Optimal Controller Identification for multivariable non-minimum phase systems.

This work deals with data-driven control for non-minimum phase (NMP) systems, where the goal is to design a controller for a plant whose model is unknown by using a batch of input-output data collected from it. The approach is based on the Model Reference paradigm, where a transfer function matrix - the reference model - is used to specify the desired closed-loop performance. The NMP systems issue in Model Reference approaches is a well-known problem in control literature and it is no different in data-driven methods. This work explains how to adapt the formulation of the Optimal Controller Identification (OCI) method to cope with this class of systems. Considering a convenient parametrization of the reference model and a flexible performance criterion, it is possible to identify the NMP transmission zeros of the plant along with the optimal controller parameters, as it will be shown. Both diagonal and block-triangular reference model structures are treated in detail. Simulation examples show the effectiveness of the proposed approach.

Practical one-shot data-driven design of fractional-order PID controller: Fictitious reference signal approach.

This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable.

Repetitive dynamic matrix control for systems with periodic specifications.

This paper proposes a Repetitive Dynamic Matrix Control (RDMC) for systems with periodic specifications. The new algorithm is able to track periodic references and reject repetitive disturbances with a known period based on a modified prediction error. A repetitive version of the Generalized DMC (GDMC) is also proposed such that it can be applied to control open-loop unstable systems. Only the step-response coefficients are required to describe the dynamical system such that the RDMC preserves the modeling simplicity of the Dynamic Matrix Control (DMC). The proposed solution can be interpreted as an extension of the DMC for repetitive control applications. A data-driven filter design is proposed in order to ensure null prediction steady-state error in the presence of periodic disturbances even for unstable open-loop systems. Two case studies are presented to show the usefulness of the proposed strategy for control systems with periodic specification and to illustrate the typical advantages and drawbacks of the proposed repetitive control extension of the DMC algorithm.

Model-Free Control of a Soft Pneumatic Segment.

Soft robotics faces challenges in attaining control methods that ensure precision from hard-to-model actuators and sensors. This study focuses on closed-chain control of a segment of PAUL, a modular pneumatic soft arm, using elastomeric-based resistive sensors with negative piezoresistive behaviour irrespective of ambient temperature. PAUL's performance relies on bladder inflation and deflation times. The control approach employs two neural networks: the first translates position references into valve inflation times, and the second acts as a state observer to estimate bladder inflation times using sensor data. Following training, the system achieves position errors of 4.59 mm, surpassing the results of other soft robots presented in the literature. The study also explores system modularity by assessing performance under external loads from non-actuated segments.

Cell reprogramming design by transfer learning of functional transcriptional networks.

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach's versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.

Data-based bipartite formation control for multi-agent systems with communication constraints.

This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant's quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme.

Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach.

Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. This paper considers an event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic programming (ADP) via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP-based data driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. Then, an event-based feedback strategy is introduced to the closed-loop system to save the communication resources and reduce the number of control updates. The convergence of the ADP-based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSAs.

Adaptive control of a soft pneumatic actuator using experimental characterization data.

Fiber reinforced soft pneumatic actuators are hard to control due to their non-linear behavior and non-uniformity introduced by the fabrication process. Model-based controllers generally have difficulty compensating non-uniform and non-linear material behaviors, whereas model-free approaches are harder to interpret and tune intuitively. In this study, we present the design, fabrication, characterization, and control of a fiber reinforced soft pneumatic module with an outer diameter size of 12 mm. Specifically, we utilized the characterization data to adaptively control the soft pneumatic actuator. From the measured characterization data, we fitted mapping functions between the actuator input pressures and the actuator space angles. These maps were used to construct the feedforward control signal and tune the feedback controller adaptively depending on the actuator bending configuration. The performance of the proposed control approach is experimentally validated by comparing the measured 2D tip orientation against the reference trajectory. The adaptive controller was able to successfully follow the prescribed trajectory with a mean absolute error of 0.68° for the magnitude of the bending angle and 3.5° for the bending phase around the axial direction. The data-driven control method introduced in this paper may offer a solution to intuitively tune and control soft pneumatic actuators, compensating for their non-uniform and non-linear behavior.

Data-driven H∞ control of constrained systems: An application to bilateral teleoperation system.

A novel, identification-free, data-driven (DD) H∞ control method is presented for discrete-time (DT) linear time-invariant (LTI) systems under physical limitations and norm-bounded disturbances. The presented approach does not demand information on system matrices or any measurements of disturbance affecting the system. The only information needed to develop a static state-feedback (SF) controller is the bounds on disturbances, states and control signals. It is assumed that only the disturbance input matrix and the performance matrices the user generally defines are known, and all others are entirely unknown. The proposed method relies on the closed-loop (CL) parametrization of the LTI system with control input and state measurements. The disturbances affecting the system states are handled as affine uncertainties, later represented as Linear Fractional Transformation (LFT). For obtaining a less conservative controller, a full block S-procedure method (FBSPM) is used, which takes advantage of relaxations such as convex hull relaxation or Pólya relaxation for the inner approximation of the disturbance set with arbitrary precision. Numerical illustrations and extensive case studies on a bilateral teleoperation system indicate that the proposed design method allows us to obtain very effective controllers which never exceed the bounds of the state and input variables and are capable of reference and force tracking.

A Generalised Dynamic Matrix Control for unstable processes based on filtered predictions.

This paper presents a Generalised Dynamic Matrix Control (GDMC) algorithm that can be used to control open-loop unstable processes. In contrast to the Dynamic Matrix Control (DMC), the GDMC is able to provide internally stable predictions due to a generalised filtered approach. The conditions to achieve internal stability are shown and a new data-driven filter design procedure is proposed. Two simulation case studies are presented to illustrate the usefulness of the GDMC.

SMC-based model-free tracking control of unknown autonomous surface vehicles.

This paper investigates the robust tracking control problem of disturbed unknown autonomous surface vehicles (ASVs), and whereby a sliding-mode-control-based model-free tracking control (SMTC) approach by the combination of sliding-mode control and data-driven backstepping techniques is innovatively devised. By deploying a data-driven backstepping sliding-mode surface, a robust model-free adaptive controller is designed to achieve strong adaptability and robustness to unknown couplings, uncertainties and disturbances. Besides, a data-driven adaptive law based on disturbance observer and feedforward control strategies is effectively developed to estimate these unknowns, and thereafter the estimation is served as the compensation within the controller. Rigorous analysis proves that asymptotic tracking performance and strong robustness can be guaranteed theoretically. Lastly, simulation studies for the ASV are explored to demonstrate the validity and superiority of the devised SMTC approach in terms of disturbance attenuation, nonlinearity adaption, and high accurate tracking.

Physiologic Data-Driven Iterative Learning Control for Left Ventricular Assist Devices.

Continuous flow ventricular assist devices (cfVADs) constitute a viable and increasingly used therapy for end-stage heart failure patients. However, they are still operating at a fixed-speed mode that precludes physiological cfVAD response and it is often related to adverse events of cfVAD therapy. To ameliorate this, various physiological controllers have been proposed, however, the majority of these controllers do not account for the lack of pulsatility in the cfVAD operation, which is supposed to be beneficial for the physiological function of the cardiovascular system. In this study, we present a physiological data-driven iterative learning controller (PDD-ILC) that accurately tracks predefined pump flow trajectories, aiming to achieve physiological, pulsatile, and treatment-driven response of cfVADs. The controller has been extensively tested in an in-silico environment under various physiological conditions, and compared with a physiologic pump flow proportional-integral-derivative controller (PF-PIDC) developed in this study as well as the constant speed (CS) control that is the current state of the art in clinical practice. Additionally, two treatment objectives were investigated to achieve pulsatility maximization and left ventricular stroke work (LVSW) minimization by implementing copulsation and counterpulsation pump modes, respectively. Under all experimental conditions, the PDD-ILC as well as the PF-PIDC demonstrated highly accurate tracking of the reference pump flow trajectories, outperforming existing model-based iterative learning control approaches. Additionally, the developed controllers achieved the predefined treatment objectives and resulted in improved hemodynamics and preload sensitivities compared to the CS support.

Data-driven model-free resilient speed control of an autonomous surface vehicle in the presence of actuator anomalies.

This paper is concerned with the resilient speed control of an autonomous surface vehicle (ASV) in the presence of actuator anomalies. A data-driven model-free resilient speed control method is presented based on available input and output data only with pulse-width-modulation inputs. Specifically, a data-driven neural predictor is designed to learn the unknown system dynamics of the speed control system of the ASV. Then, a resilient speed control law is designed based on the learned dynamics obtained from the neural network predictor, where a cost function is designed for selecting the optimal duty cycle for the motor. The stability of the data-driven neural predictor is analyzed by using input-state stability (ISS) theory. The advantage of the developed data-driven model-free resilient control method is that the optimal speed control performance can be achieved in the presence of actuator anomalies without any modeling process. Simulation results show the learning ability of the data-driven neural predictor and the effectiveness of the proposed data-driven model-free resilient speed control method for the ASV subject to actuator anomalies.

Predefined performance-based model-free adaptive fractional-order fast terminal sliding-mode control of MIMO nonlinear systems.

The purpose of this article is to tackle with the problem of data-driven robust control of multi-input multi-output discrete-time nonlinear plants under tracking error constraints and output perturbations. Thereby, based upon the concept of dynamic linearization, a novel predefined performance based model-free adaptive fractional-order fast terminal sliding-mode controller is proposed so that the tracking errors can converge and remain within a preassigned neighborhood. The presented approach does solely rely on the real-time input/output data of the process, and the transient response together with the steady-state manner of the errors can be arbitrarily predefined. In the meantime, the closed-loop behavior is investigated by mathematical analysis, and the efficiency of the method is validated through various simulation examples.

Data Driven Control of Vagus Nerve Stimulation for the Cardiovascular System: An in Silico Computational Study.

Vagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where an electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. Currently, vagus nerve stimulation is under investigation for the treatment of heart failure, cardiac arrhythmia and hypertension. Through several clinical trials that sought to assess vagus nerve stimulation for the treatment of heart failure, stimulation parameters were determined heuristically and the results were inconclusive, which has led to the suggestion of using a closed-loop approach to optimize the stimulation parameters. A recent investigation has demonstrated highly specific control of cardiovascular physiology by selectively activating different fibers in the vagus nerve. When multiple locations and multiple stimulation parameters are considered for optimization, the design of closed-loop control becomes considerably more challenging. To address this challenge, we investigated a data-driven control scheme for both modeling and controlling the rat cardiovascular system. Using an existing in silico physiological model of a rat heart to generate synthetic input-output data, we trained a long short-term memory network (LSTM) to map the effect of stimulation on the heart rate and blood pressure. The trained LSTM was utilized in a model predictive control framework to optimize the vagus nerve stimulation parameters for set point tracking of the heart rate and the blood pressure in closed-loop simulations. Additionally, we altered the underlying in silico physiological model to consider intra-patient variability, and diseased dynamics from increased sympathetic tone in designing closed-loop VNS strategies. Throughout the different simulation scenarios, we leveraged the design of the controller to demonstrate alternative clinical objectives. Our results show that the controller can optimize stimulation parameters to achieve set-point tracking with nominal offset while remaining computationally efficient. Furthermore, we show a controller formulation that compensates for mismatch due to intra-patient variabilty, and diseased dynamics. This study demonstrates the first application and a proof-of-concept for using a purely data-driven approach for the optimization of vagus nerve stimulation parameters in closed-loop control of the cardiovascular system.

Security data-driven iterative learning control for unknown nonlinear systems with hybrid attacks and fading measurements.

To achieve the stabilization objective of a class of nonlinear systems with unknown dynamics, this paper studies the security data-driven control problem under iterative learning schemes, where the faded channels are suffering from randomly hybrid attacks. The networked attacks try to obstruct the data transmission by injecting the false data. The plant is transformed into a dynamic data-model with the iteration-related linearization method. Then, two data-driven control methods, including a compensation scheme multiplied by increasing gains, are designed by using incomplete I/O signals. The effectiveness of the algorithms and the influence brought by stochastic issues are analyzed theoretically. Finally, a numerical simulation and a tracking example of agricultural vehicles illustrate the validity of the design.

Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach.

The dynamics of a robot may vary during operation due to both internal and external factors, such as non-ideal motor characteristics and unmodeled loads, which would lead to control performance deterioration and even instability. In this paper, the adaptive optimal output regulation (AOOR)-based controller is designed for the wheel-legged robot Ollie to deal with the possible model uncertainties and disturbances in a data-driven approach. We test the AOOR-based controller by forcing the robot to stand still, which is a conventional index to judge the balance controller for two-wheel robots. By online training with small data, the resultant AOOR achieves the optimality of the control performance and stabilizes the robot within a small displacement in rich experiments with different working conditions. Finally, the robot further balances a rolling cylindrical bottle on its top with the balance control using the AOOR, but it fails with the initial controller. Experimental results demonstrate that the AOOR-based controller shows the effectiveness and high robustness with model uncertainties and external disturbances.

An efficient algorithm for collaborative learning model predictive control of nonlinear systems.

This paper contributes to an efficiently computational algorithm of collaborative learning model predictive control for nonlinear systems and explores the potential of subsystems to accomplish the task collaboratively. The collaboration problem in the control field is usually to track a given reference over a finite time interval by using a set of systems. These subsystems work together to find the optimal trajectory under given constraints in this study. We implement the collaboration idea into the learning model predictive control framework and reduce the computational burden by modifying the barycentric function. The properties, including recursive feasibility, stability, convergence, and optimality, are proved. The simulation is presented to show the system performance with the proposed collaborative learning model predictive control strategy.

Prescribed performance model-free adaptive sliding mode control of a shape memory alloy actuated system.

This paper concentrates on a simple and robust control method for the discrete time nonlinear systems to fulfill the requirement of predefined accuracy. A sliding mode control method is designed by introducing equivalent dynamic linearization technique according to the input/output (I/O) information merely. A square-root type error transformation method is presented for the tracking error to be restricted within a preassigned zone. The performance of presented control method is demonstrated through experiments on a nonlinear system. Experiment results show that the presented control method has a superior tracking accuracy compared with PID controller and model-free adaptive control (MFAC).

Data-driven game-based control of microsatellites for attitude takeover of target spacecraft with disturbance.

This paper investigates the problem of using multiple microsatellites to control the attitude of a target spacecraft losing control ability. Considering external disturbance and unknown system dynamics, a data-driven robust control method based on game theory is proposed. Firstly, the attitude takeover control of the target using multiple microsatellites is modeled as a robust differential game among disturbance and multiple microsatellites, in which microsatellites can obtain the worst-case control policies. Subsequently, policy iteration algorithm is put forward to acquire the robust Nash equilibrium control policies of microsatellites with known dynamics, which is a basis of data-driven algorithm. Then, by employing off-policy integral reinforcement learning, a data-driven online controller without information about system dynamics is developed to get the feedback gain matrices of microsatellites by learning robust Nash equilibrium solution from online input-state data. To validate the effectiveness of the proposed control method, numerical simulations are provided.