Hierarchical Control Research Articles

A Hierarchical Control Method for Trajectory Tracking of Aerial Manipulators Arms

To address the control challenges of an aerial manipulator arm (AMA) mounted on a drone under conditions of model inaccuracy and strong disturbances, this paper proposes a hierarchical control architecture. In the upper-level control, Bézier curves are first used to generate smooth and continuous desired trajectory points, and the theory of singular trajectory lines along with a Radial Basis Function Neural Network (RBFNN) is introduced to construct a highly accurate multi-configuration inverse kinematic solver. This solver not only effectively avoids singular solutions but also enhances its precision online through data-driven methods, ensuring the accurate calculation of joint angles. The lower-level control focuses on optimizing the dynamic model of the manipulator. Using a Model Predictive Control (MPC) strategy, the dynamic behavior of the manipulator is predicted, and a rolling optimization process is executed to solve for the optimal control sequence. To enhance system robustness, an RBFNN is specifically introduced to compensate for external disturbances, ensuring that the manipulator maintains stable performance in dynamic environments and computes the optimal control commands. Physical prototype testing results show that this control strategy achieves a root mean square (RMS) error of 0.035, demonstrating the adaptability and disturbance rejection capabilities of the proposed method.

ADP-based online compensation hierarchical sliding-mode control for partially unknown switched nonlinear systems with actuator failures

This article investigates an adaptive dynamic programming-based online compensation hierarchical sliding-mode control problem for a class of partially unknown switched nonlinear systems with actuator failures and uncertain perturbations under an identifier-critic neural networks architecture. Firstly, by introducing a cost function related to hierarchical sliding-mode surfaces for the nominal system, the original control problem is equivalently converted into an optimal control problem. To obtain this optimal control policy, the Hamilton–Jacobi–Bellman equation is solved through an adaptive dynamic programming method. Compared with conventional adaptive dynamic programming methods, the identifier-critic network architecture not only overcomes the limitation on the unknown internal dynamic but also eliminates the approximation error arising from the actor network. The weights in the critic network are tuned via the gradient descent approach and the experience replay technology, such that the persistence of excitation condition can be relaxed. Then, a compensation term containing hierarchical sliding-mode surfaces is used to offset uncertain actuator failures without the fault detection and isolation unit. Based on the Lyapunov stability theory, all states of the closed-loop nonlinear system are stable in the sense of uniformly ultimately boundedness. Finally, numerical and practical examples are given to demonstrate the effectiveness of our presented online compensation control strategy.

Hierarchical predictive optimal control for range extension of EV with ANN based torque control for IPMSM drives

This study presents a novel methodology to enhance the energy efficiency of Electric Vehicles (EVs) while maintaining dynamic stability and driving comfort, even on uneven roads, using onboard vehicle measurements. The approach involves a hierarchical control scheme that integrates a model predictive controller with a torque vectoring algorithm to optimize torque demand and accurately anticipate road-specific torque requirements. The proposed control is designed and implemented on an EV with Interior Permanent Magnet Synchronous Motors (IPMSM) on four wheel drives. The optimal torque control is realised through an Artificial Neural Network (ANN) - based motor torque control scheme. The design is validated through tests in real-world driving scenarios. In comparison with conventional methods, the proposed method shows a 37% increase in energy efficiency across different test conditions, thereby resulting in an increase in EV driving range. These advancements are realised without substantial modifications to the EV’s drivetrain, representing a significant step forward in sustainable and efficient electric mobility.

Finite-Time Hierarchical Containment Control of Heterogeneous Multi-Agent Systems With Intermittent Communication

Finite-Time Hierarchical Containment Control of Heterogeneous Multi-Agent Systems With Intermittent Communication

Control strategy of distributed flexible distribution network based on hierarchical optimal control

Control strategy of distributed flexible distribution network based on hierarchical optimal control

Hierarchical Nonsingular Terminal Sliding Mode Control With Finite-Time Disturbance Observer for PMSM Speed Regulation System

Hierarchical Nonsingular Terminal Sliding Mode Control With Finite-Time Disturbance Observer for PMSM Speed Regulation System

Hierarchical Control of Inverter Air Conditioners for Frequency Regulation Service of Islanded Microgrids With Fair Power Participation

Hierarchical Control of Inverter Air Conditioners for Frequency Regulation Service of Islanded Microgrids With Fair Power Participation

Hierarchical Control for Partially Feasible Tasks With Arbitrary Dimensions: Stability Analysis for the Tracking Case

Hierarchical Control for Partially Feasible Tasks With Arbitrary Dimensions: Stability Analysis for the Tracking Case

Wireless Magnetic Robot for Precise Hierarchical Control of Tissue Deformation.

Mechanotherapy has emerged as a promising treatment for tissue injury. However, existing robots for mechanotherapy are often designed on intuition, lack remote and wireless control, and have limited motion modes. Herein, through topology optimization and hybrid fabrication, wireless magneto-active soft robots are created that can achieve various modes of programmatic deformations under remote magnetic actuation and apply mechanical forces to tissues in a precise and predictable manner. These soft robots can quickly and wirelessly deform under magnetic actuation and are able to deliver compressing, stretching, shearing, and multimodal forces to the surrounding tissues. The design framework considers the hierarchical tissue-robot interaction and, therefore, can design customized soft robots for different types of tissues with varied mechanical properties. It is shown that these customized robots with different programmable motionscan induce precise deformations of porcine muscle, liver, and heart tissues with excellent durability. The soft robots, the underlying design principles, and the fabrication approach provide a new avenue for developing next-generationmechanotherapy.

Task-space tracking for networked heterogeneous robotic systems via adaptive neural fixed-time control

The task-space distributed adaptive neural network (NN) fixed-time tracking problem is studied for networked heterogeneous robotic systems (NHRSs). In order to address this complex problem, we propose a NN-based fixed-time hierarchical control approach that transforms the problem into two sub-problems: a distributed fixed-time estimation problem and a local fixed-time tracking problem, respectively. Specifically, distributed estimators are constructed so that each follower can acquire the dynamic leader’s state in a fixed time. Then, the neural networks (NNs) are employed to approximate the compounded uncertainty consisting of the unknown dynamics of robotic systems and the boundary of the compounded disturbance. More importantly, to guarantee that the tracking errors can converge into a small neighborhood of equilibrium in a fixed time independent of the initial state, the adaptive neural fixed-time local tracking controller is proposed. Another merit of the proposed controller is that the approximation errors are addressed in a novel way, eliminating the need for prior precise knowledge of uncertainties and improving the robustness and convergence speed of unknown robotic systems. Finally, the experimental results demonstrate the effectiveness and advantages of the proposed control method.

Hierarchical Control Strategy for the Hybrid Electric Propulsion System of a Flying Car With Engine Start-Stop System and Dynamic Coordination

Hierarchical Control Strategy for the Hybrid Electric Propulsion System of a Flying Car With Engine Start-Stop System and Dynamic Coordination

Data-driven optimal cooperative tracking control for heterogeneous multi-agent systems

This paper presents a novel hierarchical control scheme for solving the data-driven optimal cooperative tracking control problem of heterogeneous multi-agent systems. Considering that followers cannot communicate with the leader, a prescribed-time fully distributed observer is devised to estimate the leader’s state for each follower. Then, the data-driven decentralized controller is designed to ensure that the follower’s output can track the leader’s one. Compared with the existing results, the advantages of the designed distributed observer are that the prescribed convergence time is completely predetermined by the designer, and the design of the observer gain is independent of the global topology information. Besides, the advantages of the designed decentralized controller are that neither the follower’s system model nor a known initial stabilizing control policy is required. Finally, simulation results exemplify the advantage of the proposed method.

Data-Driven-Based Cooperative Resilient Learning Method for Nonlinear MASs Under DoS Attacks.

In this article, we consider the cooperative tracking problem for a class of nonlinear multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. To solve such a problem, a hierarchical cooperative resilient learning method, which involves a distributed resilient observer and a decentralized learning controller, is introduced in this article. Due to the existence of communication layers in the hierarchical control architecture, it may lead to communication delays and DoS attacks. Motivated by this consideration, a resilient model-free adaptive control (MFAC) method is developed to withstand the influence of communication delays and DoS attacks. First, a virtual reference signal is designed for each agent to estimate the time-varying reference signal under DoS attacks. To facilitate the tracking of each agent, the virtual reference signal is discretized. Then, a decentralized MFAC algorithm is designed for each agent such that each agent can track the reference signal by only using the obtained local information. Finally, a simulation example is proposed to verify the effectiveness of the developed method.

Hierarchical attitude stabilization controller design and analysis for underactuated spacecraft on SO(3)

In this paper, the complete attitude stabilization of underactuated spacecraft with two parallel single gimbal control moment gyroscopes (SGCMGs) is explored on 3-dimensional special orthogonal group (SO(3)) and its corresponding Lie algebra (so(3)). Instead of assumption on zero total angular momentum in existing article, a less conservative criterion is adopted to ensure system controllability, which does not simplify system dynamics. On the kinematic level, an ideal controller is proposed to globally stabilize attitude without singular initial condition and stagnation behavior. Besides, the ideal kinematic controller is developed on so(3), which avoids singularity or unwinding phenomenon caused by other attitude parameters. On the dynamic level, a hierarchical controller consisting of two parts is proposed. The high-level sliding mode part enforces finite time stabilization of angular velocity about underactuated axis while the low-level part tracks ideal angular velocity commands about actuated axes. A rigorous proof of the whole dynamic-kinematic system that has not been explored before is given. Analysis of hierarchical control from the perspective of linear algebra provides a novel insight into controller design for underactuated systems. Furthermore, the paradigm of controller design which is applicable to other underactuated systems is derived. Simulation results validate the effectiveness of the proposed control logic.

Hierarchical power reserve control of string-inverter-based photovoltaic power plant for primary frequency control

Retaining a certain power reserve is the precondition for a photovoltaic power plant (PVPP) to provide primary frequency control. Usually, a string-inverter-based PVPP may consist of hundreds of string inverters, and each string inverter has several direct current (DC) input channels. Thus, how to allocate the power reserve among so many controllable units is a challenging problem. In this paper, a hierarchical power reserve control method for a string-inverter-based PVPP to provide primary frequency control is introduced. In the inverter layer, the power reserve of each DC input channel is determined to balance the DC voltage variation of each channel during primary frequency control. In the plant layer, the power reserve is allocated to each PV cluster based on the predicted power generated by long short-term memory (LSTM) network, and the PV clusters are formed based on the historical generated power of each string inverter using K-means method. Furthermore, the string inverters within the same PV cluster evenly share the power reserve. To validate the proposed method, case studies in both the inverter layer and plant layer are carried out; the results verify the effectiveness of the proposed method.

Modelling of Vehicle Longitudinal Dynamics for Speed Control

Longitudinal dynamics control is one of the essential tasks for an autonomous vehicle, where it deals with speed regulation to ensure smooth and safe operations. To design a good controller, a simple yet reliable mathematical model is needed so that it can be used as a plant and to tune the controller. Although there are many types of mathematical models available in the literature, finding the right one for control application is essential. The model cannot be too complex and can be too simple. Thus, the main objective of this work is to derive a simple yet reliable vehicle longitudinal model so that it can be used as a simulation plant in MATLAB Simulink to test or tune various types of control algorithm's performance. The model consists of three main parts which are the vehicle body dynamics, simplified power train dynamic, and braking dynamic. To validate the reliability of the model, standard urban drive cycles will be used as a reference speed and a hierarchical PID control structure with inverse plant model is used to control the pedal inputs replacing the driver in simulation environment. Results show that the controller managed to track the drive cycle with an acceptable pedal pressing response which is between 40% throttle press and 20% brake press that in line with the normal operation of a vehicle. Although only simulation result is presented, the model can be used as a good starting point for further development and testing of different types of control algorithms for future work.

Motion planning in underactuated systems with impulsive phenomenon via dynamic shaping of virtual holonomic constraints

Rhythmic motions are traditionally achieved by developing predetermined paths for the states of the space system to follow. Since these paths are obtained offline, the dynamic behavior fails to adapt to changes in environmental conditions or user command desires. The solution we propose is a new strategy called dynamic shaping, in which the paths are formed online to allow the system to create an orbit with the characteristics we need. Hereupon, this paper focuses on applying this strategy to Dynamics with One Degree of Under-actuation and Impulsive Phenomenon (DSODUIP) to adapt the characteristics of outcomes to be in line with the demands.This research was conducted by leveraging the advantages of virtual holonomic constraints (VHCs) to establish these paths. Therefore, a novel two-level hierarchical control method is designed considering a stability criterion to avoid divergence. At the Low-Level, the controllers stabilize the output of system to follow the VHCs on the system. At the High-Level, the VHCs are modified to shape an orbit with our desired characteristic in the motion. As an illustrative example, the algorithm is implemented to adjust the average angular velocity of a devil stick and the hip velocity of a Three-Link biped robot. Their results vividly demonstrate smooth adjustments and efficient performance in achieving our desired outcomes.

Performance-guaranteed prescribed-time bipartite consensus of networked Lagrangian agents with bounded inputs and signed digraphs

In this paper, two kinds of hierarchical time-limited control (HTLC) algorithms with the ability to avoid singularities and simple parameter setting rules are proposed to achieve the bipartite consensus of Networked Lagrange Agents (NLAs), where each agent refers to external disturbances, dynamics uncertainties and bounded inputs. Each HTCL algorithm includes time-limited estimator and prescribed-time local controller. Specially, the HTCL algorithm is developed by combining error transformation and prescribed-time sliding surface. We formally demonstrate that all the states approach the neighborhood of the origin within the prescribed-time, where the settling time can be set only by selecting one parameter. The numerical examples verify the theoretical result.

A hierarchical multi-parametric programming approach for dynamic risk-based model predictive quality control

In this work, we present a hierarchical batch quality control strategy with real-time process safety management. It features a multi-time-scale decision-making framework augmenting: (i) Risk-aware model predictive controller for short-term set point tracking and dynamic risk control under disturbances; (ii) Control-aware optimizer for long-term quality and safety optimization over the entire batch operation; (iii) Intermediate surrogate model to bridge the timescale gap by readjusting the optimizer operating decisions for the controller. All of the above problems are solved via multi-parametric mixed-integer quadratic programming with a key advantage to generate offline explicit control/optimization laws as affine functions of process and risk variables. This allows for the design of a fit-for-purpose risk management plan prior to real-time implementation, while reducing the need for repetitive online dynamic optimization. A unified process model is used to underpin the consistency of hierarchical operational optimization. The proposed approach offers a flexible strategy to integrate distinct decision-making time scales which can be selected separately tailored to the process-specific need of control, fault prognosis, and end-batch quality control. A T2 batch reactor case study is presented to showcase this approach to systematically address the interactions and trade-offs of multiple decision layers toward improving process efficiency and safety.

Advancing Artificial Cells with Functional Compartmentalized Polymeric Systems - In Honor of Wolfgang Meier.

The fundamental building block of living organisms is the cell, which is the universal biological base of all living entities. This micrometric mass of cytoplasm and the membrane border have fascinated scientists due to the highly complex and multicompartmentalized structure. This specific organization enables numerous metabolic reactions to occur simultaneously and in segregated spaces, without disturbing each other, but with a promotion of inter- and intracellular communication of biomolecules. At present, artificial nano- and microcompartments, whether as single components or self-organized in multicompartment architectures, hold significant value in the study of life development and advanced functional materials and in the fabrication of molecular devices for medical applications. These artificial compartments also possess the properties to encapsulate, protect, and control the release of bio(macro)molecules through selective transport processes, and they are capable of embedding or being connected with other types of compartments. The self-assembly mechanism of specific synthetic compartments and thus the fabrication of a simulated organelle membrane are some of the major aspects to gain insight. Considerable efforts have now been devoted to design various nano- and microcompartments and understand their functionality for precise control over properties. Of particular interest is the use of polymeric vesicles for communication in synthetic cells and colloidal systems to reinitiate chemical and biological communication and thus close the gap toward biological functions. Multicompartment systems can now be effectively created with a high level of hierarchical control. In this way, these structures can not only be explored to deepen our understanding of the functional organization of living cells, but also pave the way for many more exciting developments in the biomedical field.