Category: IEEE Transactions on Control Systems Technology

Model predictive control (MPC) is a competitive option in modern control systems due to its ability to account for future response and incorporation of complex control objectives. As applications become more intricate, nonlinearities limit the utility of linear control strategies, thus requiring more sophisticated architectures, often at a significant computational cost. This article investigates the computational cost of solving nonlinear MPC problems and provides a framework for designing nonlinear MPC architectures compatible with real-time performance. To motivate the computational complexity associated with nonlinear MPC, the design of an automotive collision imminent steering system and the controller is considered. Various trajectory optimization strategies are examined and compared for this application, identifying multiple-shooting-based Runge–Kutta explicit integration as the most suitable. The control algorithm is then mapped into a graphics processor unit-based hardware system, where special considerations of the parallel hardware architecture are discussed. Compared to the single-shooting solution as the benchmark, multiple shootings on parallel hardware achieve three orders of magnitude improvement in wall time, supporting real-time implementation.

In this article, a novel distributed secondary control scheme is proposed to solve the voltage regulation and power sharing problems of an islanded direct current (DC) microgrid. It not only eliminates the voltage deviation caused by droop control but also minimizes the total generation cost of the DC microgrid. The proposed secondary control of distributed generators (DGs) does not require global information, but only current information of neighbors and the DC bus voltage. Different from the existing secondary frequency control, the corresponding closed-loop system of the DC microgrid under the proposed scheme is a nonlinear networked system. With the help of the properties of cascade systems and the input-to-state stability theory, it is found that the DC microgrid under the proposed scheme can achieve voltage regulation and minimization of the total generation cost if the corresponding communication graph of DGs is connected. A simulation model is established to verify the proposed scheme. Finally, a DC microgrid platform with two DGs in parallel is used for the experimental verification.

This article proposes a new incremental model predictive control (IMPC) strategy, which allows for constrained control of a robot manipulator, while the resulting incremental model is derived without a concrete mathematical system model. First, to reduce dependence on the nominal model of robot manipulators, the continuous-time nonlinear system model is approximated by an incremental system using the time-delay estimation (TDE). Then, based on the incremental system, the tracking IMPC is designed in the framework of MPC without terminal ingredients. Thus, compared with existing MPC methods, the nominal mathematical model is not required. Moreover, we investigate reachable reference trajectories and confirm the local input-to-state stability (ISS) of IMPC, considering the bounded TDE error as the disturbance of the incremental system. For reachable reference trajectories, the local ISS of IMPC is analyzed using the continuity of the value function, and the cumulative error bound is not overconservative. Finally, several real-time experiments are conducted to verify the effectiveness of IMPC. Experimental results show that the system can achieve optimal control performance while guaranteeing that input and state constraints are not violated.

In the explicit model predictive control (EMPC), memory increases exponentially with the number of states, inputs, constraints, and prediction horizons; this often limits its applicability to large systems. In this article, we present a novel memory reduction technique for the lightweight EMPC using a novel posit number format implemented on a field-programmable gate array (FPGA), aiming to reduce the memory footprints and power utilization of the EMPC. We developed a fully automatic framework for the design of fast embedded EMPC on FPGAs using posit arithmetic and logical unit (ALU). The proposed technique is based on encoding all data (i.e., the critical regions and the feedback laws) as posit numbers, which can be viewed as a more memory-efficient alternative to the IEEE 754 floating-point standard. The performance and efficiency of the developed posit-based offset-free EMPC are demonstrated on the anesthesia control problem. We show the results of hardware-in-the-loop co-simulation with the detailed analysis of the resource utilization, power utilization, clock achieved, and the memory footprints comparison between IEEE 754 floating-point and posit formats. By doing so, we illustrate that the total memory footprints can be reduced by 50%–75% with achieving low power utilization as compared to floating-point numbers without sacrificing the control performances. The proposed technique can be applied on top of other existing complexity reduction techniques used in EMPC as well as for the online optimization methods.

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This article proposes a nonlinear observer for estimating a powertrain transmitted torque of vehicles equipped with dual-clutch transmissions. A new dynamical model of vehicle driveline is presented. It introduces a single characteristic state based on the dynamic friction model to scale the clutch torque capacity, which is available from the actuator position-to-torque map. Based on this model, the generated torque on the clutch disk can be effectively estimated in real time, and the sensitivity of the estimated value caused by high gain feedback can be reduced in the observer. Also, the engine torque uncertainty is estimated from the integrated driveline model. The Lyapunov-based analysis is presented to prove the convergence of the proposed observer. The effectiveness of the proposed method is shown by experimental validations on a test vehicle that allows for real measurements of the input shaft torque of transmission.

This article proposes a physics-based control-oriented model and observer design for the state-of-charge (SoC) estimation of lithium-ion cells for applications involving high magnitude fluctuating current profiles. The physics-based single-particle model (SPM) provides enhanced accuracy due to the inclusion of electrolyte dynamics and addresses the issue of nonobservability associated with it. The computationally efficient physics-based model is utilized to design a robust observer-based SoC estimator in the framework of linear matrix inequality to guarantee fast convergence despite parametric uncertainty in the state and output equations, and unknown initial conditions. The observer performance is validated using FTP75 and US06 dynamic tests at different temperatures, and the results are compared with the standard unscented Kalman filter (UKF). The mean SoC estimation error and the integral square error of the estimated SoC for the proposed observer are at least one order of magnitude smaller than that of UKF. Furthermore, robustness to ±30% parametric uncertainty, measurement noise, and unknown initial conditions is demonstrated through Monte Carlo simulations at different temperatures.

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This study theoretically investigates the modeling of spherical catalytic self-phoretic magnetic Janus microrobot (MJR) evolving in uniform viscous flow. A 2-D state-space representation of the MJR is developed, exhibiting a state-dependent-coefficient (SDC) form. To evaluate the consistency of the modeling formalism, a dual Kalman filter (DKF) methodology is employed with respect to experimental results when unknown parameters or uncontrollable inputs are considered. In fact, the self-phoretic thrust mechanism and the magnetodynamics of the MJR are not well-known. SDC-DKF is implemented, and we find that there is good agreement between the dynamics computed from our theoretical predictions and the experimental observations in a wide range of model parameter variations.

Manipulating microrobots to move in a 3-D space is a basic requirement for their in vivo medical applications. In this study, the automatic manipulation of a microrobot in a liquid 3-D space via an electromagnetic coil system is investigated. A path planner is designed to search an optimal path in a 3-D space with obstacles automatically, and a cascaded control algorithm is developed to control the microrobot’s movement along the planned path. The path is generated by combining the A-star and minimum jerk methods. The collision of the generated path with the obstacles is prevented and the hysteresis caused by the current change is minimized by reducing the jerk of the movement with a passable path from the starting point to the endpoint. In the cascaded control algorithm, the incremental nonlinear dynamic inversion (INDI) method and a proportional control are used as the inner and outer loops to guide the microrobot’s movement along the desired trajectory with an appropriate velocity while eliminating the influence of system uncertainty and external disturbances. Simulation and experiments are performed to verify the effectiveness of the proposed control strategy.