Abstracts:The Furuta pendulum’s swing-up and stabilization control is currently used by many researchers to benchmark nonlinear control algorithms. In this brief, we give a systematic overview of important contributions to the control of the Furuta pendulum presented in the last 15 years. Furthermore, we use nonlinear model predictive control (NMPC) to design a real-time capable holistic controller. An optimal control problem (OCP) including a detailed nonlinear system dynamics model is defined, transcribed into a nonlinear optimization problem via direct multiple shooting, and solved in real time on an embedded system using $\textsf {acados}$ . A breakthrough concerning the control performance was achieved by the usage of efficient discretization via a nonuniform grid, solving the tradeoff between a long prediction horizon and a fast sample time. The control strategy shows excellent performance in simulation and real-world experiments using a custom-made pendulum prototype. Videos of the experiments are available at: https://www.youtube.com/shorts/oJYyD5beMqM/

Abstracts:We develop a model predictive control (MPC) policy for station-keeping (SK) on a Near-Rectilinear Halo Orbit (NRHO). The proposed policy achieves full-state tracking of a reference NRHO via a multiple-maneuver control horizon, each spaced one revolution apart to abide by typical mission operation requirements. We prove that the proposed policy is recursively feasible, and perform numerical evaluation in an output-feedback setting by incorporating a navigation filter and realistic operational uncertainties, where the proposed MPC is compared against the state-of-the-art SK algorithm adopted for the Gateway. Our approach successfully maintains the spacecraft in the vicinity of the reference NRHO at a similar cumulative cost as existing SK methods without encountering phase deviation issues, a common drawback of existing methods with one maneuver per revolution.

Abstracts:Model predictive control (MPC) is a powerful framework for optimal control of dynamical systems. However, MPC solvers suffer from a high computational burden that restricts their application to systems with low sampling frequencies. This issue is further amplified in nonlinear and constrained systems that require nesting MPC solvers within iterative procedures. In this brief, we address these issues by developing parallel-in-time algorithms for constrained nonlinear optimization problems that take advantage of massively parallel hardware to achieve logarithmic computational time scaling over the planning horizon. We develop time-parallel second-order solvers based on interior point (IP) methods and the alternating direction method of multipliers (ADMM), leveraging fast convergence and lower computational cost per iteration. The parallelization is based on a reformulation of the subproblems in terms of associative operations that can be parallelized using the associative scan algorithm. We validate our approach on numerical examples of nonlinear and constrained dynamical systems.

Abstracts:Spacecraft attitude control performances are inevitably degraded by the composite disturbances such as flexible vibration and actuator uncertainty (e.g., partial loss of effectiveness and deadzone nonlinearity). These composite disturbances are inherently coupled with the attitude angular velocity and control input, exhibiting recessive, multiplicative, and additive forms. How to achieve the refined disturbance separation of these heterogeneous composite disturbances is crucial to improve the attitude control performances. In this article, a deep-coupled attitude dynamic model is established by sufficiently revealing the coupling relations of composite disturbances. Then, the disturbance separability analysis is carried out, and a novel input excitation disturbance separator (IEDS) is proposed. Driven by an ingeniously designed input excitation signal, the IEDS constructed by a refined disturbance observer (RDO) and a switch actuator uncertainty observer (SAUO) can estimate the flexible vibration disturbance and actuator uncertainty separately. Finally, some key parameters of actuator uncertainty, including the actuator effectiveness indicator and the dead-band sizes, are identified based on the IEDS output. By incorporating a sliding mode control in the feedback channel, a parameter identification-based composite attitude control is proposed, where the flexible vibration disturbance and actuator uncertainty can be compensated accurately. Numerical and experimental results are given to show the effectiveness of the proposed method.

Abstracts:In steer-by-wire (SBW) systems, where the steering wheel and the tire are not physically connected, the steering feel is artificially generated regardless of road conditions. Typically, SBW systems generate steering feel based on steering angle to steering torque models to provide specific reaction torques in response to the driver’s steering input. However, since the steering wheel is not mechanically connected to the tire, the driver cannot feel the road surface condition. This article proposes a novel control algorithm framework that can extract and transfer road surface information while still following the desired steering feel model. The steering feel generation control and road wheel control are integrated to achieve this goal. Specifically, we propose a reference steering model (RSM) for steering feel generation and bilateral control (BiC) for integrated steering wheel and road wheel control. This allows us to reflect the road surface condition without changing the steering feel model or identifying the road surface parameters. We validate the effectiveness of our proposed control through experiments using an SBW test vehicle.

Abstracts:The sinusoidal input describing function (SIDF) is a powerful tool for control system analysis and design, with its reliability directly impacting the performance of the designed control systems. This study improves both the accuracy of SIDF analysis and the performance of closed-loop reset feedback systems through two main contributions. First, it introduces a method to identify frequency ranges where SIDF analysis becomes inaccurate. Second, these identified ranges correlate with dominated high-order harmonics that can degrade system performance. To address this, a shaped reset control strategy is proposed, incorporating a shaping filter that tunes reset actions to suppress these harmonics. A frequency-domain design procedure for the shaped reset control system is then demonstrated in a case study, where a proportional–integral–derivative (PID)-based shaping filter effectively reduces high-order harmonics and eliminates limit cycles issues under step inputs. Finally, simulations and experiments on a precision motion stage validate the shaped reset control, confirming improved SIDF analysis accuracy, enhanced steady-state performance over linear and reset controllers, and the elimination of limit cycles under step inputs.

Abstracts:Traditional multivariate statistical process monitoring algorithms focus on whether measurements are significantly shifted compared with the training data, but lack further analysis of the monitoring results. This results in frequent alarm triggering for process variations that do not require urgent operator attention, such as operating condition deviations, faults that are unrelated to key performance indicators (KPI), and faults that are compensated by the closed-loop system feedback mechanism. Machine downtime for every alarm leads to high economic losses for the plant. Therefore, it is important to perform Fault Risk analysis to identify security threats from process variations. A risk-oriented approach should be able to determine whether faults are associated with safety or quality risks, thereby reducing overhaul costs and increasing economic efficiency. In this study, a Fault Risk analysis framework is proposed for nonlinear dynamic processes based on a kernel dynamic regression (KDR) model. The framework consists of two algorithms: one is KDR for detecting process faults, and the other is KPI-related KDR (KPI-KDR) for detecting faults affecting product quality. The proposed approaches provide more reasonable and interpretable dynamic and static subspace decomposition, which facilitates further analysis of the monitoring results. First, the KDR concurrently detects normal operating condition deviations and process faults. Then, the KPI-KDR analyzes whether faults can be compensated by feedback mechanisms. Finally, a closed-loop continuous stirred tank reactor and real catalytic cracking unit data are used to validate the effective performance of the proposed algorithms.

Abstracts:This article focuses on the security control problem of a class of cyber-physical systems (CPSs) with external disturbances. There are two main objectives of this article. First, a covert-switching-based (CSB) attack mechanism is proposed from the attacker’s point of view to enhance the expected destructiveness and flexibility. Different from most existing attack models, the attack mechanism proposed in this article can dynamically adjust the duration of different types of attacks by changing the scheduling parameters and can expect greater system performance degradation. Second, in order to correspond to the proposed CSB attack mechanism and reduce the negative impact of the attacks, a polynomial parameter-dependent switching multi-instantaneous gain-scheduling (SMIGS) control law based on the normalized fuzzy weighted membership degrees (NFWMDs) of the current and past moments is designed from the defender’s point of view. Then, with the help of the Lyapunov function, sufficient conditions for mean exponential stability are successfully established to ensure the $H_{\infty }$ performance of the error system. Finally, the progressiveness of the proposed strategy is verified by hardware-in-the-loop (HIL) simulation.

Abstracts:Nonlinear model predictive control (MPC) and reinforcement learning (RL) are two powerful control strategies with complementary advantages. This work shows how actor-critic RL techniques can be leveraged to improve the performance of MPC. The RL critic is used as an approximation of the optimal value function, and an actor rollout provides an initial guess for the primal variables of the MPC. A parallel control architecture is proposed where each MPC instance is solved twice for different initial guesses. Besides the actor rollout initialization, a shifted initialization from the previous solution is used. The control actions from the lowest-cost trajectory are applied to the system at each time step. We provide some theoretical justification of the proposed algorithm by establishing that the discounted closed-loop cost is upper-bounded by the discounted closed-loop cost of the original RL actor plus an error term that depends on the (sub)optimality of the RL actor and the accuracy of the critic. These results do not require globally optimal solutions and indicate that larger horizons mitigate the effect of errors in the critic approximation. The proposed algorithm is intended for applications where standard methods to construct terminal costs or constraints for MPC are impractical. The approach is demonstrated in an illustrative toy example and an autonomous driving overtaking scenario.

Abstracts:In this work, a moving horizon estimation (MHE)-based method is developed for estimating battery cells state in parallel-connected modules. Unlike conventional approaches, the proposed method acknowledges the impact of cell-to-cell (CtC) variations and heterogeneity propagation on module performance. A nonlinear observability analysis is performed to assess the feasibility of reconstructing individual cell states from module voltage and current measurements, considering interconnection resistance, state of charge (SOC)-dependent parameters, and different numbers of cells. The results indicate that states are distinguishable when the interconnection resistance is not null, and observability improves as the number of cells in parallel decreases. To the best of our knowledge, this is the first application of MHE in the context of battery modules, validated with real-world battery data. In contrast with conventional estimation methods, this study leverages MHE’s ability to handle equality constraints, allowing for the solution of Kirchhoff’s laws without complicating the module dynamics, maintaining the estimation accuracy. The proposed estimation algorithm demonstrates robustness against measurement noise and model uncertainties, with a maximum SOC error below 2.65%. Furthermore, the MHE results are compared against two widely used observers, the extended Kalman filter (EKF) and unscented Kalman filter (UKF), showing consistently higher estimation accuracy across all experimental conditions.