The concept of priority has been introduced to the robotic systems in 1980s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and the convergence of higher priority outputs when the input–output feedback linearization becomes nonsmooth and imperfect by singularity. For that purpose, we first prioritize the input–output feedback linearization and discuss how two degenerate properties can occur when the system becomes singular. Then, we introduce the differential inclusion to deal with the nonsmooth internal dynamics and establish a condition using the upper Dini derivative for asymptotically stable zero dynamics. Also, we propose a strictly positive realness-based feedback gain design and find a condition related to an M-matrix in order to handle nonlinearities that appear in the imperfect feedback linearization. Finally, we combine these two results and show the ultimate boundedness and the output convergence. In addition, we provide a motivational example with a planar four-link manipulator to illustrate the effectiveness and limitations of the proposed method.

This article presents a new structure of a sliding mode disturbance observer (SM-DOB) for handling model uncertainty and unmodeled disturbances. The proposed SM-DOB is composed of two key components. First, a Levant’s differentiator is used to estimate the high-order time derivatives of the output. To satisfy the conditions required for the effective operation of Levant’s differentiator, we employ an integral SM technique so that on the sliding surface, the real plant’s dynamics are substituted with the nominal dynamics. With this structure, the proposed SM-DOB can achieve robust disturbance rejection and recover nominal performance in finite time. The stability and the finite time convergence of the proposed SM-DOB are rigorously proved by the Lyapunov stability method. Finally, to show the superiority and verify the validity of the proposed SM-DOB, we conduct both numerical simulations and laboratory experiments with brushless dc (BLDC) motor drives. It is observed that the proposed SM-DOB guarantees satisfactory performance in rejecting both input disturbance and plant uncertainty while remaining robust in the presence of noisy measurement.

The continuous increase in functional density and operating frequency in modern electronic systems has intensified the design challenges associated with multilayer printed circuit boards (PCBs). Beyond ensuring basic connectivity, high-speed and high-density layouts must simultaneously satisfy signal integrity (SI), power integrity (PI), and electromagnetic compatibility (EMC) constraints. However, conventional maze-search-based autorouting algorithms, such as Lee’s algorithm and the A search method, primarily focus on minimizing routing length and computational effort, and thus fail to address multiobjective requirements including via minimization and conflict-free routing in three-dimensional (3D) design environments. In this study, an enhanced multilayer PCB autorouting algorithm is proposed. The proposed method extends the conventional A algorithm by adopting an eight-direction Chebyshev distance heuristic, a composite cost function that incorporates routing length and via penalties, and a dynamic re-routing mechanism for conflict resolution. The algorithm was evaluated using a representative two-layer dc–dc converter PCB layout. The proposed approach achieved 100% routing completion while reducing the average routing length by 21.55%, the number of vias by 50.00%, and the search time by 55.71% compared with the conventional A router. The results verify the effectiveness of the proposed algorithm for dense, constraint-driven PCB layouts and demonstrate its potential extension to SI- and EMC-aware autorouting in high-performance electronic systems.

In this paper we address the problem of control Lyapunov-barrier function (CLBF)-based safe stabilization for a class of nonlinear control-affine systems. A difficulty may arise for the case when a constraint has the relative degree larger than 1, at which computing a proper CLBF is not straightforward. Instead of adding an (possibly non-existent) control barrier function (CBF) to a control Lyapunov function (CLF), our key idea is to simply scale the value of the CLF on the unsafe set, by utilizing a sigmoid function as a scaling factor. We provide a systematic design method for the CLBF, with a detailed condition for the parameters of the sigmoid function to satisfy. It is also seen that the proposed approach to the CLBF design can be applied to the problem of task-space control for a planar robot manipulator with guaranteed safety, for which a safe feedback linearization-based controller is presented.

This work addresses an extended class of optimal control problems where a target for a system state has the form of an ellipsoid rather than a fixed, single point. As a computationally affordable method for resolving the extended problem, we present a revised version of the differential dynamic programming (DDP), termed the differential dynamic programming with ellipsoidal target set (ETS-DDP). To this end, the problem with an ellipsoidal target set is reformulated into an equivalent form with the orthogonal projection operator, yielding that the resulting cost functions turn out to be discontinuous at some points. As the DDP usually requires the differentiability of cost functions, in the ETS-DDP formulation we locally approximate the (nonsmooth) cost functions to smoothed ones near the path generated at the previous iteration, by utilizing the orthogonal projection operator. Moreover, a statistical inference method is also presented for designing the ellipsoidal target set, based on data on admissible target points collected by expert demonstrations. Via a simulation on autonomous parking of a vehicle, it is seen that the proposed ETS-DDP efficiently derives an admissible state trajectory while running much faster than the point-targeted DDP, at the expense of optimality.

This work addresses an extended class of optimal control problems where a target for a system state has the form of an ellipsoid rather than a fixed, single point. As a computationally affordable method for resolving the extended problem, we present a revised version of the differential dynamic programming (DDP), termed the differential dynamic programming with ellipsoidal target set (ETS-DDP). To this end, the problem with an ellipsoidal target set is reformulated into an equivalent form with the orthogonal projection operator, yielding that the resulting cost functions turn out to be discontinuous at some points. As the DDP usually requires the differentiability of cost functions, in the ETS-DDP formulation we locally approximate the (nonsmooth) cost functions to smoothed ones near the path generated at the previous iteration, by utilizing the explicit form of the orthogonal projection operator. Moreover, a statistical inference method is also presented for designing the ellipsoidal target set, based on data on admissible target points collected by expert demonstrations. Via a simulation on autonomous parking of a vehicle, it is seen that the proposed ETS-DDP efficiently derives an admissible state trajectory while running much faster than the point-targeted DDP, at the expense of optimality.

In this paper we address the problem of control Lyapunov-barrier function (CLBF)-based safe stabilization for a class of nonlinear control-affine systems. A difficulty may arise for the case when a constraint has the relative degree larger than 1, at which computing a proper CLBF is not straightforward. Instead of adding an (possibly non-existent) control barrier function (CBF) to a control Lyapunov function (CLF), our key idea is to simply scale the value of the CLF on the unsafe set, by utilizing a sigmoid function as a scaling factor. We provide a systematic design method for the CLBF, with a detailed condition for the parameters of the sigmoid function to satisfy. It is also seen that the proposed approach to the CLBF design can be applied to the problem of task-space control for a planar robot manipulator with guaranteed safety, for which a safe feedback linearization-based controller is presented.

This special issue showcases selected contributions from the 13th Asian Control Conference, highlighting substantial advancements in both the theory and practice of control systems.These contributions, spanning diverse methodologies and applications, cover innovative control methodologies, applications in industrial systems, and novel theoretical developments.Several papers in this issue showcase noteworthy approaches to control design.For instance, Cluster Assignment in Multi-Agent Systems: Sparsity Bounds and Fault Tolerance (M.Sharf and D. Zelazo) presents a method for designing sparse and fault-tolerant network graphs for homogeneous multi-agent systems to achieve prescribed clustering configurations, providing algorithms, sparsity bounds, and robustness guarantees for practical applications in multi-agent coordination tasks.Another contribution, Open-loop Equilibrium Seeking in Two-agent Noncooperative LQ Dynamic Game with Asymmetric Horizon Length (Y.Wasa, T. Tanaka, and T. Hayakawa) proposes an iterative optimization algorithm for equilibrium seeking in two-agent noncooperative linear-quadratic dynamic games with asymmetric prediction horizon lengths, enabling agents to estimate each other's feedback gains from state information and achieve stability without requiring private horizon details.Similarly, A Novel Scheme based on Coarse-Grained Localization and Fine-Grained Isolation for Defending Sybil Attack in RPL-based Network (F.Tong, Z. Zhang, Z. Zhu, Y. Zhang, and C. Chen) proposes a novel two-step scheme, utilizing coarse-grained localization and fine-grained isolation, to defend against Sybil attacks the IPv6 Routing Protocol for Low power and Lossy networks (RPL), leveraging RSSI (Received Signal Strength Indicator)-based physical address identifiers for node identification and achieving effective isolation with minimal network disruption through implementation on the Contiki OS.Finally,

The concept of priority has been introduced to the robotic systems in 1980s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and the convergence of higher priority outputs when the input–output feedback linearization becomes nonsmooth and imperfect by singularity. For that purpose, we first prioritize the input–output feedback linearization and discuss how two degenerate properties can occur when the system becomes singular. Then, we introduce the differential inclusion to deal with the nonsmooth internal dynamics and establish a condition using the upper Dini derivative for asymptotically stable zero dynamics. Also, we propose a strictly positive realness-based feedback gain design and find a condition related to an M-matrix in order to handle nonlinearities that appear in the imperfect feedback linearization. Finally, we combine these two results and show the ultimate boundedness and the output convergence. In addition, we provide a motivational example with a planar four-link manipulator to illustrate the effectiveness and limitations of the proposed method.