control. The study also compares the results acquired from Fuzzy PID and Fuzzy PI plus PD against the proposed Fuzzy PID with first-order reference model controller. The evaluation covers for a pole starting from 0 until 1.0 for the first-order model. As a results, the proposed controller is able to produce an output response as specified where the fastest response was acquired when pole is set to 0.9. Whereas the Fuzzy PID and Fuzzy PI plus PD always produce the same transient response during step input test.

Design of a robust adaptive self-tuning regulator controller on single-phase full-bridge grid-connected inverter

Design of a robust adaptive self-tuning regulator controller on single-phase full-bridge grid-connected inverter

Self-tuning Fuzzy PID Controller Design and Energy Management in DC Microgrid: Standalone and Grid Connected Mode

This paper presents a control system and energy management strategy with a standalone and grid-connected mode in the microgrid. The microgrid is energized by distributed generation system of a photovoltaic panel, wind turbine and lithium-ion batteries. In this paper, the controller structure for regulating the voltage regulation in this microgrid consisting of renewable energy sources and battery system is covered. Overshoot, rise time, settling performances of designed self-tuning fuzzy proportional-integral-derivative controller and conventional proportional-integral-derivative controller examined comparatively. In addition, an energy management system has been proposed for loads which fed in the microgrid. Here, the aim is to make maximum use of renewable energy sources as much as possible, to maintaining voltage regulation and to ensure continuity in the feeding of the critical load. The system switches between the standalone and grid-connected modes if necessary. A model of the microgrid's dynamic behavior was constructed and it is simulated in the MATLAB®/Simulink environment. The results show that despite the changes in the production and load side of the grid, voltage and frequency oscillations are stabilized within a short time and allowed tolerance limits.

Control System and Algorithm of Sewage Treatment Plant Based on Artificial Intelligence

With the improvement of people’s awareness of environmental protection in recent years, the related problems of water pollution treatment have gradually come into people’s view. As the source of life, water accounts for a huge proportion in our lives, but at the same time, water pollution is quietly spreading in places we don’t know. The continuous discharge of heavy industrial wastewater, agricultural wastewater and domestic sewage leads to increasingly serious water pollution. Sewage treatment(ST) is imperative, and its social benefits are huge, but the corresponding cost is high, and the return on investment is low. Traditional ST methods can not load large-scale ST. How to carry out ST based on artificial intelligence(AI), build ST plant control system, and make ST enter the era of automation is the problem to be solved. The purpose of this paper is to put forward the reform of control system for ST plant based on AI, apply AI into ST, and realize the automation and precision of ST plant. This paper mainly uses the fuzzy self-tuning PID control system algorithm, through the analysis of ST control object, analysis of fuzzy self-tuning PID controller design to complete the ST control system settings. In this paper, the literature review method and data analysis method are used. By collecting relevant data, the control system of ST plant is constructed to simulate ST, and the real-time data of ST is analyzed. The traditional PID control and fuzzy self-tuning PID control are compared. The experimental results show that the wastewater treatment plant system based on AI input, in the aspect of wastewater treatment, the concentration of COD and BOD in the treated wastewater are reduced by a certain proportion, the dissolved oxygen content in the wastewater reaches about 2.0mg/l, which meets the national discharge standard, and its rising time is reduced to 25 seconds, and the adjustment time is saved by 50 seconds.

Design of Neutrosophic Self-Tuning PID Controller for AC Permanent Magnet Synchronous Motor Based on Neutrosophic Theory

In practical control applications, AC permanent magnet synchronous motors need to work in different response characteristics. In order to meet this demand, a controller which can independently realize the different response characteristics of the motor is designed based on neutrosophic theory and genetic algorithm. According to different response characteristics, neutrosophic membership functions are constructed. Then, combined with the cosine measure theorem and genetic algorithm, the neutrosophic self-tuning PID controller is designed. It can adjust the parameters of the controller according to response requirements. Finally, three kinds of controllers with typical system response characteristics are designed by using Simulink. The effectiveness of the designed controller is verified by simulation results.

Robust self-tuning regressive adaptive controller design for a DC–DC BUCK converter

This paper demonstrates Robust Adaptive Control (RAC) approach using a technique for system identification applied to a Buck (step-down) converter by PWM (Pulse width modulation) in the presence of input voltage variations, load variations, parametric variations and high variance noises. Maintaining stability margins and closed-loop regulation within a pre-determined range of operating condition is the main objective behind designing feedback loops. The proposed control technique omits the need for mathematical model of the system by using a black-box identification procedure. Due to the various disturbing factors on a DC–DC converter, Minimum Degree Pole Placement (MDPP) scheme is proposed as an adaptive control technique, which is optimized with an Improved Exponential Regressive Least identification (IERLS) algorithm. Additionally, to hinder the impact and the uncertainty caused by high variance noise, a robust identification algorithm is introduced. Finally, capability of the proposed method is examined in different operating conditions through simulations and experimental results.

Optimized fuzzy self-tuning PID controller design based on Tribe-DE optimization algorithm and rule weight adjustment method for load frequency control of interconnected multi-area power systems

The reliable load frequency control (LFC) is addressed as one of the most important services in the modern electric power system operation and planning. Since the power systems have various structural and non-structural uncertainties, using control methods with fixed parameters may not yield the optimal performance of the system. Thus, in this research, a novel fuzzy PID controller is introduced to LFC for interconnected multi-area power systems in parametric uncertainties as well as external disturbances existence. The proposed algorithm adjusts the scaling factors and the modal parameters of input and output membership functions as well as the weights of fuzzy PID controller rule values. According to the transient response of the area control error (ACE) variable partitioning, the fuzzy rule weight values are obtained. Furthermore, in order to enhance the quality of the response, the scaling factors and the modal parameters of the input and output membership functions of fuzzy PID controllers are optimized by Tribe-DE (TDE) algorithm. The method is examined on the two and three area interconnected power systems at several conditions and an Integral of Time multiplied Absolute Error (ITAE) less than 0.0108 as well as an absolute maximum undershoot less than 0.0210 Hz for Δf1 regulation are obtained in the two area interconnected power system. Good transient behavior, disturbance rejection capability and insensitivity to parameter changes are advantages of the proposed controller.

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed.

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

System identification and control design of a nonlinear continuously stirred tank reactor

This study presents a black-box identification and a self-tuning control design of a nonlinear continuously stirred tank reactor (CSTR). Analyzing and controlling the CSTR is a challenging topic because of its highly nonlinear dynamics and its sensitivity, which, in turn, requires a high-performance control design. The data set used in the identification is collected from the nonlinear dynamical equations of the CSTR in the presence of white and colored noises for considering realistic conditions. Then, linear model structures are selected, and by employing the online and offline estimation methods, the best representation of the CSTR is determined based on a fitness index. By employing the results of the online identification algorithms, a self-tuning proportional–integral–derivative (PID) controller is designed which provides an ability to control the CSTR in the presence of unwanted signals (i.e., noise and disturbance) and variations in system dynamics by considering several tuning parameters including forgetting factor and suitable initial values. The performance of the proposed approach is compared with several works in the literature, reflecting the superiority of the presented technique.

Design of a Data-Driven Multi-loop Self-Tuning PID Controller

PID controllers have been widely employed in real processes. Since PID gains strongly affect the control performance, lots of schemes for tuning PID gains have been proposed. Recently, data-driven PID tuning schemes which determine PID gains directly from the closed-loop operating data attract attentions. Lots of controlled objects are multi-input and multi-output systems, though almost data-driven PID tuning schemes target single-input and single-output systems. In this paper, a data-driven multi-loop self-tuning PID controller design is proposed. The proposed scheme first employs a post-compensator to remove mutual interference. Multi-loop self-tuning PID controller is designed for the decoupled system. The effectiveness of the control scheme is evaluated by a simulation example.

Design of Self-Tuning SISO Partial-Form Model-Free Adaptive Controller for Vapor-Compression Refrigeration System

Vapor-compression refrigeration systems (VCRS) are applied extensively in domestic, commercial and industrial refrigeration and are responsible for a high percentage of worldwide energy consumption. To achieve high energy efficiency, the application of advanced control methods in VCRS has increasingly attracted the attention of academia and industry. The model-free adaptive control (MFAC) strategy, as an important branch of advanced control research, encounters the problem of parameter tuning when applied to VCRS with strong nonlinearities. In this work, a parameter self-tuning methodology based on the self-learning and self-adapting properties of back propagation neural networks with the System Error set and/or Gradient Vector set as inputs is proposed to adjust the parameters utilized in SISO Partial-Form Model-Free Adaptive Control (SISO-PFMFAC). To test the performance of this novel methodology named SISO-PFMFAC-NNSEGV, qualitative and quantitative comparisons are carried out between the proposed method and the decentralized single PIDs given in the simulation platform provided by the benchmark PID 2018. The integral absolute error ( IAE ), the integral time-weighted absolute error ( ITAE ), the integral absolute variation of control signal ( IAVU ) and a combined index $J_{c}$ are used to evaluate the performance. As a result, the proposed method shows the best performance with a higher tracking accuracy and less variation of the control signal with a combined index $J_{c} = 0.7088$ , which represents a 29.1% improvement over the benchmark PID controller and a 9.6% improvement over the SISO-PFMFAC controller, making it a promising control method for vapor-compression refrigeration systems.

Design and simulation of self-tuning fractional order fuzzy PID controller for robotic manipulator

Two-link robotic manipulator system is completely nonlinear and time-varying multi-input-multiple output. In this research, the fractional order fuzzy PID (FOFPID) controller is proposed in order to control the robotic manipulator position. Since real control systems are generally nonlinear systems, therefore, better control of these systems requires the usage of an adaptive or nonlinear controller. So we applied a fuzzy system in order to determine the coefficients of a FOPID controller based on particle swarm optimization (PSO) algorithm. In definition of fitness function for this optimization, we considered integral of absolute error (IAE) and integral of absolute change in controller output (IACCO). Finally, in order to compare this controller with the FPID controller, numerical simulations were performed on the robotic manipulator. The results demonstrate that the overshoot of FOFPID controller is less than the FPID and proposed controller has less oscillations amplitude, totally, its performance is better than the FPID controller.

Design and simulation of self-tuning fractional order fuzzy PID controller for robotic manipulator

Design and simulation of self-tuning fractional order fuzzy PID controller for robotic manipulator

Samopodesivi regulator baziran na kaskadnom upravljanju temperaturom kod egzotermne reakcije mešanog sredstva u rezervoaru reaktora

In the process of chemical industries production, an exothermic stirred tank reactor permits the reaction to release the heat due to mixing of substances. The release of heat due to the exothermic process in the stirred tank makes the system nonlinear and uncertain unless it is not regulated properly. In order to regulate the heat release, the self-regulated cascade control model of reactor temperature plant is developed with reference to material and energy balance model of exothermic stirred tank reactor. In this paper the recursive least square algorithm is used for self-tuning regulator cascade control online temperature plant and its controller parameters estimation and also the temperature plant output has been simulated in the presence and absence of a primary self-tuning controller. The use of the secondary loop PI controller will respond rapidly to the flow rate of cooling jacket plant that in turn cascade with the disturbance variables like inlet feed concentration and cooling water temperature. The reactor temperature is monitored to the desired steady state level and becomes stable for further steady state value of parameters. The parameters of the temperature plant will also be stable with online parameter estimating loop of the control system. The designing of master controller and desired plant model using minimum degree pole placement algorithm is highlighted. With the developed SIMULINK design of self-tuning controller, the better comparative results have been achieved over Internal Model Control (IMC), Model Predictive Control (MPC) and conventional based temperature controller plant.

A self-tuning robust full-state feedback control design for the magnetic levitation system

In this study, a self-tuning robust integral of signum of error (RISE) based controller is designed and used to control a magnetic levitation (maglev) system. In the control design, unlike the classical RISE controller, ‘tanh’ function is used instead of ‘signum’ function to obtain a more smooth control signal. The gains of the controller are updated according to a time-varying update rule. Convergence of the error under the closed-loop operation is proven via Lyapunov-based stability analysis. The controller is tested on an experimental maglev system and successful results are obtained.

Design of an Implicit Self-tuning PID Controller based on an Augmented Output

Design of an Implicit Self-tuning PID Controller based on an Augmented Output

A BP Network Control Approach for QoS-Aware MAC in Cloud Robotics

The basic idea of Cloud Robotics is dynamically uploading the compute-intensive applications to the cloud, which greatly enhances the intelligence of robots for the high processing and parallel ability of cloud. However, for the nature of uncertainty of mobility, different kinds of applications on robot may have different Quality of service (QoS). The paper proposes a BP network for QoS-aware MAC(BPFD-MAC) in Cloud Robotics form a view control theory, which can support both absolute and relative QoS guarantees while the energy saving. The hard and soft QoS constraints are de-coupled by normalized into a two-level cascade feedback loop. The former is Active Time Loop (AT-Loop) to enforce the absolute QoS guarantee for real-time application and the later is Contention Window Loop (CW-Loop) to enforce the relative QoS guarantee for Best Effort traffics. Finally, the Back-propagating (BP) neuron network based PID is used for self-tuning parameters and controller design. The hardware experiments demonstrate the feasibility of BPFD-MAC. Comparing with FD-MAC, BPFD-MAC has new feature of absolute QoS support and further developed two advantages:In the condition of heavy loads, BPFD have about 18% great throughput and 14% great power efficient; and in light load, BPFD have lower total energy consumption.

Study on a Kalman Filter based PID Controller

This study proposes a self-tuning PID controller design method based on a Kalman filter. Recently, data-driven controller tuning methods that can directly tune control parameters by closed-loop data without system models have been received much attention as convenient tuning approaches. On the other hand, in parameter estimation problems, the Kalman filter that can obtain high-precision estimation results has been applied in many research/industrial area. In this paper, a data-driven PID parameters tuning problem that is derived based on a PID control law is resolved as a Kalman filtering problem, and a self-tuning PID controller based on the Kalman filter is proposed. The effectiveness of the proposed method is evaluated by simulation and experimental examples.

Robust Self-Tuning Control Design under Probabilistic Uncertainty using Polynomial Chaos Expansion-based Markov Models

A robust adaptive controller is developed for a chemical process using a generalized Polynomial Chaos (gPC) expansion-based Markov decision model, which can account for time-invariant probabilistic uncertainty and overcome computational challenge for building Markov models. To calculate the transition probability, a gPC model is used to iteratively predict probability density functions (PDFs) of system’s states including controlled and manipulated variables. For controller tuning, these PDFs and controller parameters are discretized to a finite number of discrete states for building a Markov model. The key idea is to predict the transition probability of controlled and manipulated variables over a finite future control horizon, which can be further used to calculate an optimal sequence of control actions. This approach can be used to optimally tune a controller for set point tracking within a finite future control horizon. The proposed method is illustrated by a continuous stirred tank reactor (CSTR) system with stochastic perturbations in the inlet concentration. The efficiency of the proposed algorithm is quantified in terms of control performance and transient decay.