Iterative Feedback Tuning Algorithm Research Articles

Sensitivity-Based Iterative State-Feedback Tuning for MIMO Systems

In this paper, a new approach to tuning and optimisation of controlled systems regarding the tracking behaviour is presented. This approach can be understood as an extension to the iterative feedback tuning (IFT) approach known from the literature. Motivated by the sensitivity concept, the IFT algorithm is extended to both linear and nonlinear systems in state-space description with static state-feedback control, resulting in the proposed iterative state-feedback tuning (ISFT) method. The main contribution consists of the derivation of first and second-order sensitivity functions for quadratic cost functions, which is typical for control optimisation problems. The proposed approach results in a gradient-based iterative algorithm for control parameter adaptation. Finally, exemplary simulation results will be presented for a nonlinear system, demonstrating the usability of this new approach.

Data-driven fractional order phase-lead and proportional–integral feedback control strategy with application to a reluctance-actuated compliant micropositioning system

This paper aims to overcome complex dynamic problems such as low damping resonant frequency, resonant point drift, and uncertainty of dynamic model of the large stroke micropositioning stage, based on Maxwell reluctance actuator. A composite control method of data-driven fractional phase-lead proportional-integral feedback controller with series hysteresis compensator is proposed. Firstly, the working principle of the Maxwell reluctance actuator is briefly explained, a large stroke flexure micropositioning system is built, and its motor constant and stiffness characteristics are experimentally measured. Secondly, aiming at the rapid phase drop caused by low damping resonance, a data-driven fractional order phase-lead and proportional-integral feedback controller is designed, and the parameters of the fractional order feedback controller are experimentally adjusted based on the iterative feedback tuning algorithm. And the intrinsic hysteresis nonlinearity of the reluctance actuator is firstly compensated by a Prandtl–Ishlinskii model hysteresis compensator which is always placed in the feedback loop of the control system. Then, the proposed controller is compared with other controllers. The experimental results show that this method has a significant suppression effect on high-frequency noise, and the tracking error is significantly reduced, which proves the effectiveness of this stage and control method. • Based on the Maxwell reluctance actuator, a large stroke flexure micropositioning system is built. • A fractional order phase-lead proportional integral controller is proposed, and the integer order approximation of the controller is completed by using oustaloup approximation method • The data-driven method of iterative feedback tuning is used to obtain the parameters of the fractional order controller. • Through the analysis and comparison of linearity, sensitivity function and open-loop frequency response, the advantages of the proposed controller are fully proved.

Throughput Maximization Leveraging Just-Enough SNR Margin and Channel Spacing Optimization

Flexible optical network is a promising technology to accommodate high-capacity demands in next-generation networks. To ensure uninterrupted communication, existing lightpath provisioning schemes are mainly done with the assumption of worst-case resource under-provisioning and fixed channel spacing, which preserves an excessive signal-to-noise ratio (SNR) margin. However, under a resource over-provisioning scenario, the excessive SNR margin restricts the transmission bit-rate or transmission reach, leading to physical layer resource waste and stranded transmission capacity. To tackle this challenging problem, we leverage an iterative feedback tuning algorithm to provide a just-enough SNR margin, so as to maximize the network throughput. Specifically, the proposed algorithm is implemented in three steps. First, starting from the high SNR margin setup, we establish an integer linear programming model as well as a heuristic algorithm to maximize the network throughput by solving the problem of routing, modulation format, forward error correction, baud-rate selection, and spectrum assignment. Second, we optimize the channel spacing of the lightpaths obtained from the previous step, thereby increasing the available physical layer resources. Finally, we iteratively reduce the SNR margin of each lightpath until the network throughput cannot be increased. Through numerical simulations, we confirm the throughput improvement in different networks and with different baud-rates. In particular, we find that our algorithm enables over 20\% relative gain when network resource is over-provisioned, compared to the traditional method preserving an excessive SNR margin.

Iterative Feedback Tuning Algorithm for Tower Crane Systems

This paper proposes validates an Iterative Feedback Tuning (IFT) algorithm, which is a classical and also popular data-driven algorithm, on tower crane systems. The IFT algorithm is applied to improve the performances of three proportional-integral (PI) controllers by solving an optimization problem. The IFT algorithm is validated using experiments to control the cart position, the arm angular position and the payload position. The experiments are conducted on a tower crane system in authors’ laboratory, and the experimental setup is laboratory equipment that has strong nonlinearities. Ten iterations are performed to highlight the control system performance using IFT controllers.

Data-driven iterative tuning based active disturbance rejection control for piezoelectric nano-positioners

The nano scale motions of piezoelectric actuated nano-positioning systems are very sensitive to operating tasks, external disturbances, as well as variations of system dynamics. In this paper, a data-driven Iterative Feedback Tuning (IFT) based Active Disturbance Rejection Control (ADRC) approach is developed to optimize the control performance by conducting controller parameter tuning iteratively from experimental test data. In particular, a parameterized input-output form of the linear ADRC controller is derived for the nano-positioner, where the IFT algorithm is applied to solve the established optimization problem to get the optimal control parameters. The proposed method is verified in simulations where the selection of control criterion and the impact of update step-size are also discussed. Single-axis and dual-axes real-time experiments are finally conducted on an X-Y piezoelectric actuated nano-positioner, which demonstrate significant performance improvement on nano-positioning and tracking over the conventional ADRC method.

Iterative feedback tuning for Hamiltonian systems based on variational symmetry

SummaryThis paper proposes a novel iterative feedback tuning (IFT) for Hamiltonian systems, which can describe a practically important class of nonlinear systems. Hamiltonian systems have a special property called variational symmetry, and it can be used to estimate the input‐output mapping of the variational adjoint for certain input‐output mappings of the systems. First, we derive a modified variational symmetry to adapt to the gradient estimation of an optimal control–type cost function with respect to adjustable parameters of a controller. Second, we provide an IFT algorithm based on the property, which generates the optimal parameters minimizing the cost function by iteration of experiments. The proposed algorithm requires less number of experiments to estimate the gradient than the conventional IFT methods for nonlinear systems. We also provide a method to optimize the elements of the dissipation matrix, which does not directly appear in the Hamiltonian function, by equipping a dynamic feedback of the generalized coordinate. Moreover, we provide an IFT algorithm considering parameter constraints so that the parameters can be optimized within a prescribed search range. Finally, a numerical simulation of a two‐link robot manipulator including a comparison with the conventional IFT methods demonstrates the effectiveness of the proposed method.

Fractional order Proportional-Integral-Derivative Controller parameter selection based on iterative feedback tuning. Case study: Ball Levitation system

In the present research, Iterative Feedback Tuning (IFT) algorithm is employed to tune a type of fractional order Proportional-Integral-Derivative (PID) [Formula: see text] controller. For this purpose, fractional order calculus is introduced and some important principles are represented. Then, IFT algorithm is presented in a general form. Following it, IFT algorithm is derived to tune the specific type of PID. The relevant update law is calculated for the fractional order controller. Finally, the proposed algorithm is tested on a Hardware in Loop (HIL) system, that is, Ball Levitation (BL) system, and the efficiency of the proposed method is verified via experiments.

Control of IPMC Actuators for Microfluidics With Adaptive “Online” Iterative Feedback Tuning

Ionic polymer metal composites (IPMCs) are actuators that lend themselves well to microfluidic applications due to their lightweight, flexibility, ability to tailor their geometry, as well as the capability to be miniaturized and implanted into microelectro-mechanical systems devices. The major issue with implementing IPMCs into such devices is the ability to control their actuation and, hence, their reliability over a long period of time. This paper presents a novel iterative feedback tuning (IFT) algorithm that tunes the system online using experimental data during normal system operation. The controller adaptively tunes the highly nonlinear and time varying IPMC for a newly proposed micropump. This demonstrates the ability of the system to have a reliable performance over a long period of time without the need of any offline tuning or system identification. The system was run for 20 controller updates. This corresponds to 10 and 20 min of operation for the 0.1 and 0.05 Hz reference inputs, respectively. 100 and 300 μm amplitudes were tested to demonstrate the ability of the system to adaptively tune to different input signals. Experimental results show the newly proposed IFT algorithm has successfully tuned the controller to achieve up to 92% better performance when compared with a conventional model-based tuned controller.

Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems

This paper proposes new stability analysis and convergence results applied to the Iterative Feedback Tuning (IFT) of a class of Takagi–Sugeno–Kang proportional-integral-fuzzy controllers (PI-FCs). The stability analysis is based on a convenient original formulation of Lyapunov’s direct method for discrete-time systems dedicated to discrete-time input affine Single Input-Single Output (SISO) systems. An IFT algorithm which sets the step size to guarantee the convergence is suggested. An inequality-type convergence condition is derived from Popov’s hyperstability theory considering the parameter update law as a nonlinear dynamical feedback system in the parameter space and iteration domain. The IFT-based design of a low-cost PI-FC is applied to a case study which deals with the angular position control of a direct current servo system laboratory equipment viewed as a particular case of input affine SISO system. A comparison of the performance of the IFT-based tuned PI-FC and the performance of the PI-FC tuned by an evolutionary-based optimization algorithm shows the performance improvement and advantages of our IFT approach to fuzzy control. Real-time experimental results are included.

Iterative performance improvement of fuzzy control systems for three tank systems

This paper suggests the performance improvement of fuzzy control systems (FCSs) for three tank systems using iterative feedback tuning (IFT). The stable design of Takagi–Sugeno–Kang fuzzy controllers is guaranteed by means of a stability theorem based on LaSalle’s global invariant set theorem formulated for a class of multi input-multi output (MIMO) nonlinear processes. An IFT algorithm characterized by setting the step size to guarantee the FCS stability is proposed. The theoretical approaches are applied in a case study that deals with the IFT-based stable design of fuzzy controllers dedicated to the level control of a cylindrical three tank system as a representative MIMO system. A set of experimental results for a laboratory setup illustrates the performance improvement.

IFTtune: a PID automatic tuning software tool

The regulatory layer of every process industry is made up of many decentralized loops using, almost exclusively, PID controllers. The task of keeping each of these devices correctly tuned is key to the efficient and safe operation of the factory but very demanding on the time of technical and maintenance personnel should it be carried out manually. The tool here described is designed to perform the automatic tuning of PID regulators facilitating its application in an industrial setting. The regulator parameters are updated starting from their current values so that a performance index is locally optimized using the Iterative Feedback Tuning algorithm. The tool is easily configurable and uses the widely adopted OPC industrial communication standard.

Application of IFT and SPSA to Servo System Control

This paper treats the application of two data-based model-free gradient-based stochastic optimization techniques, i.e., iterative feedback tuning (IFT) and simultaneous perturbation stochastic approximation (SPSA), to servo system control. The representative case of controlled processes modeled by second-order systems with an integral component is discussed. New IFT and SPSA algorithms are suggested to tune the parameters of the state feedback controllers with an integrator in the linear-quadratic-Gaussian (LQG) problem formulation. An implementation case study concerning the LQG-based design of an angular position controller for a direct current servo system laboratory equipment is included to highlight the pros and cons of IFT and SPSA from an application's point of view. The comparison of IFT and SPSA algorithms is focused on an insight into their implementation.

Position control of an Ionic Polymer Metal Composite actuated rotary joint using Iterative Feedback Tuning

Ionic Polymer Metal Composites (IPMCs) are a novel material that has been the subject of considerable interest over recent decades because of their unique electrochemical and mechanical properties which allow them to be used as smart transducers. However, there has been insufficient research to determine if the electro-active polymer can reliably actuate common engineering mechanisms due to its nonlinear and time-variant nature. This paper explores a model-free approach for controlling the position of an IPMC actuated rotary linkage for micro-manipulation. The mechanism was developed based on the mechanical characteristics of the IPMC actuators. A Proportional, Integral (PI) controller was initially developed and tested to control the tip displacement of the mechanism. Test results show that this classical controller is capable of actuating the rotary mechanism to microscopic deflections but would not completely stabilise at the steady state position. An adaptive, nonlinear tuning method called Iterative Feedback Tuning (IFT) was developed to tune the performance of the PI controller. Empirical results show that the new control scheme improved the steady state response. However, the enhancement of the transient response could not be definitively validated as solely the work of the IFT algorithm due to the time-variant and variable response behaviour of IPMCs.

Gain scheduled control of IPMC actuators with ‘model-free’ iterative feedback tuning

IPMCs are actuators with significant potential in robotics and biomedical applications. In order to make full use of their potential, their complex actuation behaviour must be effectively controlled. Previous efforts to control these actuators relied heavily on the development of an accurate model of the IPMC. After 15 years of modelling research, not one such accurate model exists. This paper presents a nonlinear controller which is adaptively tuned using a model free approach called iterative feedback tuning (IFT). Automatic tuning of the controller, without the any knowledge of the system is achieved. By eliminating the need for a plant model, the developed control system represents major progress towards the implementation of IPMCs into real world applications. The controller is tuned to achieve high position accuracy on both the micro and macro scale, where most previous controllers limit their operating range to remain in a small linear region. The proposed controller continuously adapts its parameters to handle the IPMC nonlinearity. The IFT algorithm has successfully tuned the control parameters with a performance increase of up to 65% from an initial arbitrary un-tuned controller. Experimental results show the newly proposed controller achieves much higher performance when compared with a conventional linear controller.

Design and Experiments for a Class of Fuzzy Controlled Servo Systems

This paper proposes a new fuzzy control solution employing 2-DOF proportional-integral-fuzzy controllers dedicated to a class of servo systems. The controlled plants in these systems, widely used in mechatronics applications, can be characterized by second-order dynamics with integral type. The original design method suggested here starts with linear design results in terms of the extended symmetrical optimum method accompanied by an iterative feedback tuning (IFT) algorithm. Next, these results are transferred to the fuzzy case by the modal equivalence principle. The convergence of the IFT algorithm is guaranteed by the derivation of sufficient global asymptotic stability conditions based on Krasovskii-LaSalle's invariant set theorem with quadratic Lyapunov function candidate. Real-time experimental results corresponding to a low-cost fuzzy controlled servo system validate the theoretical approaches.

Iterative Feedback Tuning for Hamiltonian Systems

This paper is concerned with iterative feedback tuning for Hamiltonian systems. Hamiltonian systems have a property called variational symmetry which can be used to estimate the input-output mapping of the variational adjoint for certain input-output mappings of the systems. Here this property is utilized for estimating the gradient of an optimal control type cost function with respect to the design parameters of the controllers. This allows one to obtain an iterative feedback tuning algorithm for Hamiltonian systems which generates the optimal parameters by iteration of experiments. The proposed algorithm requires less number of experiments to estimate the gradient and can be used with the iterative learning control proposed previously. Furthermore, numerical simulations demonstrate the effectiveness of the proposed method.

A signal convolution method for estimation of controller parameter sensitivity functions for tuning of feedback control systems by an iterative process

Optimisation of controller parameters can be achieved through the use of parameter sensitivity functions in an iterative tuning process. Unlike some other methods for the generation of sensitivity functions, the approach described in this paper avoids the need for explicit a priori knowledge of the plant model structure or parameters, systematic adjustments of controller parameters being made entirely through the processing of signals obtained from tests on the closed-loop system. The methods, based on convolution, may be applied to the estimation of controller parameter sensitivity functions in linear closed-loop systems using simple test inputs, such as step signals. This approach differs from the Iterative Feedback Tuning algorithm in that it does not involve repeated experiments involving system outputs from one test being used as reference inputs in a second test. The signal convolution approach is illustrated through an example involving a simple two-input two-output liquid-level control system involving two coupled tanks.

Iterative feedback tuning for internal model controllers

In this paper, the applicability of iterative feedback tuning (IFT) to internal model controllers (IMCs) and smith predictors is examined. IFT is a model-free gradient descent controller tuning tool; refer to Hjalmarsson et al. (IEEE Control Systems Mag. 18 (1998) 26) and Hjalmarsson et al. (Proceedings of the Conference on Decision and Control, Orlando, FL, 1994, pp. 1735) for further details. It is shown that the IFT algorithm can be modified to tune IMCs and, more in particular, Smith predictors. The main difference with the original algorithm is that the cost gradient is obtained by doing four experiments (two recycling experiments) instead of three (one recycling experiment) for conventional controllers.

Relay auto-tuning of PID controllers using iterative feedback tuning

In this paper, ideas from iterative feedback tuning (IFT) are incorporated into relay auto-tuning of the proportional-plus-integral-plus-derivative (PID) controller. The PID controller is auto-tuned to give specified phase margin and bandwidth. Good tuning performance according to the specified bandwidth and phase margin can be obtained and the limitation of the standard relay auto-tuning technique using a version of Ziegler–Nichols formula can be eliminated. Furthermore, by using common modelling assumptions for the relay system, some of the required derivatives in the IFT algorithm can be derived analytically. The algorithm was tested in the laboratory on a coupled tank and good tuning result was demonstrated.

Iterative feedback tuning—an overview

AbstractAdaptive and iterative control algorithms based on explicit criterion minimization are briefly reviewed and an overview of one such algorithm, iterative feedback tuning (IFT), is presented. The basic IFT algorithm is reviewed for both single‐input/single‐output and multi‐input/multi‐output systems. Subsequently the application to non‐linear systems is discussed. Stability and robustness aspects are covered. A survey of existing extensions, applications and related methods is also provided. Copyright © 2002 John Wiley & Sons, Ltd.