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It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning parameters to achieve satisfactory performance with the selected approach. In this context, the automatic design of algorithms, particularly those based on heuristics, has been increasing in popularity in the previous years due to its undoubted relevance nowadays. This paper explores a novel approach based on hyper-heuristics to carefully select population-based search operators and their tuning parameters to generate metaheuristics capable of dealing with a given practical engineering problem. The proposed strategy is assessed using three highly relevant and illustrative problems: training Artificial Neural Networks, designing PID controllers, and modeling a calorimetric phenomenon based on fractional calculus. In addition, we implement three well-known optimization metaheuristics to compare achieved solutions via the proposed hyper-heuristic strategy, namely Particle Swarm Optimization, Genetic Algorithm, and Cuckoo Search. Results from extensive numerical tests prove that the customized metaheuristics are generally superior to the three well-known algorithms, taking only a few iterations to converge to an optimal solution. This is an excellent indicator of alleviating the effort and expertise required to choose the proper methodology when dealing with real-valued optimization problems.

This article presents a method for calculating all the proportional–integral/proportional–derivative controllers with a low-pass filter of a given order, which satisfy gain–phase margins and sensitivity (or complementary sensitivity) specifications. The method is explicit and requires solving a set of a polynomial and two linear equations. It is shown how to extend the method to design all the proportional–integral–derivative or cascaded controllers with a low-pass filter assuming that a minimum value of one of the controller components is given. [ABSTRACT FROM AUTHOR]

This article presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modeled using Gaussian processes. The safety requirement is imposed via a barrier-like term in the objective, which is introduced to account for operational changes in the system. We further introduce a data-driven constraint that captures the stability requirements from system data. Numerical evaluation shows that the proposed approach outperforms relay feedback autotuning and quickly converges to the global optimum, thanks to a tailored stopping criterion. We demonstrate the performance of the method through simulations and experiments. For experimental implementation, in addition to the introduced safety constraint, we integrate a method for automatic detection of the critical gains and extend the optimization objective with a penalty depending on the proximity of the current candidate points to the critical gains. The resulting automated tuning method optimizes system performance while ensuring stability and standardization. [ABSTRACT FROM AUTHOR]

In this article, a speed control scheme based on an analytically designed fractional-order PID with Bode’s ideal cutoff (FOPID-BICO) filter is proposed. The FOPID controller is designed to track the speed references and the BICO suppress is applied to filter the high-frequency noise. Considering the difficulty of parameters tuning of the FOPID controller, a comprehensive analytical design method based on frequency specifications-defined loop-shaping for the FOPID-BICO controller is first proposed for the permanent magnet synchronous motor speed control. The designed speed servo system satisfies five frequency-domain specifications: gain crossover frequency, phase margin, phase crossover frequency, gain margin, and a “flat phase” constraint. Moreover, the influence of phase crossover frequency on control system is fully exposed through the frequency-domain analysis. The proposed designed method ensures that the control system is robust to loop gain variations and guarantees the optimal performance on rejecting noise in high-frequency and disturbance in low-frequency. The effectiveness of the proposed FOPID-BICO controller has been verified by experimental results. [ABSTRACT FROM AUTHOR]

This paper reviews the novel concept of a controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic properties, and offers useful extensions and applications. ControlVAE is a new variational autoencoder (VAE) framework that combines automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) controller, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows us to precisely control the KL-divergence to a desired value (set point) that is effective in avoiding posterior collapse and learning disentangled representations. While prior work developed alternative techniques for controlling the KL divergence, we show that our PI controller has better stability properties and thus better convergence, thereby producing better disentangled representations from finite training data. In order to improve the ELBO of ControlVAE over that of the regular VAE, we provide a simplified theoretical analysis to inform the choice of set point for the KL-divergence of ControlVAE. We evaluate the proposed method on three tasks: image generation, language modeling, and disentangled representation learning. The results show that ControlVAE can achieve much better reconstruction quality than the other methods for comparable disentanglement. On the language modeling task, our method can avoid posterior collapse (KL vanishing) and improve the diversity of generated text. Moreover, it can change the optimization trajectory, improving the ELBO and the reconstruction quality for image generation. [ABSTRACT FROM AUTHOR]

This article is concerned with the extended dissipative fuzzy proportional–integral–derivative (PID) control problem for nonlinear systems subject to controller parameter perturbations over a class of mixed fading channels. The sensors of plant are divided into two groups according to engineering practice, where the individual sensor group transmits the measurements to the controller via a respective communication channel undergoing specific fading effects. Considering the complicated nature of the signal fading with the transmission channels, two stochastic models (i.e., the independent and identically distributed fading model and the Markov fading model) are simultaneously employed to describe the mixed fading effects of the two communication channels corresponding to the two sensor groups. The objective of this article is to design a nonfragile PID controller such that the closed-loop system is exponentially stable in mean square and extended stochastically dissipative. With the assistance of the Lyapunov stability theory and stochastic analysis method, sufficient conditions are obtained to analyze the system performance. Then, within the established theoretical framework, an iterative optimization algorithm is proposed to design the desired controller parameters by using the convex optimization technique. Finally, two simulation examples are given to verify the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

This article reports on the design, modeling, control, and testing of a novel compliant nanopositioning stage driven by a self-developed normal-stressed electromagnetic actuator. To facilitate the parameter selection for the stage to achieve the desired stroke and natural frequency, an analytical model of both the electromagnetic circuit and flexure mechanism is established, which is then systematically verified through finite element analysis. By combining a proportional—integral—differential (PID)-based main controller with a system dynamics inversion-based feedforward compensator, a closed-loop control system for the stage is constructed with the main controller being tuned through Bode’s ideal transfer function-based loop-shaping method. The experimental result shows that a stroke of ${\pm } 95 ~\mu \text{m}$ and a first natural frequency of 743 Hz are achieved for the designed stage. Finally, taking advantage of the constructed control system, the nanopositioning capability is demonstrated by finely tracking the harmonic and nanostair commands. Note to Practitioners—Piezoelectric actuators (PEAs) and voice coil motors (VCMs) are commonly adopted for the actuation of nanopositioning stages. In general, constrained by the inherent small strain, PEA is more suitable for the generation of dynamic motions in dozens of micrometers. Meanwhile, VCM has a theoretically infinite motion range but a slow response due to its low force density nature. Taking advantage of a newly developed normal-stressed electromagnetic actuator (NSEA), we demonstrate the design, modeling, control, and testing of an NSEA-based nanopositioning stage with a relatively long motion range, high bandwidth, and compact size. Considering the high force density and relatively large motion range only constrained by the effective air gap, the NSEA-based stage is demonstrated herein to be very promising to achieve highly dynamic motions within hundreds of micrometers. [ABSTRACT FROM AUTHOR]

The speed and radial displacement of the rotor have the obvious ripples due to the strong coupling between torque and suspension force of a single-winding bearingless flux-switching permanent magnet motor, and the traditional PID/PI controller is difficult to suppress the disturbance of the unbalanced magnetic force. A linear active disturbance rejection control method based on model compensation is proposed in this article. First, the topology and operation principle of the motor are described, and then the dynamic model of the rotor and flux linkage model of the motor are analyzed. Accordingly, the integral-series standard mathematical models of the displacement and speed are deduced, and then the mathematical expressions of the controller gain and radial disturbance are obtained. Second, the linear extended state observer with known model perturbation information and the linear feedback control law are designed, and the parameters tuning method is given. Finally, the simulation and experimental results verified that the proposed method can significantly reduce the rotor displacement and torque ripples, and it has better disturbance rejection performance than the traditional PID/PI control method in the same stability margin. [ABSTRACT FROM AUTHOR]

Flexure-based scanning stages driven by piezoelectric (PZT) actuator with large stroke, nanoscale precision, and high bandwidth are quite appealing for developing an advanced multiphoton polymerization 3-D nanolithography system. The motivation of this article is to develop a nanopositioning system, which can simultaneously achieve large stroke, high bandwidth, and nanoscale precision to ensure its machining size, efficiency, and accuracy without traditional step-by-step splicing operations. First, an XY nanopositioning stage with millimeter-scale workspace and nanoscale positioning accuracy is designed. Besides, the natural frequency modeling of the nanopositioning stage is conducted by resorting to compliance analysis based on the matrix method, which is validated by a finite-element analysis (FEA). Moreover, linear active disturbance rejection controller (LADRC) is demonstrated that it can be equivalent to a PID controller filtered using a second-order low-pass filter, which theoretically verifies the effectiveness of controlling the nanopositioning stage. In order to complete LADRC’s successful implementation for nanopositioning stage, a novel quantitative one-parameter-tuning method of LADRC is proposed. Finally, a series of trajectory tracking experiments has been carried out to verify the effectiveness and superiority of the proposed nanopositioning stage. The experimental results verify that the large-stroke compliant nanopositioning system has the capability to achieve millimeter stroke, which has reached 1.035 mm $\times \,\,1.035$ mm, while the average tracking error is kept within ±100 nm, and the closed-loop bandwidth is achieved up to 32 Hz. Note to Practitioners—With the purpose to effectively improve the performance in terms of stroke, speed, and accuracy for the piezoelectric (PZT)-actuated scanning system, a completely decoupled nanopositioning stage with millimeter-scale travel range is developed and linear active disturbance rejection controller (LADRC) strategy with a novel quantitative one-parameter-tuning method of strategy is proposed. Experimental results indicate that the compliant nanopositioning system has the capability to achieve millimeter stroke, which has reached 1.035 mm $\times \,\,1.035$ mm, while the average tracking error of dynamic tracking control of the nanopositioning stage is kept within ±100 nm, and the closed-loop bandwidth is achieved up to 32 Hz. Therefore, the developed nanopositioning system has satisfactory performance for fulfilling advanced large-size multiphoton polymerization 3-D nanolithography task without step-by-step splicing operations. In summary, the potential applications of the proposed large-stroke nanopositioning strategy will be promising. [ABSTRACT FROM AUTHOR]

Recent deep convolutional neural networks for real-world noisy image denoising have shown a huge boost in performance by training a well-engineered network over external image pairs. However, most of these methods are generally trained with supervision. Once the testing data is no longer compatible with the training conditions, they can exhibit poor generalization and easily result in severe overfitting or degrading performances. To tackle this barrier, we propose a novel denoising algorithm, dubbed as Meta PID Attention Network (MPA-Net). Our MPA-Net is built based upon stacking Meta PID Attention Modules (MPAMs). In each MPAM, we utilize a second-order attention module (SAM) to exploit the channel-wise feature correlations with second-order statistics, which are then adaptively updated via a proportional-integral-derivative (PID) guided meta-learning framework. This learning framework exerts the unique property of the PID controller and meta-learning scheme to dynamically generate filter weights for beneficial update of the extracted features within a feedback control system. Moreover, the dynamic nature of the framework enables the generated weights to be flexibly tweaked according to the input at test time. Thus, MPAM not only achieves discriminative feature learning, but also facilitates a robust generalization ability on distinct noises for real images. Extensive experiments on ten datasets are conducted to inspect the effectiveness of the proposed MPA-Net quantitatively and qualitatively, which demonstrates both its superior denoising performance and promising generalization ability that goes beyond those of the state-of-the-art denoising methods. [ABSTRACT FROM AUTHOR]

This article is concerned with the $H_{\infty }$ proportional–integral–derivative (PID) control problem for class of discrete-time Takagi–Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed $H_{\infty }$ performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the $H_{\infty }$ disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method. [ABSTRACT FROM AUTHOR]

In this paper, an individualized intelligent multiple-model technique is proposed to design automatic artificial pancreas (AP) systems for the glycemic regulation of type 1 diabetic patients. At first, using the multiple-model concept, the insulin-glucose regulatory system is mathematically identified by constructing some local models. In this step, trade-offs between the number of local models and the complexity of the overall closed-loop system are made by defining and solving a bi-objective optimization problem. Then, optimal AP systems are designed by tuning a bank of proportional–integral–derivative (PID) controllers via the genetic algorithm (GA). A fuzzy gain scheduling strategy is employed to determine the participation percentages of the PID controllers in the control action. Finally, two safety mechanisms, called insulin on board (IOB) constraint and pump shut-off, are installed in the AP systems to enhance their performance. To assess the proposed AP systems, in silico experiments are performed on virtual patients of the UVA/Padova metabolic simulator. The obtained results reveal that the proposed intelligent multiple-model methodology leads to AP systems with limited hyperglycemia and no severe hypoglycemia. [ABSTRACT FROM AUTHOR]

A high-performance triaxial fast tool servo (FTS) with the hybrid electromagnetic–piezoelectric actuation and the hybrid parallel–serial-kinematic structure is reported. Featuring the balanced and uniform actuation, in this article, a novel axis-symmetric linearized reluctance actuator is proposed to generate the planar motion in parallel, and the piezoactuated vertical motion is then serially carried by the planar motion within a limited space. Verified by the finite-element analysis, a two-stage design strategy is developed to optimally determine the multiphysical system parameters for the triaxial FTS, assisted by an analytical model of the electromagnetic circuit as well as the mechanical mechanism. As for the trajectory tracking, the loop-shaping tuned PID controller with a feedforward compensator is employed for each axis, and a damping controller is additionally designed for the planar motion. Finally, both open-loop and closed-loop performance of the prototype are carefully demonstrated. [ABSTRACT FROM AUTHOR]

In recent years, a new paradigm in control engineering, called networked control system (NCS), has emerged due to the success of Internet and associated communication infrastructure. The NCS offers various advantages compared to the traditional control systems, which include increase of agility, wiring reduction, and easy maintenance and diagnosis. With an objective to investigate the effectiveness of various controllers in networked environment, the present study designs a $\Delta \Sigma$ -based single-bit PID controller for the rotor-side converter of a doubly fed induction generator (DFIG)-based wind energy conversion system. This consists of 20-DFIGs of 5 MW capacity each. A comparative performance investigation has been carried out with three other controllers; a conventional PID, Q-learning and dynamic fuzzy Q-Learning controller. The study considers various imperfections of a Zigbee protocol-based practical communication network, such as random delay and packet dropout. The results of both the simulation and experiment demonstrate that the $\Delta \Sigma$ -based single-bit PID controller shows better performance compared to other controllers. [ABSTRACT FROM AUTHOR]

We present a reset control approach to achieve setpoint regulation of a motion system with a proportional-integral-derivative (PID)-based controller, subject to Coulomb friction and a velocity-weakening (Stribeck) contribution. While classical PID control results in persistent oscillations (hunting), the proposed reset mechanism induces asymptotic stability of the setpoint and significant overshoot reduction. Moreover, robustness to an unknown static friction level and an unknown Stribeck contribution is guaranteed. The closed-loop dynamics are formulated in a hybrid systems framework, using a novel hybrid description of the static friction element, and the asymptotic stability of the setpoint is proven accordingly. The working principle of the controller is demonstrated experimentally on a motion stage of an electron microscope, showing superior performance over classical PID control. [ABSTRACT FROM AUTHOR]

In this article, a hybrid algorithm of Levenberg–Marquardt (LM) and proportional-integral-derivative neural network (PIDNN) with the relay feedback (LM-PIDNN-RF) is proposed to solve control problems for unknown discrete nonlinear systems. First, the PIDNN is initialized by the relay feedback to solve the problem of assigning initial weights; meanwhile, the LM neural network is regarded as an identifier to fit system input/output quickly and accurately. Second, the partial derivative of the system output to system input is transferred to the PIDNN, which ensures that the weights of the PIDNN can be updated correctly in time. The hybrid algorithm can update the weights of the neural network controller correctly against the errors caused by system instantaneous disturbance, and the controller has only one parameter to be tuned manually. Moreover, the stability of the closed-loop system is proven by using the Lyapunov stability theory. The proposed hybrid algorithm can significantly improve tracking performance in comparison with PIDNN and RF-PID. The results of three simulation examples and a physical experiment are presented to show superior tracking performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this article, a modified reduced order generalized integrator-based frequency-locked loop (MROGI-FLL) is proposed for controlling the interfacing inverter of the grid-tied photovoltaic system to mitigate the harmonics. Additionally, a fuzzy tuned PID controller is integrated in the proposed scheme to minimize the steady-state error, which results the voltage level is maintained constant at the common dc-link bus terminals. The MROGI-FLL is designed to evaluate the three-phase reference currents by extracting the fundamental constituents from the load currents and grid voltages. The proposed MROGI-FLL has several advantages such as better harmonic mitigation capability, control adaptivity, adaptive frequency and phase, grid synchronization, and low computation burden. The proposed reference current generator is simulated on MATLAB/Simulink platform under steady state and various transient conditions.The comparative study of proposed scheme is compared with the existing and adaptive control techniques, which assures the potency of the proposed control algorithm in-terms of harmonic mitigation, dc-offset rejection, frequency variation, and computation burden. Finally, the experimental results are verified through an experimental platform by using the dSPACE evaluation kit and both the simulation and hardware characteristics are well justified to the IEEE-519 standard limits. [ABSTRACT FROM AUTHOR]

In this article, we introduce an adaptive strictly negative-imaginary (SNI) autopilot for a low-cost quadrotor aerial vehicle, specifically designed to achieve high precision hovering and perform accurate trajectory tracking under time-varying dynamic load (i.e., displacement, velocity, and acceleration). Leveraging the learning ability of an artificial neural network, our adaptive SNI controller is robustly designed to overcome uncertainties in flight environments such as variations in the centre-of-gravity, modeling errors, and unpredictable wind gusts. The efficacy of the proposed adaptive control system is investigated under extensive flight tests in addition to numerous computer simulations and rigorous comparison with other control techniques, namely, fixed-gain SNI, fuzzy-SNI, and conventional PID controllers. We also conduct a stability analysis of the proposed control system using the SNI theorem. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers. [ABSTRACT FROM AUTHOR]

In this article, a novel robust observer-based nonlinear control approach is proposed for quadrotors with unmodeled dynamics and external disturbances, wherein the tracking errors are restricted effectively. Two nonlinear disturbance observers are proposed to deal with uncertainties in the inner and outer loops, respectively. On this basis, a robust observer-based nonlinear control approach is put forward for quadrotor systems. Specifically, in the outer loop subsystem, a robust observer-based control scheme is proposed, which can reduce unexpected tracking errors for quadrotors effectively. Subsequently, based upon the geometric methods, the coordinate-free attitude controller and the nonlinear disturbance observer are integrated as a robust controller in the inner loop subsystem. Based upon full nonlinear dynamics, the solutions of the closed-loop system are proven to be uniformly ultimately bounded by means of rigorous Lyapunov analysis. A series of comparative experiments consisting of the implementation of a nonlinear proportional-integral-derivative (PID)-type controller, an observer-based sliding mode controller, and the proposed controller are conducted to demonstrate the remarkable performance of the proposed method in terms of higher tracking accuracy and stronger robustness. [ABSTRACT FROM AUTHOR]

Electrical capacitance tomography (ECT) utilizes measured mutual capacitances across a region of interest to visualize distributions inside. As typical two-phase flows can be roughly treated as binary-valued material distributions, in this article, a fuzzy PID-controlled iterative algorithm is proposed for image reconstruction in cases of binary distributions. A closed-loop control system includes a fuzzy PID controller, Calderon’s method, and fast calculation of the Dirichlet-to-Neumann map. Capacitances measured in an electrode array of the ECT sensor are compared with the feedback, and the difference is input to the controller. Fuzzy rules are used to automatically adjust the three parameters of the controller, i.e., $K_{P}$ , $K_{I}$ , and $K_{D}$. The controller passes the difference to Calderon’s method for reconstructing permittivity distribution. Reconstructed distribution is used to calculate a boundary map for feedback, by fast calculation of the Dirichlet-to-Neumann map, and serves as an updated reference for measured capacitances. A smooth segmentation method is also introduced to deal with the binary distribution and release the fluctuation in the tuning of the PID controller. Numerical simulations were done to verify the performance of the proposed iterative Calderon’s method for binary distributions. Experiments on real phantoms were also carried out using an ECT system to evaluate the proposed method. Several distributions were set up with solid particles and air. The results show that the proposed method can produce images with clear edges and shapes of binary distributions. [ABSTRACT FROM AUTHOR]

Objective: This work aims to determine whether photoacoustic (PA) thermometry from a commercially available PA imaging system can be used to control the temperature in nanoparticle-mediated thermal therapies. Methods: The PA imaging system was interfaced to obtain PA images while scanning ex-vivo tissue. These images were then used to obtain temperature maps in real-time during heating. Validation and calibration of the PA thermometry were done using a fluoroptic thermometer. This thermometer was also used to develop and tune a software-based proportional integral derivative (PID) controller. Finally, a PA-based PID closed-loop controller was used to control gold nanorod (GNR) mediated laser therapy. Results: The use of GNRs substantially enhanced laser heating; the temperature rise increased 7-fold by injecting a GNR solution with a concentration of 0.029 mg/mL. The control experiments showed that the desired temperature could be achieved and maintained at a targeted location in the ex-vivo tissue. The steady-state mean absolute deviations (MAD) from the targeted temperature during control were between 0.16 °C and 0.5 °C, depending on the experiment. Conclusion: It was possible to control hyperthermia treatments using a software-based PID controller and a commercial PA imaging system. Significance: The monitoring and control of the temperature in thermal-based therapies are important for assuring a prescribed temperature to the target tissue while minimizing the temperature of the surrounding healthy tissue. This easily implemented non-invasive control system will facilitate the realization of a broad range of hyperthermia treatments. [ABSTRACT FROM AUTHOR]

Bifurcation control remains largely unresolved for fractional-order dynamical systems. This article addresses the optimal control issue of bifurcation for a delayed complex networks model with Caputo derivative, where the time delay is selected as the variable parameter. We first devise a fractional-order proportional-integral-derivative (PID) feedback synthesis for regulation of the Hopf bifurcation embedded in a delayed small-world network model with Caputo derivative. Dynamic stability criterion and Hopf bifurcation condition are obtained by carrying out the eigenvalue analysis of the controlled network. The stability range of the parameters of the PID control is evaluated completely for the small-world network. We can optimize the dynamics of stability and bifurcation of small-world networks by manipulating the gain parameters. Finally, we implement some simulations to show the performance of the presented PID scheme. The numerical simulations verify the advantage of the fractional PID controller in bifurcation control. [ABSTRACT FROM AUTHOR]

Variable impedance control has been considered as one of the most important compliant control approaches for its abilities in improving compliance, safety, and efficiency in robot–environment interaction. However, existing variable impedance controllers have deficits in stability guarantee. This article proposes a stability-guaranteed variable impedance control approach for robots with modeling uncertainties based on approximate dynamic inversion (ADI). Novel constraints on variable impedance profiles are given to guarantee the exponential stability of the desired variable impedance dynamics. An ADI-based impedance control law is designed to achieve the desired variable impedance dynamics through the convergence of a variable impedance error. Based on the extended Tikhonovs theorem, it is proven that the closed-loop control system has semiglobal practical exponential stability. The proposed impedance controller can be implemented in a PID form and is appealing for its simple structure, easy implementation, and control stability guarantee. The effectiveness of the proposed variable impedance controller is illustrated by an illustrative example taken on a five-bar parallel robot. [ABSTRACT FROM AUTHOR]

In the context of disturbance-estimation (DE)-based control, it is reported that including desired dynamics into the nominal model yields a simple and compact control law that is easier to implement. The resulting control structure unifies the nominal feedback controller and feed-forward disturbance compensator to formulate an overall feed-forward disturbance compensation problem. Based on this, two popular DE-based techniques, namely active disturbance rejection control (ADRC) and uncertainty and disturbance estimator (UDE)-based control, are restructured into a unified form. By reformulating the controllers in the frequency domain, it is shown that the two structures are equivalent provided the UDE filter is selected based on the observer designed for ADRC. For an easier understanding of practitioners who are familiar with the conventional control structures, the designed controllers are reinterpreted in the framework of two-degree-of-freedom (2DoF) internal model control and 2DoF proportional-integral-derivative (PID) controller to show the equivalence among them. Tuning rules for PID controllers in terms of unified ADRC parameters are also presented to aid practical implementation in industry. Performance of the unified control scheme is evaluated on a non-minimum phase dc–dc boost converter that is operating under line and load disturbances using simulation and experimental study. [ABSTRACT FROM AUTHOR]

This article deals with the observer-based proportional-integral-derivative (PID) security control problem for a kind of linear discrete time-delay systems subject to cyber-attacks. The cyber-attacks, which include both denial-of-service and deception attacks, are allowed to be randomly occurring as regulated by two sequences of Bernoulli distributed random variables with certain probabilities. A novel observer-based PID controller is proposed such that the closed-loop system achieves the desired security level and the quadratic cost criterion (QCC) has an upper bound. Sufficient conditions are derived under which the exponentially mean-square input-to-state stability is guaranteed and the desired security level is then achieved. Subsequently, an upper bound of the QCC is obtained and the explicit expression of the desired PID controller is also parameterized. Finally, the validity of the developed design approach is verified via an illustrative example. [ABSTRACT FROM AUTHOR]

In this article, we propose an analytical method to design a robust nonfragile controller for load voltage regulation of high-gain boost converter exhibiting nonminimum phase behavior due to right-half plane (RHP) zero. The proposed single-loop voltage-mode controller design technique is based on the stability boundary locus and Kharitonov's theorem. Using this approach, a robust performance region having guaranteed gain margin and phase margin criteria for nominal and 16 Kharitonov's plants are generated in the controller parameter plane. Controller robustness-fragility analysis due to the tolerance of analog compensator passive components is done with the help of a delta 20 robustness fragility index. Controller robustness, performance, and nonfragility are tested on a 200-W laboratory prototype against the load and source perturbations. Experimental results validate the effectiveness of the robust controller to regulate the load voltage of the converter in spite of the limit on performance set by RHP zero. [ABSTRACT FROM AUTHOR]

The article discusses dynamic requirements for designing ground manipulator platforms that can be utilized to verify the performance of controllers developed for space manipulators. Dimensional analysis is used to formalize the dynamic equivalence conditions and control scaling laws between a given space manipulator and the ground manipulator to be designed, such that the space manipulator and the designed ground manipulator are dynamically equivalent. Given that, unlike its space counterpart, the ground manipulator may not have the complete six degrees of freedom for its base and it must function under the gravity effects, as well as manufacturing imprecisions in building the ground manipulator, the sensitivity of dynamic equivalence conditions to the abovementioned factors is analyzed. Furthermore, an error compensation scheme based on the feedback linearization technique is developed to make the closed-loop ground manipulator have similar joint movements to those of the space manipulator under its controller. As a result, ground-based verification experiments can be performed for the controllers that will operate on the space manipulator. The design of a ground manipulator with the error compensation scheme for a specific space manipulator is illustrated through simulations, and it is shown how the performance of a PID controller for the space manipulator can be verified using the designed ground manipulator. [ABSTRACT FROM AUTHOR]

A new control strategy combing the Finite Element Method (FEM) with the control method is proposed to study the control of crystal growth process in this article. In the proposed control strategy, the crystal diameter control loop and the crystal temperature control loop are designed respectively to meet the requirements on crystal diameter and crystal temperature distribution. In the crystal diameter control loop, a bio-heuristic model based PID controller is designed to control crystal diameter. In the crystal temperature control loop, considering the undetectable characteristic of temperature distribution inside crystal, the control of crystal temperature distribution is transformed into the control of a key monitoring point in crystal and the constraint of crystal temperature gradient, and a model reference adaptive temperature controller is designed to achieve the control of the monitoring point temperature. Simulation results show that the crystal diameter and the temperature of the monitoring point are controlled well. Furthermore, the internal temperature distribution inside crystal is constrained within a reasonable range, avoiding the excessive thermal stress and dislocation defects. The proposed control strategy solves the problem of the traditional crystal growth system which cannot control crystal temperature, providing a new way for growing high-quality single silicon crystal. [ABSTRACT FROM AUTHOR]

Hydroturbine control system (HTCS) is a nonlinear, nonminimum phase system comprising of speed/frequency sensors, hydraulic servo motors, wicket gates, gate position feedback mechanism, and proportional integral derivative (PID) controller (governor). Governor controls the guide vane opening (GVO) corresponding to the input (speed/frequency). Nowadays, HTCS becomes more intricate insight of variable speed doubly-fed induction generator for compensating wide variation in water head. Hence, robust control techniques are necessary for the optimal utilization of hydropower potential. In this article, a brief review of HTCS is given and a complimentary sliding-mode controller (CSMC) is designed for GVO serving a 250 MW hydrogenerating unit. The CSMC control law is derived and the stability is verified through the Lyapunov function. Simulation is carried out through the MatLab Simulink environment for a 250 MW HTCS. Then, the performance of CSMC for GVO and its influence on the generator characteristics are analyzed and compared with the traditional PID controller. The simulation results demonstrate the efficacy of the controller during the startup process, subsynchronous, and super synchronous mode of operations. It is inferred that the proposed CSMC has three times faster settling time than that of the traditional PID controller. Experimental validation is carried out with a scaled-down laboratory prototype. Furthermore, future scope and design challenges associated with the hydropower control system are illustrated. [ABSTRACT FROM AUTHOR]

A boost converter which closely follows the quadratic converter voltage gain is proposed in this article. At tradeoff duty ratios, this converter is able to boost a lower input dc voltage to higher values at load side, higher than the traditional boost converter. This article also proposes a double-loop voltage-mode controller (DLVMC) for load voltage regulation, which exhibits better reference tracking as well as robust performance over the conventional proportional-integral-derivative (PID)-type robust controller. Also, addition of load voltage forward term suppresses the sensitivity and stability issues. An analytical and graphical approach is established to formulate the stabilizing region for the DLVMC parameter set. Parameter-space approach is the basis for the evolution of all possible controller parameter stabilizing regions. Furthermore, guaranteed combined sensitivity and guaranteed up–down glitch margin-related multiple performance constraints are simultaneously adopted in addition to absolute stability. As a result, any parameter combination corresponding to these stabilizing regions definitely ensures the proposed closed-loop converter system stability with sufficient robustness. An optimization methodology using integral time square error performance index is adopted to arrive at the optimal parameter set for DLVMC belonging to robust stabilizing regions. In order to assess the efficacy of the proposed robust controller design technique, a 75-W laboratory prototype is designed for experimentation. The DLVMC effectiveness in terms of load voltage regulation against the load and source perturbations along with reference tracking features is demonstrated experimentally. To also highlight its performance improvements, dynamic responses are compared and illustrated with the sample PID controller dynamic performance characteristics. [ABSTRACT FROM AUTHOR]

This brief proposes a constrained control scheme for the control of the depth of hypnosis during the induction phase in clinical anesthesia. In contrast with the existing control schemes for propofol delivery, the proposed scheme guarantees overdosing prevention while ensuring good dynamic performance. The core idea is to reformulate overdosing prevention as a constraint, and then use the recently introduced explicit reference governor to enforce the constraint satisfaction at all times. The proposed scheme is evaluated in comparison with a robust PID controller on a simulated surgical procedure for 44 patients whose pharmacokinetic-pharmacodynamic models have been identified using the clinical data. The results demonstrate that the proposed constrained control scheme can deliver propofol to yield good induction phase response while preventing overdosing in patients; whereas other existing schemes might cause overdosing in some patients. Simulations show that mean rise time, mean settling time, and mean overshoot of less than 5 [min], 8 [min], and 10%, respectively, are achieved, which meet typical anesthesiologists’ response specifications. [ABSTRACT FROM AUTHOR]

Maintaining the cathode pressure stable and avoiding oxygen starvation are crucial for the air supply of proton exchange membrane fuel cell (PEMFC). In most applications, the cathode pressure and oxygen excess ratio (OER) are unmeasurable parameters, which makes it difficult to precisely control the air supply system. This article proposes a fractional order PID (FOPID) controller based on an unknown input nonlinear observer for the fuel cell air supply system. The proposed nonlinear observer is able to estimate internal states including the cathode pressure of the PEMFC system. Then, the OER is obtained based on the observed system states. A fractional-order PID controller with a parameter optimization algorithm is developed to regulate as fast the OER and the cathode pressure to their desired values. Both the steady and transient performances of the proposed control method are simulated and experimentally validated compared with supertwisting sliding mode controller and traditional PID controller. [ABSTRACT FROM AUTHOR]

This paper improves and implements a framework based on robustness analysis to aid in the certification of unmanned aircraft system (UAS) flight controllers. The approach first models the UAS using a linear fractional transformation on uncertainties and then conducts robustness analysis on the uncertain system via integral quadratic constraint (IQC) theory. By expressing the set of desired UAS flight paths with an uncertainty, the framework considers the analysis of the uncertain UAS flying about any level path whose radius of curvature is bounded. The approach is capable of analyzing trajectory-tracking and path-following controllers, and an accurate expression of UAS path-following dynamics in the presence of nonzero wind is derived. To demonstrate the versatility of this technique, we use IQC analysis to tune trajectory-tracking and path-following controllers that are designed via $\mathcal {H}_{2}$ or $\mathcal {H}_\infty $ synthesis methods. IQC analysis is also used to tune path-following PID controllers. By employing a nondeterministic simulation environment and conducting numerous flight tests, we see that the uncertain UAS framework reliably predicts loss of control, compares the robustness of different controllers, and provides tuned controllers that are sufficiently robust. Finally, this work demonstrates that signal IQCs have an important role in obtaining IQC analysis results that are less conservative and more consistent with observations from flight test data. [ABSTRACT FROM AUTHOR]

We explore the equivalence among flat filters, dirty derivative-based proportional integral derivative (PID) controllers, active disturbance rejection control, and integral reconstructor-based sliding mode control, in the context of SISO second-order, perturbed, pure integration systems. This is the prevailing paradigmatic class of differentially flat systems or feedback linearizable systems. The equivalence is valid beyond the second-order pure integration systems. However, PID controllers of such plants do not make much sense without imposing assumptions that will move the considerations out of the pure integration systems case. The equivalence among the rest of the controllers is valid for any finite order pure integration system. [ABSTRACT FROM AUTHOR]

This paper develops a model for optimal and online tuning of PID controllers and evaluates its performance. The proposed model is based on computational intelligence approaches and can be used in industrial plant operating processes. The proposed tuning model is called PID-neuro-fuzzy model, a formulation based on a structured artificial neural network and fuzzy rules. The neural network is used to perform an optimal adjustment of PID controller gains to ensure the operating point of the system required to reduce the time of accommodation and the steady-state error. A fuzzy system is incorporated into a real-time gain-scheduling scheme to compensate for the possible variations in plant parameters. The performance of the proposed model is evaluated based on its ability to deal with uncertainties and disturbances in the process. The efficiency of the model is investigated through computational simulations in five plants of two industrial processes in the mining sector: 1) solid bulk unloading processes by car dumper and 2) solid bulk resumption process by bucket wheel reclaimers. In conclusion, the main advantage of the proposed model is its adaptability in relation to variations in plant parameters during the operational process. [ABSTRACT FROM AUTHOR]

In this article, we investigate the tuning problem of digital proportional-integral-derivative (PID) parameters for a dc motor controlled via the controller area network (CAN). First, the model of the dc motor is presented with its parameters being identified with experimental data. By studying the CAN network characteristics, we obtain the CAN-induced delays related to the load rate and the priorities. Then, considering the system model, the network properties, and the digital PID controller, the tuning problem of PID parameters for the CAN-based dc motor is transformed into a design problem of a static-output-feedback controller for a time-delayed system. To solve this problem, particle swarm optimization algorithm and linear-quadratic-regulator method are adopted by incorporating the sufficient condition of time-varying delay system. Finally, the effectiveness of the proposed PID tuning strategy is validated by experimental results. [ABSTRACT FROM AUTHOR]

In scan-based 3-D systems, the achievable measurement time is strongly restrained by the moving mass. This limitation can be relaxed, by scanning the optical path instead of moving the entire sensor, such that a higher measurement speed can be achieved. This article deals with the design, the control, and the measurement results of a scanning triangulation sensor, in which the illumination and reflection paths of the sensor are scanned by a fast steering mirror (FSM). The system architecture is determined using ray-tracing simulations, such that the performance of the scanning system can be determined in advance. To scan the area of interest with the FSM, conventional raster trajectories and Lissajous trajectories are employed, which provide an early overview of the scan area. For tracking these trajectories, PID and dual-tone controllers are used, respectively. The experimental results demonstrate that for both scan trajectories, the sample can be captured correctly, with a frame rate of 1 frame/s, a maximum spatial resolution of 130 $\mu \text{m}$ , and a field of view of $11.5\,\,\times 18.5\,\,{\mathrm{ mm}}^{2}$. [ABSTRACT FROM AUTHOR]

In this paper, a neural tuning technique is proposed and applied to a fractional-order proportional-integral-derivative (FOPID) controller. The proposed controller is applied to a radar-guided missile which is used for tracking high-speed moving targets in defense systems. The proposed neural tuning along with $\text{H}_{2}\text{/} \text{H}_{\infty }$ optimization method is intended to improve the tracking performance of the proportional navigation (PN) system and the stability of the missile trajectory during flight time. Due to the coupled and nonlinear dynamics of the considered missile, we propose a new control structure that integrates the FOPID controller into the PN system of the missile which results in better stability properties for the missile. In order to tune the FOPID, we propose a neural tuning technique that starts with a genetic algorithm-based optimizer and continues with a neural network-based scheme. This speeds up finding a near-optimal solution and refining it effectively. Then, $\text{H}_{2}\text{/} \text{H}_{\infty }$ optimization process is applied within the neural tuning technique to achieve better stability and tracking performance for the missile during the whole flight time. The proposed controller is compared with the standard PID controller tuned by the conventional Ziegler–Nichols (ZN) tuning method as well as particle swarm optimization (PSO) method, and the simulation results proved the superiority of the proposed tuning method over ZN- and PSO-based tuning approaches. [ABSTRACT FROM AUTHOR]

The proportional-integral-derivative (PID) controller is a widely used controller in automation industries. Several advanced PID tuning/design methods, such as response-based design, internal model control, and controller optimization by stochastic algorithms, have been proposed in the literature. However, regardless of the advantages and accuracy of developed tuning methods, due to the process modeling error and parametric uncertainties, the experimental response always differs from the theoretical response, which required online fine-tuning of the PID parameter. Manual adjustment of three PID parameters disturbs the robustness of the controller from its desired value; also, it does not guarantee the stability of the process. Therefore, this paper focuses on online PID controller tuning with the guaranteed robustness of the controller. A new single variable tuning method is developed for the online robustness and performance adjustment. Moreover, the proposed rules only depend upon the previously optimized PID parameters. The proposed method is an additional feature to all existing PID tuning methods, including optimal PID controller with stochastic optimization algorithms. The proposed method is validated with the help of optimal PID controller design by existing optimal tuning rules, optimized PID with particle swarm optimization and experimental validation on the canonical tank system. [ABSTRACT FROM AUTHOR]

This paper is concerned with the $\mathcal {H}_{\infty}$ proportional-integral-derivative (PID) control problem for a class of linear discrete-time systems with fading measurements. The fading measurements are governed by the Rice fading model whose coefficients are hypothesized to be a series of independent and identically distributed Gaussian variables. By utilizing the received measurements subject to fading phenomena, a novel output-feedback PID controller is proposed where the integral-loop (accumulation sum-loop for the discrete-time case) is equipped with the limited time-window in order to reduce the computational burden. The main objective of the addressed problem is to design a desired PID controller such that both the exponentially mean-square stability and the prescribed $\mathcal {H}_{\infty}$ performance are guaranteed for the closed-loop system in the presence of fading measurements. With the help of Lyapunov stability theory, a sufficient condition is obtained to guarantee the desired performance and, on the basis of such a condition, the synthesis issue of the PID controller is subsequently discussed, where the orthogonal decomposition combined with a free matrix is introduced to facilitate the controller design. Finally, a numerical example is exploited to demonstrate the usefulness and effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

This article presents a novel optimization algorithm to control the ultrasonic vibration of piezo-transducer in microelectronics packaging. The control of resonant frequency tracking to piezo-transducer is significant to the bonding quality. A conventional PID controller is applied to track the resonant frequency of piezo-transducer dynamically; however, there are shortcomings, such as tedious parameter tuning and uncertain optimal parameters. This article proposes the particle swarm optimization (PSO) to search the PID parameters intelligently. The MATLAB-Simulink model of PSO-PID is established to analyze the phase and frequency response of the piezo-transducer by a closed loop, where the optimal control parameters are determined by the PSO search. To solve the conventional abrupt ultrasonic power, an editable voltage trajectory with analytical geometry is proposed to provide various driving voltage trajectories. Finally, the control experiments with the proposed optimization are carried out in ultrasonic wire bonder. The results validate that the PSO-PID and editing trajectory optimizations can attain competitive bonding quality, stability, and robustness. [ABSTRACT FROM AUTHOR]

Left ventricular assist devices (LVADs) can provide mechanical support for a failing heart as a bridge to transplant and destination therapy. Physiological control systems for LVADs should be designed to respond to changes in hemodynamic across a variety of clinical scenarios and patients by automatically adjusting the heart pump speed. In this study, a novel adaptive physiological control system for an implantable heart pump was developed to respond to interpatient and intrapatient variations to maintain the left-ventricle-end-diastolic-pressure (LVEDP) in the normal range of 3 to 15 mmHg to prevent ventricle suction and pulmonary congestion. A new algorithm was also developed to detect LVEDP from pressure sensor measurement in real-time mode. Model-free adaptive control (MFAC) was employed to control the pump speed via simulation of 100 different patient conditions in each of six different patient scenarios, and compared to standard PID control. Controller performance was tracked using the sum of the absolute error (SAE) between the desired and measured LVEDP. The lower SAE on control tracking performance means that the measured LVEDP follows the desired LVEDP faster and with less amplitude oscillations, preventing ventricle suction and pulmonary congestion (mean and standard deviation of SAE (mmHg) for all 600 simulations were 18813 ± 29345 and 24794 ± 28380 corresponding to MFAC and PID controller, respectively). In four out of six patient scenarios, MFAC control tracking performance was better than the PID controller. This study shows the control performance can be guaranteed across different patients and conditions when using MFAC over PID control, which is a step toward clinical acceptance of these systems. [ABSTRACT FROM AUTHOR]

On the way to fully automated driving, Level 3 automated driving systems (ADS) seem to be a more realistic choice in the short term. Within operational design domain (ODD), although the driver is not required to monitor the vehicle all the time, it is expected that the driver is ready to intervene in the ADS whenever certain risks are encountered. Hence, timely identification of driver intervention in the vehicle lays the foundation for safe switching from the ADS to the driver. Although many researches have been conducted on driver torque estimation in the last decade, most of them are based on electric power steering (EPS) systems, therefore are not quite suitable for application in steer-by-wire (SBW) systems, which shall be the next generation's steering systems. In view of this, the SBW system parameters with uncertainties are first identified through experiments. Then, a PID controller and a robust observer considering Coulomb friction are designed and analyzed for the SBW systems, so that the system is asymptotically stable and the driver torque can be identified irrespective of parameter uncertainties and system noises. Detailed experimental results on a hardware-in-the-loop test bench verify the effectiveness of the proposed method, which shall also be implementable in a real vehicle. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPS) are controlling many critical and sensitive aspects of our physical world while being continuously exposed to potential cyber-attacks. These systems typically have limited performance, memory, and energy reserves, which limits their ability to run existing advanced malware protection, and that, in turn, makes securing them very challenging. To tackle these problems, this paper proposes, Remote, a new robust framework to detect malware by externally observing Electromagnetic (EM) signals emitted by an electronic computing device (e.g., a microprocessor) while running a known application, in real-time and with a low detection latency, and without any a priori knowledge of the malware. Remote does not require any resources or infrastructure on, or any modifications to, the monitored system itself, which makes Remote especially suitable for malware detection on resource-constrained devices such as embedded devices, CPSs, and Internet of Things (IoT) devices where hardware and energy resources may be limited. To demonstrate the usability of Remote in real-world scenarios, we port two real-world programs (an embedded medical device and an industrial PID controller), each with a meaningful attack (a code-reuse and a code-injection attack), to four different hardware platforms. We also port shellcode-based DDoS and Ransomware attacks to five different standard applications on an embedded system. To further demonstrate the applicability of Remote to commercial CPS, we use Remote to monitor a Robotic Arm. Our results on all these different hardware platforms show that, for all attacks on each of the platforms, Remote successfully detects each instance of an attack and has $<$ < 0.1 percent false positives. We also systematically evaluate the robustness of Remote to interrupts and other system activity, to signal variation among different physical instances of the same device design, to changes over time, and to plastic enclosures and nearby electronic devices. This evaluation includes hundreds of measurements and shows that Remote achieves excellent accuracy ($<$ < 0.1 percent false positive and $>$ > 99.9 percent true positive rates) under all these conditions. We also compare Remote to prior work EDDIE and SYNDROME , and demonstrate that these prior work are unable to achieve high accuracy under these variations. [ABSTRACT FROM AUTHOR]

This paper describes a method of PID-like adaptive fuzzy controller design for a linear time-invariant single-input and single-output dynamic plant. The plant transfer function is assumed to be partially known. The controller operates in the direct adaptation mode with the output feedback and a nonlinear reference model which provides better closed-loop response parameters than the system with a PID linear controller. The closed-loop stability is guaranteed during the adaptation process. The fuzzy controller is assumed to be a nonlinearity in a bounded sector and is defined by highly interpretable fuzzy rules. The absolute stability criterion for the system, containing PID-like fuzzy controller, is delivered. A design procedure that generalizes and significantly extends results reported so far in the literature and considerably automates the searching process of a nonlinear adaptive fuzzy controller is provided. [ABSTRACT FROM AUTHOR]