Adaptive Gain Tuning Research Articles

Signal Compensation Control With DDPG-Based Tuning Strategy for Electronic Throttle System

The electronic throttle system (ETS) is one of the important components of an automobile engine that adjust the engine air intake to directly affect the combustion power of the engine. In practical scenarios, Proportional-Integral-Derivative (PID) is a common and effective method. However, the traditional PID is difficult to satisfy the ETS performance requirements, e.g., the system response overshoot is not allowed to exist. Moreover, the controller gains are also difficult to tune manually. To tackle the above-mentioned issues, we propose a signal compensation control framework with an adaptive gain tuning strategy for the electronic throttle system, which consists of an upper-level to tune controller gains and a lower-level signal compensation controller to eliminate the nonlinear terms. The main feature of the signal compensation controller relies on two nonlinear compensators to remove the strong nonlinearity and uncertainty in the actual throttle system. Furthermore, we develop an adaptive gain tunning strategy based on Deep Deterministic Policy Gradient (DDPG) algorithm, and we analyze the corresponding stability and convergence properties of the proposed method. Finally, we illustrate the proposed algorithm on benchmarks and the tracking control problem using the real-world experiment platform of the ETS to show the effectiveness of the proposed algorithm.

Adaptive Gain Tuning Rule for Nonlinear Sliding-Mode Speed Control of Encoderless Three-Phase Permanent Magnet Assisted Synchronous Motor

In this paper, an adaptive gain tuning rule is designed for the nonlinear sliding mode speed control (NSMSC) in order to enhance the dynamic performance and the robustness of the permanent magnet assisted synchronous reluctance motor (PMa-SynRM) with considering the parameter uncertainties. A nonlinear sliding surface whose parameters are altering with time is designed at first. The proposed NSMSC can minimize the settling time without any overshoot via utilizing a low damping ratio at starting along with a high damping ratio as the output approaches the target set-point. In addition, it eliminates the problem of the singularity with the upper bound of an uncertain term that is hard to be measured practically as well as ensures a rapid convergence in finite time, through employing a simple adaptation law. Moreover, for enhancing the system efficiency throughout the constant torque region, the control system utilizes the maximum torque per ampere technique. The nonlinear sliding surface stability is assured via employing Lyapunov stability theory. Furthermore, a simple sliding mode estimator is employed for estimating the system uncertainties. The stability analysis and the experimental results indicate the effectiveness along with feasibility of the proposed speed estimation and the NSMSC approach for a 1.1-kW PMa-SynRM under different speed references, electrical and mechanical parameters disparities, and load disturbance conditions.

Improved Adaptive PSO-based Gain Tuning for PID Controllers in Utility Boilers

In this paper, the stabilization and set point tracking of the utility boiler is achieved via the Proportional-Integral-Derivative (PID) controller, tuned using the versions of metaheuristic Particle Swarm Optimization (PSO) algorithms. The utility boiler model dynamics are based on the Bell and Astrom boiler turbine system. Initially, the non-linear mathematical model is obtained using the first principles data from the plant model. Further, Taylor series approximation is applied to linearize the non-linear equations around the equilibrium point to implement a stable controller for the plant. Finally, the PID controller gains for the plant model are determined using the PSO algorithms to achieve the desired response. For tuning the PID controller, three PSO algorithms namely standard PSO, Adaptive PSO (APSO) and Improved Adaptive PSO (IAPSO) are proposed. The performance of the proposed PSO based PID control algorithms are evaluated using error metrics such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). The simulation results show that the proposed APSO and IAPSO algorithms have improved performance when compared to regular PSO algorithms and are capable of optimizing the control parameters.

Fully Distributed Optimal Position Control of Networked Uncertain Euler-Lagrange Systems Under Unbalanced Digraphs.

The distributed optimal position control problem, which aims to cooperatively drive the networked uncertain nonlinear Euler-Lagrange (EL) systems to an optimal position that minimizes a global cost function, is investigated in this article. In the case without constraints for the positions, a fully distributed optimal position control protocol is first presented by applying adaptive parameter estimation and gain tuning techniques. As the environmental constraints for the positions are considered, we further provide an enhanced optimal control scheme by applying the ϵ -exact penalty function method. Different from the existing optimal control schemes of networked EL systems, the proposed adaptive control schemes have two merits. First, they are fully distributed in the sense without requiring any global information. Second, the control schemes are designed under the general unbalanced directed communication graphs. The simulations are performed to verify the obtained results.

Adaptive Model-Free Gain Tuning for Active Damping of Machine Tool Vibrations

Adaptive Model-Free Gain Tuning for Active Damping of Machine Tool Vibrations

Distributed Adaptive and Resilient Control of Multi-Robot Systems With Limited Field of View Interactions

In this letter, we consider two coupled problems for distributed multi-robot systems (MRSs) coordinating with on-board sensors: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rejection of sensor attacks and adaptive tuning of interaction gains</i> . First, MRSs with on-board sensors can be more susceptible to sensor attacks, and therefore must be resilient to such attacks. Second, a typical shortcoming of distributed control frameworks (e.g., potential fields) is that the overall system behavior is highly sensitive to the gain assigned to relative interactions. Therefore, we derive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> control protocols by employing a static output-feedback technique, guaranteeing bounded <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> gains of the error induced by the attack (fault) signals for a potential-based control framework developed for the particular application of limited fields of view (FOVs) control of MRS with additive sensor and actuator attacks (or faults). Moreover, to overcome the shortcomings of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general</i> potential-based control framework in the first place, we also apply an adaptive gain tuning scheme based on satisfying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nominal pairwise interactions</i> , which yields a dynamic balancing of interaction strengths in a robot’s neighborhood. Finally, simulation results using ROS Gazebo are provided to support our theoretical findings.

Adaptive PID computed-torque control of robot manipulators based on DDPG reinforcement learning

This paper presents a design of an adaptive PID gain tuning based on deep deterministic policy gradient reinforcement learning agent for PID computed-torque control of robot manipulators, taking the presence of unmodelled dynamics and external disturbances into consideration. The proposed approach adaptively computes the outer-loop PID controller gains, that minimise trajectory tracking errors and reject disturbances, while the closed-loop dynamics remain stable. Since the control scheme requires the knowledge of the robot's dynamics, both kinematic and dynamic equations of n-link serial manipulator are developed. The agent is implemented on UR5e robot manipulator model, using the most valid dynamic and kinematic parameters provided by the manufacturer and related works. Simulation results show that the proposed approach is robust against bounded internal and external disturbances, and achieves a good trajectory tracking performance, due to the adaptability of gain tuning over the conventional PID controller.

Adaptive control to actively damp bistabilities in highly interrupted turning processes using a hardware-in-the-loop simulator

Interruptions in turning make the process forces non-smooth and nonlinear. Smooth nonlinear cutting forces result in the process of being stable for small perturbations and unstable for larger ones. Re-entry after interruptions acts as perturbations making the process exhibit bistabilities. Stability for such processes is characterized by Hopf bifurcations resulting in lobes and period-doubling bifurcations resulting in narrow unstable lenses. Interrupted turning remains an important technological problem, and since experimentation to investigate and mitigate instabilities are difficult, this paper instead emulates these phenomena on a controlled hardware-in-the-loop simulator. Emulated cutting on the simulator confirms that bistabilities persist with lobes and lenses. Cutting in bistable regimes should be avoided due to conditional stability. Hence, we demonstrate the use of active damping to stabilize cutting with interruptions/perturbations. To stabilize cutting with small/large perturbations, we successfully implement an adaptive gain tuning scheme that adapts the gain to the level of interruption/perturbation. To facilitate real-time detection of instabilities and their control, we characterize the efficacy of the updating scheme for its dependence on the time required to update the gain and for its dependence on the levels of gain increments. We observe that higher gain increments with shorter updating times result in the process being stabilized quicker. Such results are instructive for active damping of real processes exhibiting conditional instabilities prone to perturbations.

Sensor fusion algorithms for orientation tracking via magnetic and inertial measurement units: An experimental comparison survey

Lightweight and low-cost wearable magnetic and inertial measurement units (MIMUs) have found numerous applications, such as aerial vehicle navigation or human motion analysis, where the 3D orientation tracking of a rigid body is of interest. However, due to the errors in measurements of gyroscope, accelerometer, and/or magnetometer inside a MIMU, numerous studies have proposed sensor fusion algorithms (SFAs) to estimate the 3D orientation accurately and robustly. This paper contributes to these efforts by performing an experimental comparison among a variety of SFAs. Notably, we compared the estimated orientation of 36 SFAs from the complementary filter and linear/extended/complementary/unscented/cubature Kalman filter families with the reference orientation obtained from a camera motion-capture system. The experimental study included data collection with a foot-worn MIMU where nine participants performed various short- and long-duration tasks. We shared the codes and sample of data in https://www.ncbl.ualberta.ca/codes to enable other researchers to compare their works with the literature toward creating a comprehensive online repository for SFAs. To perform a fair comparison, we used the Particle Swarm Optimization routine to find the optimal adaptive gain tuning scheme for each SFAs, as recommended in the literature. Our experimental results showed that gyroscope static bias removal, in general, showed to be effective in reducing the estimation error of SFAs, specifically during long-duration trials. Moreover, our experimental results identified the SFAs with the highest accuracy from each family. We also reported the execution times for the selected SFAs from each family. This paper is among the first experimental comparison studies which provide such breadth of coverage across various SFAs for tracking orientation with MIMUs.

Part 1: robust adaptive control of quadrotor with disturbance observer

PurposeThe aim of this paper is to design a nonlinear disturbance observer-based control (DOBC) method obtained by patching a control method developed using a robust adaptive technique and a DO.Design/methodology/approachFor designing a DOBC, initially a class of nonlinear system is considered with an external disturbance. First, a DO is designed to estimate the external disturbances. This estimate is combined with the controller to reject the disturbances and obtain the desired control objective. For designing a controller, the robust sliding mode control theory is used. Furthermore, instead of using a constant switching gain, an adaptive gain tuning criterion is designed using Lyapunov candidate function. To investigate the stability and effectiveness of the developed DOBC, stability analysis and simulation study are presented.FindingsThe major findings of this paper include the criteria of designing the robust adaptive control parameters and investigating the disturbance rejection when robust adaptive control based DOBC is developed.Practical implicationsIn practice, the flight of quadrotor is affected by different kind of external disturbances, thus leading to the change in dynamics. Hence, it is necessary to design DOBCs based on robust adaptive controllers such that the quadrotor model adapts to the change in dynamics, as well as nullify the effect of disturbances.Originality/valueDesigning DOBCs based on robust control method is a common practice; however, the robust adaptive control method is rarely developed. This paper contributes in the domain of DOBC based on robust adaptive control methods such that the behavior of controller varies with the change in dynamics occurring due to external disturbances.

Enhanced PI control and adaptive gain tuning schemes for distributed secondary control of an islanded microgrid

This paper develops an enhanced proportional-integral distributed control scheme (EPI-DCS) to regulate the frequency and voltage of a droop-controlled microgrid and share the power mismatch, simultaneously. The proposed EPI-DCS is designed by using the control Lyapunov function method and adding a new consensus-based term to the integrand dynamic of the conventional PI control. In the proposed distributed EPI-DCS, the distributed generation units intermittently exchange information with the neighbouring distributed generation units, through a communication network. Considering the communication network time delays, the stability of the proposed EPI-DCS is examined using the Lyapunov–Krasovskii linear matrix inequality conditions, and the maximum stable time delay is calculated. In order to stabilise the system for large destabilising time delays, an adaptive gain scheme is proposed. Effectiveness of the proposed adaptive EPI-DCS is validated by numerical simulations with detailed models of the components and the converters, including load change, distributed generation outage, and adaptive gain-scheduling against destabilising communication network time delays.

40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: Methods, lessons learned, and future challenges

Technological developments over the past two decades have resulted in the development of more accurate and lightweight low-cost magnetic and inertial measurement units (MIMUs). These developments have allowed the extensive application of MIMUs in various fields, specifically tracking the 3D orientation of a rigid body. Despite recent technological improvements, measurements from a tri-axial gyroscope, accelerometer, and/or magnetometer inside the MIMU are characterized by uncertainties. Numerous studies have been conducted to address these uncertainties and develop sensor fusion algorithms (SFAs) to estimate the 3D orientation accurately and robustly. This paper contributes to these efforts by providing a survey of the state-of-the-art SFAs for orientation estimation. We surveyed +250 publications, categorized the SFAs with various structures, identified the modifications proposed to improve their performance, and discussed the strengths and weaknesses of these approaches. We found that, while early SFAs were mostly a vector observation algorithm or an extended Kalman filter, to improve the computational efficiency, more recent works have developed SFAs with a complementary filter or complementary Kalman filter structure. At the same time, to improve the performance of the SFAs, several research teams have proposed various modifications to the basic structure of these filters, such as adaptive gain tuning or imperfect measurement rejection. We also provided an outlook on the lessons learned as well as the main challenges related to SFAs and discussed the practical steps toward developing an effective SFA. We have identified the need for benchmarking studies as the main challenge at the moment. This paper is among the first surveys which provide such breadth of coverage across different SFAs for tracking orientation with MIMUs.

Adaptive Gain Regulation of Sensor Fusion Algorithms for Orientation Estimation with Magnetic and Inertial Measurement Units

Magnetic and inertial measurement units (MIMUs) proved to be an accurate alternative for optic, electromagnetic, or acoustic measurement technologies. While the orientation of the MIMU could be estimated using accelerometer, gyroscope, or magnetometer sensors alone, many studies proposed sensor fusion algorithms (SFAs) to overcome the drawbacks that appear when each sensor is used individually. However, the performance of such SFAs highly depends on their gains, and poor initialization or incorrect adjustment of the gains would degrade the SFAs’ performance. Therefore, this article proposes a general framework to find the optimal adaptive gain tuning scheme for Kalman filters and complementary filters to achieve accurate and robust orientation estimation with MIMUs. To this end, we proposed an innovative optimization framework to find the fixed optimal gain of an SFA or the optimal adaptive gain regulation scheme. Also, we demonstrated that the designed adaptive gain regulation scheme (a hard switch with two or three levels or a fuzzy inference system) is essential for orientation tracking with various SFAs. We measured the thigh, shank, and foot motion of nine participants while performing various activities using MIMUs and a camera motion-capture system to calculate the MIMUs’ error in 3-D angle estimations. Gain regulation by hard switch was significantly ( $p ) more accurate and robust than it was for innovation adaptive estimation. Also, for all tested SFAs, hard switching for shank and foot MIMUs was significantly more accurate or robust than that for fixed optimal gain. Our experimental results showed that the adaptive gain tuning of SFAs using optimized gains is crucial, regardless of the algorithm structure or complexity.

Autonomous load sharing technique in solar photovoltaic system-based DC microgrid

In this article, efforts have been made to study, analyse and implement the autonomous current sharing technique by using two parallel-connected Solar Photovoltaic (SPV) systems with a maximum power point tracking controller (MPPT) considering cable resistances. It mainly formulates a procedure to minimise the current sharing mismatch across the load by controlling the duty cycle of MPPT controller. Also, an analysis of various factors affecting (SPV) characteristics has been reported. Further, the system has been analysed with two interconnected SPV systems to a common load, and an accurate load sharing phenomenon has been achieved by using adaptive gain tuning technique. The proposed scheme has been verified by conducting the experiments, and its results have been explored for various irradiation and load conditions. Experimental results reveal that at all conditions current from the sources are equally shared to loads and adequate rest is delivered to energy storage systems.

Compensation of Parameter Uncertainty Using an Adaptive Sliding Mode Control Strategy for an Interior Permanent Magnet Synchronous Motor Drive

This paper designs an adaptive sliding mode speed controller (ASMSC) that can compensate for parameter uncertainty of an interior permanent magnet synchronous motor (IPMSM) drive. Unlike the previous control systems, the proposed ASMSC guarantees a precise speed tracking capability in the presence of severe parameter variations without accurate knowledge on the motor parameter values and uncertainty bounds. In particular, the proposed adaptive switching gain tuning (ASGT) term can effectively solve the excessive input energy consumption problem due to an unnecessarily overestimated switching gain. Next, the convergence and stability analysis are proven through a Lyapunov function. The feasibility of the proposed approach is verified via experimental results using a prototype IPMSM test bed with a TI TMS320F28335 digital signal processor. In this paper, the proposed SMSC is investigated with two control terms: without an ASGT term and with an ASGT term. In addition, the conventional proportional-integral speed controller and conventional linear matrix inequalities based sliding mode speed controller are chosen for the performance comparison. Finally, the proposed ASMSC can assure more robust and faster speed performances against the parameter uncertainty under three dynamic load conditions.

Decentralized Adaptive Double Integral Sliding Mode Controller for Multi-Area Power Systems

Most of the existing results for load frequency control of multi-area interconnected power systems can only be obtained when the norm of the aggregated uncertainties is bounded by a positive constant. This condition is difficult to achieve in real multi-area interconnected power systems. In this paper, a new load frequency control (LFC) for multi-area interconnected power systems is developed based on a decentralised adaptive double integral sliding mode control technique where the above limitation is eliminated. First, an adaptive gain tuning law is adopted to estimate the unknown upper bound of the aggregated uncertainties. Second, a double integral sliding surface based adaptive sliding mode controller is proposed to improve the transient performance of the closed loop system. Simulation results show that the proposed control law results in shortening the frequency’s transient response, avoiding the overshoot, rejecting disturbance better, maintaining required control quality in the wider operating range, and being more robust to uncertainties as compared to some existing control methods.

Robust global second-order sliding mode control with adaptive parameter-tuning law for perturbed dynamical systems

This paper offers an adaptive global second-order sliding surface for perturbed dynamical systems with matched and unmatched external disturbances. Based on the composite nonlinear feedback theory, a nonlinear function Ψ( x1) is employed in the switching surface to adjust the damping ratio of the controlled system. Initially, using a constant gain matrix, a small value of the damping ratio is achieved presenting a fast performance. As the system state converges to the origin, the damping ratio of the closed-loop system is increased that yields fast settling time and a low overshoot. A novel smooth and chattering-free controller is proposed to fulfill the removal of the reaching mode and the existence of the sliding behavior around the surface from the beginning. The adaptive gain tuning controller reduces the required information for the upper bound of disturbances. Moreover, the selection of the constant parameters μ1 and τ1 for the control law is turned into a cost function and solved by a particle swarm optimization method. Several criteria are derived to assure the robust stability of the uncertain dynamical system. Some simulations are offered to demonstrate the effectiveness of the planned technique.

A Nonlinear Sliding Mode Controller for IPMSM Drives with an Adaptive Gain Tuning Rule

This paper presents a nonlinear sliding mode control (SMC) scheme with a variable damping ratio for interior permanent magnet synchronous motors (IPMSMs). First, a nonlinear sliding surface whose parameters change continuously with time is designed. Actually, the proposed SMC has the ability to reduce the settling time without an overshoot by giving a low damping ratio at the initial time and a high damping ratio as the output reaches the desired setpoint. At the same time, it enables a fast convergence in finite time and eliminates the singularity problem with the upper bound of an uncertain term, which cannot be measured in practice, by using a simple adaptation law. To improve the efficiency of a system in the constant torque region, the control system incorporates the maximum torque per ampere (MTPA) algorithm. The stability of the nonlinear sliding surface is guaranteed by Lyapunov stability theory. Moreover, a simple sliding mode observer is used to estimate the load torque and system uncertainties. The effectiveness of the proposed nonlinear SMC scheme is verified using comparative experimental results of the linear SMC scheme when the speed reference and load torque change under system uncertainties. From these experimental results, the proposed nonlinear SMC method reveals a faster transient response, smaller steady-state speed error, and less sensitivity to system uncertainties than the linear SMC method.

Design and Evaluation of Simple Ship's Automatic Maneuvering System Using Sliding Mode Controller

In this paper, we present a simple automatic ship's maneuvering system using sliding mode controller. In this system, the input chattering in sliding mode control is reduced by introducing the gain scheduling method and the adaptive gain tuning method. Moreover, the ideal target course is generated using the Hermite interpolation method based on the minimum-time maneuvering solution. In order to evaluate the performance of the proposed system, computer simulation and actual sea tests carried out using small training ship Shioji-Maru under various conditions. As a result, we can see that the control performance is improved under all experimental conditions by comparing the proposed two systems and PID control which has been used traditionally to automatic maneuvering of the ship.

Motion Control of Adaptive Truss Structures Using Fuzzy Rules

Abstract: This note describes a design of the fuzzy control system for the kinematic maneuvers of an adaptive system consisting of a truss‐type structure with several variable‐length members. The deformations of the adaptive modules are coordinatively governed by fuzzy controllers according to the distance signals between the endpoint of the structure and the moving target. In addition, an adaptive gain tuning technique is investigated in this study. The advantages of applying adaptive gain tuning on motion control of adaptive truss structures are presented. The simulation results show that the application to this model is effective and can be easily implemented in the microcomputer.