Controller Design Research Articles

Re-imagining the future state of the ventricular assist device controller interface through human-centered design.

Ventricular assist devices (VADs) are effective therapy for patients with end-stage heart failure. Current VAD controllers offer improved interactivity, yet limitations of the visual, tactile, and auditory interface persist that impact patient experience and quality of life (QoL). This study explores how VAD controllers can be redesigned using a human-centered design approach to enhance the emotional and functional experience of the device for patients. VAD patients (n = 21), caregivers (n = 4) and healthcare practitioners (n = 24) were interviewed to uncover design opportunities. From this, a series of realistic scenarios to design for emerged. A "design by analogy" method took inspiration from existing consumer products to ideate new functionality for the VAD wearable system, creating concepts for a controller interface and paired wearable device. An additional 15 patients and 2 caregivers were engaged to explore current VAD controller experiences and evaluate the future-state concepts. This research validated the need for increased automation and emergency functionality in VAD controllers, including remote monitoring of data, accurate communication of battery status, and automated medical alerts for critical device alarms. "Manage my health," "Feeling normal," "Social belonging," "Feeling safe," and "Sense of control" emerged as key patient concerns to be met by future VAD controller designs. The study demonstrated an innovative and relevant approach to improve usability of future VAD peripherals. By considering both emotional and functional perspectives in the design of lifesaving medical devices such as VADs, device manufacturers can uncover new opportunities to improve patient QoL through improved user experiences.

Finite-Time Funnel Synchronization Control Based on Distributed Observer for Multi-Motor Driving Systems With Input Saturation

Abstract In this article, the finite-time synchronization control problem is developed for multi-motor driving systems with input saturation. A radial basis function (RBF) neural network is utilized to estimate the unknown uncertainties and the disturbances. In contrast to current control strategies, this study proposes a new approach that utilizes a finite-time distributed observer to estimate the reference signal and then uses the estimated signal to design a synchronization controller, which effectively separates the tracking problem from the synchronous controller design. Furthermore, a funnel function is constructed to actualize the state constraints and confine the synchronization error within a specified boundary. Then, a finite-time funnel control protocol is proposed to ensure that each motor follows the estimated reference signal. Eventually, we illustrate the effectiveness of the proposed method through a numerical example.

Path Tracking Controller Design of Automated Parking Systems via NMPC with an Instructible Solution

Parking difficulties have become a social issue that people have to solve. Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents. The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method (RF-LNMPC) in order to improve the overall path tracking accuracy in parking conditions. Firstly, A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively. Secondly, the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization, of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution. Thirdly, the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented. The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically. Finally, the proposed RF-LNMPC is implemented on a self-driving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions. The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

Disturbance Observer-Based Adaptive Fault Tolerant Control with Prescribed Performance of a Continuum Robot

This paper studies an adaptive fault tolerant control (AFTC) scheme for a continuum robot subjected to unknown actuator faults, dynamics uncertainties, unknown disturbances, and prescribed performance. Specifically, to deal with uncertainties, a function approximation technique (FAT) is employed to evaluate the unknown actuator faults and uncertain dynamics of the continuum robot. Then, a nonlinear disturbance observer (DO) is developed to estimate the unknown compounded disturbance, which contains the unknown disturbances and approximation errors of the FAT. Furthermore, the prescribed error bound is treated as a time-varying constraint, and the controller design method is based on an asymmetric barrier Lyapunov function (BLF), which is operated to strictly ensure the steady-state and transient performance of the continuum robot. Afterwards, the simulation results validate the effectiveness of the proposed AFTC in dealing with the unknown actuator faults, uncertainties, unknown disturbances, and prescribed performance. Finally, the effectiveness of the proposed AFTC scheme is verified through experiments.

An adaptive fractional order optimizer based optimal tilted controller design for artificial ventilator

An adaptive fractional order optimizer based optimal tilted controller design for artificial ventilator

Optimal fuzzy P + Dµ controller for cancer chemotherapy

This paper aims to develop a robust, effective, and optimal controller for an automatic intravenous drug delivery system for the chemotherapy treatment of cancer patients. For this purpose, a novel hybrid control method, namely the Fuzzy P + Dµ controller, is designed by combining fractional calculus and fuzzy logic controller (FLC). Particle swarm optimization (PSO) algorithm is employed to tune the controller parameters optimally. To evaluate the performance of the proposed controller, a cancer patient model that considers cumulative drug toxicity, one of the most common side effects of chemotherapy, is utilized. Limits on toxicity and drug concentration for patient safety are incorporated into the controller design and optimization process. The graphical and numerical simulation results show that almost no cancerous cells are left at the end of the treatment period. The effectiveness of the controller is proven by the performance index (PI) obtained. Our optimal hybrid Fuzzy P + Dµ outclasses other control techniques in previous related studies with the best PI of 44.426 and the minimum final number of cancerous cells (FNCCs) of 5.084E-08. Moreover, in robustness tests perturbing intra-patient parameters and using different treatment protocols, the cumulative drug toxicity level is kept within safe limits, and almost no cancerous cells remain.

Compound Control Design of Near-Space Hypersonic Vehicle Based on a Time-Varying Linear Quadratic Regulator and Sliding Mode Method

The design of a hypersonic vehicle controller has been an active research field in the last decade, especially when the vehicle is studied as a time-varying system. A time-varying compound control method is proposed for a hypersonic vehicle controlled by the direct lateral force and the aerodynamic force. The compound control method consists of a time-varying linear quadratic regulator (LQR) control law for the aerodynamic rudder and a sliding mode control law for the lateral thrusters. When the air rudder cannot continuously produce control force and torque, the direct lateral force is added to the system. To solve the problem that LQR cannot directly obtain the analytical solution of the time-varying system, a novel approach to approximate analytical solutions using Jacobi polynomials is proposed in this paper. Finally, the stability of the time-varying compound control system is proven by the Lyapunov–Krasovskii functional (LKF). The simulation results show that the proposed compound control method is effective and can improve the fast response ability of the system.

Cancellation of Steady State Error Using Auxiliary Diophantine Equation for a Bio-Reactor Process -A case study

RST (Regulation, Sensitivity and Tracking) controller is very widely used in electrical engineering application. The controller provides both feed-forward and feedback actions. PID, Internal Model Controller (IMC) designs are mainly very effective in set point tracking but poor in disturbance rejection, however, the disturbance rejection can be obtained at the cost of reduced stability margins. The controller is based on the resolution of a Diophantine equation. A mathematical Model for Bioreactor was developed using MATLAB. Once the discrete model of the process is known and a model transfer function for the closed loop system has been chosen, the cancellation of steady state errors in response to reference signals has been solved for polynomial reference signals of any order by the introduction of an auxiliary Diophantine equation. The results are compared with the PID controller based on pole assignment method.

Non-fragile tracking controller design for fractional order systems against active disturbance rejection

Non-fragile tracking controller design for fractional order systems against active disturbance rejection

Non-fragile tracking controller design for fractional order systems against active disturbance rejection

Non-fragile tracking controller design for fractional order systems against active disturbance rejection

Adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems with unmeasurable states

This paper addresses the adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems, where the states and nonlinear functions are not feasible for the controller design. To address the issue of unmeasurable states, a state observer is developed, and fuzzy logic systems are utilized to approximate unknown nonlinear functions. Under the framework of fixed-time Lyapunov function theory and cooperative control, an adaptive fixed-time fuzzy containment control protocol is derived via the adaptive backstepping and adding one power integrator method. The derived fixed-time containment controller can confirm that the closed-loop systems are practical fixed-time stable, which implies that all signals in the systems are bounded and all follower agents can converge to the convex hull formed by the leader agents within fixed-time in the presence of unmeasurable states and unknown nonlinear functions . Simulation examples are conducted to test the validity of the present control algorithm.

Adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems with unmeasurable states

This paper addresses the adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems, where the states and nonlinear functions are not feasible for the controller design. To address the issue of unmeasurable states, a state observer is developed, and fuzzy logic systems are utilized to approximate unknown nonlinear functions. Under the framework of fixed-time Lyapunov function theory and cooperative control, an adaptive fixed-time fuzzy containment control protocol is derived via the adaptive backstepping and adding one power integrator method. The derived fixed-time containment controller can confirm that the closed-loop systems are practical fixed-time stable, which implies that all signals in the systems are bounded and all follower agents can converge to the convex hull formed by the leader agents within fixed-time in the presence of unmeasurable states and unknown nonlinear functions . Simulation examples are conducted to test the validity of the present control algorithm.

Finite-Time Fault-Tolerant Control of Nonlinear Spacecrafts with Randomized Actuator Fault: Fuzzy Model Approach

The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique.

Model Validation and Real-Time Process Control of a Continuous Flow Ohmic Heater

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design.

Genetic Cache: A Machine Learning Approach to Designing DRAM Cache Controllers in HBM Systems

DRAM memory controller plays a critical role in maximizing the performance of High Bandwidth Memory by efficiently managing data transfers between the CPU and the memory modules. Thus, they are suitable for low-power data-intensive applications. However, the complexity of DRAM command scheduling combined with tag management overheads in the cache makes the design of in-package cache controllers significantly challenging. Traditional memory controllers often face challenges with static, inflexible access scheduling designed for general applications, leading to suboptimal performance in dynamic environments. On the other hand, while some advanced controllers employing reinforcement learning offer adaptability to workload fluctuations, they tend to introduce hardware complexity and incur longer training latencies. Our approach aims to design low-power and efficient DRAM cache controllers using a machine learning model that dynamically adjusts to workload changes with optimized hardware efficiency and reduced training time. Therefore, we propose a machine learning approach to design low-power and efficient DRAM cache controllers that will leverage the trained model to produce optimal cache command schedules at runtime. The model considers several conditions for each request queue, and chooses the best response among the options. The simulation results show the superiority of our proposed design over the previous algorithms in a set of twelve data-intensive applications; our model is able to improve the performance up to 40% in some cases, and an average of 15% in performance and 10% in power consumption.

Disturbance Observer-Based Feedback Linearized Controller for Grid-Forming Four-Leg VSI Supplying Unbalanced and Nonlinear Loads

This paper presents the design of a disturbance observer-based controller that regulates the output voltage of three-phase four-leg voltage source inverters (VSIs) deployed for grid-forming operation in Renewable Energy-based Distributed Generation (REDG) Systems. The primary objective of the controller is to provide a symmetric and sinusoidal voltage at the output of the VSI when supplying highly unbalanced and nonlinear loads. The controller employs the feedback linearization (FL) technique and incorporates a disturbance observer (DO) to address a range of disturbances that include oscillations resulting from unbalanced loads, harmonics generated by nonlinear loads, and non-oscillatory disturbances. Notably, the controller adopts a direct control scheme without the need for nested current control loops and does not use any transformation frames. Simulation studies and experimental investigations were conducted to assess the controller’s performance under various load conditions, including both linear and nonlinear types, as well as load transients. The findings demonstrate the controller’s capability to accurately track references while complying with the IEEE power quality standards for the tested conditions.

Design of consensus and cluster consensus controller for first-order nonlinear multi-agent systems based on subgroup structure

It is well known that nonlinear multi-agent systems (MASs) with a directed topology can reach a consensus if the global coupling coefficient is strong enough. In this paper, we propose a novel distributed protocol based on the subgroup structure. Our results demonstrate that the MAS design can follow a part-to-whole approach, starting with subgroups and gradually building up to a more complex system. We firstly consider two cases for the MASs: one with a single leader and one with a leader group, where agents within the leader group can communicate with each other, but cannot receive the information of other group. We show that followers can track the leader or group leaders if intra-coupling within the subgroup and information strength received by the root node are above a certain threshold. Next, we investigate cluster consensus and practical cluster consensus for the Laplacian matrix with unequal row sum, equal row sum, and mixing of partitioned matrices. Our results reveal that row sum and community structure of the partitioned matrix are crucial in determining the final state of each agent. Our proposed control protocol is highly flexible and relies only on the cluster structure and information received by the root node, rather than global information. Moreover, our method is not limited to tracking problems, cooperative control, or multi-objective control, and can accommodate new group agents joining the system, making it highly adaptable. We provide illustrative examples to demonstrate the effectiveness of our approach.

Disturbance Observer-Based Feedback Linearized Controller for Grid-Forming Four-Leg VSI Supplying Unbalanced and Nonlinear Loads

This paper presents the design of a disturbance observer-based controller that regulates the output voltage of three-phase four-leg voltage source inverters (VSIs) deployed for grid-forming operation in Renewable Energy-based Distributed Generation (REDG) Systems. The primary objective of the controller is to provide a symmetric and sinusoidal voltage at the output of the VSI when supplying highly unbalanced and nonlinear loads. The controller employs the feedback linearization (FL) technique and incorporates a disturbance observer (DO) to address a range of disturbances that include oscillations resulting from unbalanced loads, harmonics generated by nonlinear loads, and non-oscillatory disturbances. Notably, the controller adopts a direct control scheme without the need for nested current control loops and does not use any transformation frames. Simulation studies and experimental investigations were conducted to assess the controller’s performance under various load conditions, including both linear and nonlinear types, as well as load transients. The findings demonstrate the controller’s capability to accurately track references while complying with the IEEE power quality standards for the tested conditions.

Adaptive fixed-time neural consensus control for a class of uncertain nonlinear multi-agent systems with full state constraints

This paper is concerned with the fixed-time consensus control problem for non-strict feedback multi-agent systems with asymmetric output constraints and full state constraints. Considering the feasibility of controlling execution, a novel practical virtual control signal is developed utilizing both saturation function and hyperbolic tangent function to ensure that this signal can remain within the same restricted range as the corresponding state variable throughout entire operation process. In backstepping steps, the design of ideal virtual control signal also adopts a different form of piecewise function than before, introducing high-order polynomial functions to avoid singularity problems in the derivation process. In addition, function approximation ability of radial basis function neural networks technique is applied to estimate uncertainties derived from the system functions and controller design procedure. Moreover, universal barrier Lyapunov function approach is improved for constructing an adaptive constrained synchronization control scheme. By fixed-time stability theory, it is shown that the tracking errors of the MAS converge to an adjustable region around the origin in a fixed time and the state variables always obey their constraints. And the upper bound of the settling time is merely dependent on design parameters, which is not affected by the initial states of MAS. The effectiveness of the proposed control strategy is shown by a numerical simulation example at last. Two scenarios are provided to demonstrate the advantages of the control protocol proposed in this paper.

Performance-prescribed optimal neural control for hypersonic vehicles considering disturbances: An adaptive dynamic programming approach

The precise trajectory tracking control of hypersonic vehicles plays a crucial role in secure flight. To address this challenge, this article develops an optimal neural control framework with prescribed performance constraints for height-velocity control tasks with disturbances. To this end, the control-oriented error model is firstly established and transformed into an affine nonlinear form to provide a foundation for the design of controller. Then, in order to balance performance constraints and convergence speed, a performance-constraint function is introduced to convert the constrained tracking error into an unconstrained one. After that, an improved sliding mode compensation observer is proposed by combining low-fidelity approximation data with high-fidelity compensation model for eliminating disturbances. The developed control scheme is organized by an adaptive dynamic programming (ADP) algorithm based on the actor-critic policy structure to achieve the near-optimal control strategy. Meanwhile, the designed neural network (NN) weight updating laws are provided to obtain the solution of Hamiltonian-Jacobi-Bellman (HJB) equation rapidly without high calculation consumption. Finally, the stability conditions of the closed-loop system are performed and ensure the entire framework is semi-global uniformly ultimately bounded (SGUUB), two simulation cases verify the effectiveness and superiority of the proposed approach.