Steady-state Error Performance Research Articles

Wideband Interference Cancellation System Based on a Fast and Robust LMS Algorithm.

The interference cancellation ratio (ICR) is a key performance indicator of digital-to-analog hybrid radio frequency (RF) interference cancellation systems. Aiming at the low convergence speed of a digital-to-analog hybrid RF interference cancellation system based on a multi-tap structure (MDARFICS), a novel, fast, and robust variable-step-size least-mean-square (LMS) algorithm based on an improved hyperbolic tangent function (IHVSS-LMS) is proposed. The IHVSS-LMS algorithm adopts an improved hyperbolic tangent function and uses adjustable parameters and the iteration number to jointly adjust the step size, which improves the convergence speed and reduces the computational complexity. Moreover, by using the prior information of the input signal, the non-linear relationship between the step size and the input signal power is established, which enhances the robustness and the ability to suppress interference with mutable power. The IHVSS-LMS algorithm is applied to the MDARFICS. Through theoretical derivation, the convergence speed and the steady-state expressions of the interference cancellation ratio of the MDARFICS are obtained. The simulation results show that under the conditions of high and low signal-to-noise ratio (SNR), the robustness, convergence speed, and steady-state error performance of the IHVSS-LMS algorithm are better than the existing variable-step-size algorithm. The experimental results show that using the IHVSS-LMS algorithm, the MDARFICS can not only effectively accelerate the convergence speed by at least three times but can also improve the ICR by more than 3 dB.

African vulture optimizer algorithm based vector control induction motor drive system

<span lang="EN-US">This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response.</span>

Fractional-order ascent maximum mixture correntropy criterion for FLANNs based multi-channel nonlinear active noise control

The filtered-s maximum correntropy criterion (FsMCC) algorithm was developed for nonlinear active noise control (NANC) in impulsive noise environments with the use of functional linked artificial neural networks (FLANNs) structure. However, the kernel-width of the FsMCC is constant, which is dull and cannot self-regulate to achieve both the fast convergence and low steady-state error performance. For better convergence ability, in this paper, a fractional-order gradient ascent filtered-s maximum correntropy criterion algorithm (FGA-FsMCC) is presented for multi-channel NANC system. The fractional lower-order moments supersede the non-existent second-order moments. To come up with a more reliable algorithm, by merging two kernels with complementary width, the fractional-order gradient ascent filtered-s mixture correntropy criterion (FGA-FsMMCC) was presented. The computational complexity and convergence conditions are analyzed. To verify the robustness of proposed algorithms, simulations are carried out to discuss parameters and control effects of algorithms for alpha-stable noise in different nonlinear environments.

Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm

<span lang="EN-US">Mobile robots are typically depending only on robot kinematics control. However, when high-speed motions and highly loaded transfer are considered, it is necessary to analyze dynamics of the robot to limit tracking error. The goal of this paper is to present a new algorithm, chaotic-billiards optimizer (C-BO) to optimize internal controller parameters of a differential-drive mobile robot (DDMR)-based dynamic model. The C-BO algorithm is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. In addition, a comparison between the performance of C-BO and ant colony optimization (ACO) to determine the optimum controller coefficient that provides superior performance and convergence of the path tracking. The ISE criterion is selected as a fitness function in a simulation-based optimization strategy. For the point of accuracy, the velocity-based dynamic compensation controller was successfully integrated with the motion controller proposed in this study for the robot's kinematics. Control structure of the model was tested using MATLAB/Simulink. The results demonstrate that the suggested C-BO, with steady state error performance of 0.6 percent compared to ACO's 0.8 percent, is the optimum alternative for parameter optimizing the controller for precise path tracking. Also, it offers advantages of quick response, high tracking precision, and outstanding anti-interference capability.</span>

Study on Active Control of Stick-slip Vibration of Drill String System

The stick-slip vibration phenomenon of drill string widely exists in the drilling process, which has a great negative impact on drilling equipment, efficiency, cost and safety. In order to effectively suppress the stick-slip vibration of the drill string system, a two-degree-of-freedom torsional vibration model of the drill string system is established considering the nonlinear friction between the drill bit and rock. Based on this model, an optimal LQR controller is designed to suppress the stick-slip vibration of drill string, and the driving torque is fed back in real time through the state error. The effectiveness of the controller under parameter perturbation is verified by simulation analysis. The results show that the controller can effectively suppress the stick-slip vibration of drill string system under the desired speed, feedforward torque, start time and bit weight parameter perturbation, achieve real-time feedback of driving torque, and ensure that the angular velocity of rotary table and BHA can be tracked and maintained at the desired speed in a short time. The control effect shows good steady-state error and dynamic performance. The research results have a certain reference significance for suppressing stick-slip vibration of drill string system and improving drilling efficiency.

Adaptive online auto-tuning using Particle Swarm optimized PI controller with time-variant approach for high accuracy and speed in Dual Active Bridge converter

<abstract> <p>Electric vehicles (EVs) are an emerging technology that contribute to reducing air pollution. This paper presents the development of a 200 kW DC charger for the vehicle-to-grid (V2G) application. The bidirectional dual active bridge (DAB) converter was the preferred fit for a high-power DC-DC conversion due its attractive features such as high power density and bidirectional power flow. A particle swarm optimization (PSO) algorithm was used to online auto-tune the optimal proportional gain (<italic>K<sub>P</sub></italic>) and integral gain (<italic>K<sub>I</sub></italic>) value with minimized error voltage. Then, knowing that the controller with fixed gains have limitation in its response during dynamic change, the PSO was improved to allow re-tuning and update the new <italic>K<sub>P</sub></italic> and <italic>K<sub>I</sub></italic> upon step changes or disturbances through a time-variant approach. The proposed controller, online auto-tuned PI using PSO with re-tuning (OPSO-PI-RT) and one-time (OPSO-PI-OT) execution were compared under desired output voltage step changes and load step changes in terms of steady-state error and dynamic performance. The OPSO-PI-RT method was a superior controller with 98.16% accuracy and faster controller with 85.28 s<sup>-1</sup> average speed compared to OPSO-PI-OT using controller hardware-in-the-loop (CHIL) approach.</p> </abstract>

A Disturbance Estimation Approach to Self-Calibration of Gimbal Resolver- to-Digital Conversion System

High-precision position measurement of the gimbal servo system is indispensable to accurate torque output of the control moment gyroscope (CMG). This article addresses the systematic error of the resolver caused by the nonideal output signals of the resolver in the gimbal resolver-to-digital (R/D) conversion system. Due to the limitation of the structure and installation space of the CMG, it is impractical to install additional high-precision sensors to calibrate the raw angular position. Thus, this article proposes a new self-calibration method based on the discrete extended state observer (DESO) to compensate the systematic error of the resolver. The systematic error of the resolver is observed through iterating the resolver systematic estimation error and the compensation table is generated by the output of the DESO. Then, the steady-state error and dynamic performance of the DESO is analyzed and simulated. Finally, the proposed self-calibration method is verified by experiments, which show that the angular position control accuracy is significantly improved, which enhances the speed precision of the gimbal servo system.

An Improved One-Cycle Control Algorithm for a Five-Phase Six-Leg Switching Power Amplifier in Active Magnetic Bearings

To achieve levitation in active magnetic bearing (AMB) systems, levitation coils must be driven by a switch power amplifier (SPA). Therefore, SPA performance is important to stable levitation. In previous studies, five-phase six-leg (5P6L) SPA topology was proposed to reduce hardware costs. However, current tracking performance degrades due to the coupling problem of the five-phase current and freewheeling loss. Then, the rotor may deviate markedly from the balance position, affecting rotor stability. The keys to solving these problems lie in the duty cycle control of the common leg and the compensation of freewheeling loss. This article proposes an improved one-cycle control (OCC) algorithm in which the duty cycle of the common leg is optimized by finite control set model predictive control (FCS-MPC), and the freewheeling loss is compensated for by a high-precision mathematical model. In addition, the parameter uncertainty of the FCS-MPC model is analyzed, and the convergence of the optimization algorithm is investigated. Simulations and experiments show that the proposed algorithm is effective, and the high dynamic response and low steady-state error performance of SPA are achieved.

Generalised predictive controller (GPC) design on single‐phase full‐bridge inverter with a novel identification method

The design of an on-line generalised predictive control (GPC) technique with a novel identification method is presented in this paper for a single-phase full-bridge inverter in the presence of different disturbances. The controller uses system discrete-time model to reach the control variables with a prediction over these values, followed by computing a cost function for control aims. However, in this controller, the need for the mathematical model of the system is removed since the black-box identification strategy is used. Moreover, GPC structure has many advantages including low computational complexity, systematic design procedure, and fixed switching frequency that makes it a good alternative for practical applications. Various disturbances can have a negative impact on a DC–AC inverter; thus, considering robust dynamics and ease of implantation, the GPC scheme is used along with an improved exponential regressive least square identification algorithm as an adaptive strategy in the controller. Moreover, the prediction horizons of this controller have been increased, resulting in its low steady-state error and better performance. Furthermore, harmonics in the sinusoidal signal can decrease the total efficiency of the system; thus, an LC filter is used to reduce the level of total harmonic distortion. However, the stability of the filter is the most challenging issue in designing a suitable controller. Finally, the strength of the current controller is verified using experimental and simulations results.

Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks.

The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. However, in some practical situations, perfect data exchange may not be possible among the nodes. In this paper, we develop a new version of ILMS algorithm, wherein in its adaptation step, only a random subset of the coordinates of update vector is available. We draw a comparison between the proposed coordinate-descent incremental LMS (CD-ILMS) algorithm and the ILMS algorithm in terms of convergence rate and computational complexity. Employing the energy conservation relation approach, we derive closed-form expressions to describe the learning curves in terms of excess mean-square-error (EMSE) and mean-square deviation (MSD). We show that, the CD-ILMS algorithm has the same steady-state error performance compared with the ILMS algorithm. However, the CD-ILMS algorithm has a faster convergence rate. Numerical examples are given to verify the efficiency of the CD-ILMS algorithm and the accuracy of theoretical analysis.

Steady-State Mean-Square Error Performance Analysis of the Tensor LMS Algorithm

In some system identification problems, the weight vector to be estimated can be represented by a tensor product of two low dimensional component vectors. This feature is useful for the design of adaptive filters with fast convergence rate and/or low computational complexity. The tensor LMS algorithm has been proposed under this scenario and its recent extensions find applications in several contexts. This brief analyzes the steady-state mean-square error (MSE) performance of the tensor LMS algorithm, which can provide performance prediction and design guideline for engineers. Since the updates of the weight-vectors of the component adaptive filters (CAFs) are coupled, it is challenging to perform steady-state MSE analysis. To address this problem, this brief first establishes the variance relation in steady state for the CAFs and then decouples their excess MSEs by solving a system of linear equations. Finally, the steady-state MSE of the reproduced adaptive filter is composed of the excess MSEs of the CAFs and the variance of the system noise. Simulations are performed to verify the accuracy of the theoretical findings.

Modified dual output single phase current source back end converter with resilient cyber infrastructure

In this paper, the back end current source converter is designed for supplying two different voltage modes. The front end converter is a diode rectifier, and the back end converter is dual output current source back end converter. The back end converter can operate in two different modes, equal voltage (EV) and different voltage (DV). For the effective control of inverter, the sliding mode control with cyber-physical systems are implemented. To evaluate the performance and ease of control, the comparison of sliding mode and integral sliding mode control (ISMC) is carried out. The control based on ISMC has reduced steady-state error and better performance than SMC. The power converter is integrated to cyber-physical systems (CPS) is introduced. To avoid the complexity by utilizing the multidomain program, LabVIEW based single domain programming is used. The integration of this heterogeneous frame is needed to deploy on the single platform to reduce the data exchange error, which occurs while using a different platform for each section. The cyber-physical test bench is developed based on LabVIEW, MyRIO, C-DAQ, and NI–Web services. The interaction of the model by the cyber integration layer with the physical device is needed for effective control of the system. Web services monitor the performance of the physical device.

Design and Implementation of Mobile Active Two-Axis Solar Tracker with Reflector Based on Particle Swarm Fuzzy Controller

Sun tracking system, or solar tracker, is an electronic device consisting of several electrical and mechanical elements, which serves to guide the solar panels in order to make them follow the sun position accurately and to generate the maximum solar energy reception. Sun tracking system is a solution offered in order to use solar energy optimally. The two-axis solar tracking system can absorb solar energy better than a single axis solar tracking or PV fixed system. The two-axis active sun tracking system uses an LDR (Light Dependent Resistor) sensor in order to capture the powerful solar lighting to be received by PV (photovoltaic). Four LDR sensors are used to represent the position of the sun that is north, south, west, and east. This research applies a modified particle swarm fuzzy-based control to be used in the system so that the solar panel system is more effective and responsive to sunlight position changes. The results show that the control method is capable of applying well with a 70-degree reflector. This can be noticed from the results of the steady-state error performance index in the pitch and the yaw tests that are 0.638% and 0.312%, whereas for the other performance indexes including rise time, settling time and Maximum Overshoot (MOV) for both tests are 7 seconds, 9 seconds, 0% and 4 seconds, 6 seconds and 0% respectively. The mobile active solar tracker built can increase energy gain of 43.01% compared to fixed system.

Dynamic Trajectory Generation and a Robust Controller to Intercept a Moving Ball in a Game Setting

Complex and interactive robot manipulation skills, such as playing a game of table tennis against a human opponent, are a novel problem with multifaceted challenges. Accurate dynamic trajectory generation in order to respond to the tennis ball from the opponent and a novel control scheme for robust and high-performance tracking of the ball in such dynamic situations is a prerequisite to winning the game. In this paper, the dynamic movement primitives (DMPs) are employed for the stable generation of dynamic trajectories in the presence of environmental uncertainties such as ball position and velocity, opponent position and velocity and so on. In DMP, kernels parameters need to be found from expert demonstrations using a learning technique. To mitigate environmental uncertainties by accurate reconstruction of novel dynamic trajectories by combining multiple DMPs, we propose a piecewise linear canonical system (PLCS)-based modified DMP in contrast to the standard exponential canonical system (ECS)-based DMP. To enhance effectiveness and performance, a novel learning technique based on Lyapunov stability is embedded with the above-developed formulation. We show that the proposed learning technique is faster, has the best steady state error performance, and requires almost half the kernels than the state of the art. Next, to intercept the dynamic moving ball in real-time using the DMP, a novel control scheme is needed and designed using fuzzy fractional order sliding mode control. The control scheme results in chatter free tracking of the desired trajectories because the control law is free from sgn( $\cdot $ ) function. The tracking control performance is guaranteed theoretically via the Lyapunov approach and also verified through simulations and experiments. Finally, we have developed a complete system (including a vision system for tracking the ball) using a real 4 degrees-of-freedom (DOFs) Barrett Wam robotic arm and show that the proposed overall framework is able to respond to a hanging ball with high accuracy.

Parameter effect analysis of particle swarm optimization algorithm in PID controller design

PID controller has still been widely-used in industrial control applications because of its advantages such as functionality, simplicity, applicability, and easy of use. To obtain desired system response in these industrial control applications, parameters of the PID controller should be well tuned by using conventional tuning methods such as Ziegler-Nichols, Cohen-Coon, and Astrom-Hagglund or by means of meta-heuristic optimization algorithms which consider a fitness function including various parameters such as overshoot, settling time, or steady-state error during the optimization process. Particle swarm optimization (PSO) algorithm is often used to tune parameters of PID controller, and studies explaining the parameter tuning process of the PID controller are available in the literature. In this study, effects of PSO algorithm parameters, i.e. inertia weight, acceleration factors, and population size, on parameter tuning process of a PID controller for a second-order process plus delay-time (SOPDT) model are analyzed. To demonstrate these effects, control of a SOPDT model is performed by the tuned controller and system response, transient response characteristics, steady-state error, and error-based performance metrics obtained from system response are provided.

Adopting a low hardware redesigned architectural design for educational environment by using DFE-based 5G communication

In this paper, we adopt a proposal to reduce the complexity and increase the throughput in time domain using 5G. This is achieved by speeding up the conventional architecture in advance. Using this method, the number of coefficients of feedback filter is reduced, thus decreasing the hardware complexity. The convergence strategy and steady state error performance are also increased without any additional hardware requirements. This is based on storing the past values of decision maker in a separate look up table and updating it time to time using parallel error multiplexer. Using this method, the results are very efficient by decreasing the power, area and logic utilization of the new architecture. If we include this concept as a hands-on experience, students can learn and also optimize the architecture easily.

Observer-based control of vehicle platoons with random network access

This paper investigates cooperative control of vehicle platoons, focusing on the effect of medium access control (MAC) protocol and unreliable measurement on acceleration information. A Markov chain is used to describe the randomness in vehicular network access under a MAC protocol; a reduced-order observer is proposed to estimate the relative acceleration of neighboring vehicles. Based on stochastic system techniques, a series of sufficient conditions for the existence of the observer-based controller are given in the form of backward recursive Riccati Difference Equations (RDE). A controller–estimator design algorithm is derived to determine the controller and observer gains with which the disturbance of the preceding vehicle acceleration can be eliminated and string stability and zero steady-state velocity error performance can be guaranteed. Both numerical simulations and experiments with laboratory-scale Arduino cars are given to verify the effectiveness and practicability of the proposed algorithm.

Adaptive regularisation for normalised subband adaptive filter: mean‐square performance analysis approach

The normalised subband adaptive filter (NSAF) is a useful adaptive filter, which improves the convergence rate compared with the normalised least mean-square algorithm. Most analytical results of the NSAF set the regularisation parameter set to zero or present only steady-state mean-square error performance of the regularised NSAF (e-NSAF). This study presents a mean-square performance analysis of e-NSAF, which analyses not only convergence behaviour but also steady-state behaviour. Furthermore, a novel adaptive regularisation for NSAF (AR-NSAF) is also developed based on the proposed analysis approach. The proposed AR-NSAF selects the optimal regularisation parameter that leads to improving the performance of the adaptive filter. Simulation results comparing the proposed analytical results with the results achieved from the simulation are presented. In addition, these results verify that the proposed AR-NSAF outperforms the previous algorithms in a system-identification and acoustic echo-cancellation scenarios.

Neural network–based nonaffine control of air-breathing hypersonic vehicles with prescribed performance

This article investigates a novel nonaffine control strategy using neural networks for an air-breathing hypersonic vehicle. Actual actuators are regarded as additional state variables and virtual control inputs are derived from low-computational cost neural approximations, while a new altitude control design independent of affine models is addressed for air-breathing hypersonic vehicles. To further reduce the computational load, an advanced regulation algorithm is applied to devise adaptive laws for neural estimations. Moreover, a new prescribed performance mechanism is exploited, which imposes preselected bounds on the transient and steady-state tracking error performance via developing new performance functions, capable of guaranteeing altitude and velocity tracking errors with small overshoots. Unlike some existing neural control methodologies, the proposed prescribed performance-based nonaffine control approach can ensure tracking errors with preselected transient and steady-state performance. Meanwhile, the complex design procedure of backstepping is also avoided. Finally, simulation results are presented to validate the design.

Improved Optimal Decentralized Load Modulation for Power System Primary Frequency Regulation

Nowadays the interest in smart load technologies for primary frequency regulation is spurred due to the increasing penetration of renewable energy resources. In this paper, an improved optimal load control (improved OLC) is introduced by applying a multiobjective optimization-based gain-tuning method to the conventional OLC approach. The objective is to minimize the frequency nadir, time response, steady-state error, total load shed, and aggregated disutility of controllable loads subject to power balance over the network. Simulation results indicate that enabling a multiobjective optimization-based gain-tuning procedure in the OLC approach can provide better power system frequency regulation. Time-domain analysis confirms the superior performance of improved OLC in terms of frequency nadir (Hz), steady-state error (Hz), control effort, and NERC-based performance metrics (MW/0.1 Hz), with detailed load and wind farm models. Furthermore, small-signal analysis demonstrates that the improved OLC enhances the system closed-loop performance and stability margins by increasing the damping ratio of the system's critical modes.