Adaptive Hierarchical Control Research Articles

An Adaptive Cruise Control Strategy for Intelligent Vehicles Based on Hierarchical Control

To minimize the occurrence of traffic accidents, such as vehicle rear-end collisions, while enhancing vehicle following, stability, economy, and ride comfort, a hierarchical adaptive cruise control strategy for vehicles is proposed. The upper-level controller computes the desired vehicle output acceleration based on model predictive control and switches between speed and spacing control in accordance with driving conditions. The brake/throttle opening switching model, brake control inverse model, and throttle opening inverse model in the lower-level controller of ACC are designed to obtain the desired throttle opening and braking pressure of the vehicle, thereby achieving control of the vehicle. A joint simulation platform was established using PreScan, CarSim and Matlab/Simulink. Finally, simulations for three typical working conditions were conducted in Simulink to verify the performance of the adaptive cruise control strategy. The results indicate that, in both the constant-speed cruise and vehicle-following cruise conditions, the vehicle can rapidly and stably follow the set initial speed and consistently maintain a safe distance from the preceding vehicle. Under the emergency braking condition, the vehicle can promptly respond with deceleration, ensuring driving safety. The proposed control strategy can accurately and safely track the target vehicle in diverse driving conditions and can concurrently fulfill the requirements of economy and comfort during vehicle travel.

Model reference adaptive hierarchical control framework for shake table tests

AbstractThe structural response under earthquake excitation can be simulated by shake table tests. However, the performance of the shake table is affected by the Control‐Structure Interaction (CSI) effect. In recent years, nonlinear control algorithms were developed to compensate for the CSI effect. In this study, a model reference adaptive control algorithm, named model reference adaptive hierarchical control (MRAHC) framework, is presented. MRAHC consists of a high (adaptive) and low (loop‐shaping) level controller. The high‐level (adaptive) controller develops the control algorithm on the system level, which directedly considers the inherent nonlinearity of the test specimen and the CSI effect. While the low‐level (loop‐shaping) controller develops the control algorithm to regulate the hydraulic system and make sure it can follow the reference signal generated by the high‐level (adaptive) controller. MRAHC offers many advantages including direct compensation to the structural nonlinearity and the ability to handle the CSI effect. In addition, it allows users to quantify the mass of the test specimens without measurement. To evaluate the performance of the MRAHC method, shake table tests with different upper structure masses were carried out. The performance of the MRAHC was compared with the direct loop‐shaping control method (LC) and the Proportional‐Integral‐Differentiation control method (PID). The results show that the MRAHC can achieve better acceleration tracking compared to the LC and PID control methods. Hence, the MRAHC can be used as an effective nonlinear controller for shake table tests.

On a hierarchical adaptive and robust inverse dynamic control strategy with experiment for robot manipulators under uncertainties

This paper proposes a hierarchical robot manipulation control scheme for the manipulator pushing task. Considering the contact surface and the object mass parameters are generally unknown to the pushing task, a hierarchical control strategy is designed that enables the robot manipulator to perform the manipulation pushing task without any prior knowledge of the objects mass and friction. Specifically, we first present an adaptive controller to achieve the desired interaction force exerted on the end of the manipulator even without knowing the object mass and friction contact surface. We then introduce a unified robust adaptive controller that takes into account the desired interaction manipulation force in our robot manipulator dynamics. A robust inverse dynamic controller combining a barrier Lyapunov function is included to prevent constraint violation, which can effectively keep the object motion along the desired trajectory while regulating the interaction force between the robot manipulator and the object. Furthermore, an adaptive unit control gain is designed to make the manipulator adapt to follow the perturbation variations without being overestimated. An experiment has been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

Optimizing Subway Train Operation With Hierarchical Adaptive Control Approach

Optimizing Subway Train Operation With Hierarchical Adaptive Control Approach

A Fast Optimal Speed Planning System in Arterial Roads for Intelligent and Connected Vehicles

Speed planning system is generally equipped for intelligent and connected vehicles (ICVs). Under the circumstances of autonomous driving, an energy-optimal speed trajectory is usually desired, particularly on urban arterial roads with complex traffic conditions involved. However, the existing speed planning solutions in the literature have not dealt with the problem of time consuming. Also, the ego vehicle could not perfectly track a precalculated speed reference because of the dynamically varying traffic. Thus, optimal speed planning cannot always be guaranteed. In this article, a fast optimal speed planning system for complex urban driving situations is established through an adaptive hierarchical control framework. In the planning layer, dynamic programming (DP) and the interior-point optimizer are jointly used to compute the global speed trajectory with access to signal phase and timing (SPaT) information. The computational burden is greatly alleviated based on a weighted orientation graph assumption and problem decomposition. The following layer utilizes the Informer, which is a transformer-based model, to predict preceding vehicle speed. Then, a target-switching model-predictive controller (MPC) is adopted for global speed trajectory following and adaption. The proposed approach significantly reduces speed planning computation time compared to previous solutions. Simulation results based on real road traffic scenes manifest that 22.0% of energy is saved compared with human driving.

Hierarchical Adaptive Control of the Cyber–Physical Power System With MMC-MTDC for Transient Stability Improvement

Large-scale wind power integration into the power system has promoted the development of a multiterminal DC (MTDC) transmission grid with a modular multilevel converter (MMC). Basically, MTDC with MMC is a typical cyber–physical system with continuous coupling interactions between cyber assets and power systems. However, cyber events may introduce many internet-based vulnerabilities and even result in the loss of transient stability of the power system. Therefore, a voltage compensation-based two-level hierarchical adaptive control strategy is proposed in this article. At the higher level, a modified MMC output current reference calculation method is developed in the αβ framework to guarantee the transient stability of the power system, whereas a feedback adjustment method is proposed in the MMC control framework, at the bottom level, to contain the controller from deviating from its output reference while eliminating the impact of cyber communication delay on transient stability. The article shows that the proposed hierarchical control strategy can improve the transient stability of the power grid under the interference of three-phase ground faults in physical and communication delays in the cyber layer. Finally, the simulation results of the modified IEEE 9-bus test system with MMC–MTDC are used to demonstrate the effectiveness of the proposed scheme.

An adaptive hierarchical control approach of vehicle handling stability improvement based on Steer-by-Wire Systems

This paper proposes a novel adaptive hierarchical control approach for Steer-by-Wire (SbW) vehicles to improve the handling stability. The high-level stability control scheme contains a variable steering ratio (VSR) strategy based on the adaptive-network-based fuzzy inference system (ANFIS) and an active front steering (AFS) controller designed with the integral sliding mode method by tracking the expected yaw rate, in which the desired front wheel angle is generated to enhance the cornering stability performance. Besides, an adaptive tracking controller (ATC) for the SbW system is designed by using the adaptive sliding mode control method to achieve desired steering performance in the lower level. The proposed adaptive control strategy is validated with different driving circles from ISO standards in simulation tests and hardware-in-the-loop (HiL) experiments. The results demonstrate that the designed control approach improve the vehicle handling stability significantly, even in some extreme driving conditions.

Hierarchical adaptive control of self-stabilizing electromechanical systems using artificial-immune self-tuning mechanism for state weighting-factors

This article presents a novel self-adaptive linear-quadratic-regulator (LQR) architecture to improve the robustness of self-stabilizing electromechanical systems against exogenous disturbances. The main contribution of this article is to formulate a nonlinear-type artificial-immune adaptation mechanism that dynamically adjusts the state-weighting-factors of LQR’s quadratic-performance-index online. The Riccati-equation solver uses these updated state-weighting-factors to yield time-varying state-feedback gains. This hierarchical control procedure uses immunological computations to indirectly alter the LQR gains, which helps in flexibly reconfiguring the control trajectory under disturbances. The performance of the proposed immune-adaptive LQR is benchmarked against a conventional adaptive LQR and a fixed-gain LQR by conducting software simulations on the nominal model of the QNET rotary pendulum system. Credible real-time experiments are also conducted on the QNET rotary pendulum’s hardware setup to analyze each controller’s efficacy in the physical environment. The simulation and experimental results validate the superior disturbance-rejection capability of the proposed controller under every testing scenario.

Quaternion‐based adaptive control for trajectory tracking of quadrotor unmanned aerial vehicles

SummaryThe problem of trajectory tracking of aerial vehicles with four rotors and dynamic parameter uncertainties is addressed in this article. By exploiting the cascade structure of the translational and attitude dynamics of the quadrotor, a nonlinear hierarchical adaptive control is proposed. The attitude control law is designed based on the unit quaternion, therefore, the singularities of the Euler angles and angle‐axis representation are avoided. Instead of relying on the stability properties of time‐varying cascade systems, the stability of the whole closed‐loop system is proven by an appropriate Lyapunov function. In addition, a novel method to compute the desired orientation based on the desired Cartesian trajectories and position control inputs is proposed. A set of simulations results are presented to assess the performance of the proposed adaptive controller.

A novel cable-suspended quadrotor transportation system: From theory to experiment

This paper studies the development of a novel quadrotor aerial transportation system, which carries payload with four cables. The possible stable configurations are discussed to show the advantages of the four-cable system. The bounds of the disturbance forces and torques acting on the quadrotor from the payload are estimated based on the dynamics analysis. Then a hierarchical adaptive controller is designed on the Lie group SO(3) for the transportation of the payload by the quadrotor. Finally, experimental results are presented to show the effectiveness of the proposed transportation system and the controller.

A hierarchical adaptive control framework of path tracking and roll stability for intelligent heavy vehicle with MPC

Roll stability is a major concern in the path tracking process of intelligent heavy vehicles in emergency steering maneuvers. Due to the coupling of lateral and roll motions of heavy vehicle, steering actions for path tracking may come into conflict with that for roll stability. In this paper, a hierarchical adaptive control framework composed of a supervisor, an upper controller and a lower controller is developed to mediate conflicting objectives of path tracking and roll stability via steering control. In the supervisor, path tracking control mode or cooperative control mode of path tracking and roll stability is determined by the predicted rollover index, and a weight function is introduced to balance the control objectives of path tracking and roll stability in cooperative control mode. Then, in order to achieve multi-objective real-time optimization, model predictive control with varying optimization weights is used in the upper controller to calculate the desired front steer angle. The lower controller which integrates the real electrically assisted hydraulic steering system based on Proportional-Integral-Derivative control is designed to control steering wheel angle. Simulation and hardware-in-loop implementation results in double lane change scenario show that the proposed hierarchical adaptive control framework can enhance roll stability in emergency steering maneuvers while keeping the accuracy of path tracking for intelligent heavy vehicle within an acceptable range.

An Adaptive Hierarchical Control Method for Microgrid Considering Generation Cost

Many power distribution methods of microgrid (MG) are all limited by fixed structure of MG and mathematical model, which will be out of effect once the structure changes. In order to solve the above problems, this paper proposed an adaptive hierarchical control method considering generation cost. Firstly, the distributed generators (DGs) of MG are considered as multi-agent system, and the secondary controller based on finite-time theory is constructed. Secondly, the improved gray wolf optimization (IGWO) algorithm is used as the tertiary controller to dynamically optimize the rated power of DG, and the objective function and constraints are established. Thirdly, the adaptive virtual impedance is introduced to realize accurate power sharing and the closed-loop control is formed by combining the current loop and voltage loop controllers. Finally, by dynamically optimizing the rated power, the on-line real-time optimal power distribution of MG is achieved, and the stability of the system is proved by the theories of multi-agent consistency and finite-time stability. The control method proposed in this paper accelerates the convergence speed of MG, and the calculation results have high accuracy. At the same time, the flexibility and reliability of MG are improved. The simulation model is established in Matlab/Simulink environment, and the simulation results show that the method is effective.

Hierarchical Adaptive Control System of a Manipulator Based on the Synthesis of a Neural Network of Fuzzy Inference and an Iterative Refinement Algorithm

The main aim of this work is to develop an adaptive control system for a kinematically redundant multilink industrial manipulator. Proposed solution allows to construct a unified real-time control system with the ability to control the accuracy of calculations. In order to achieve the required accuracy of the calculations and the performance of the control system, we propose an algorithm that is based on the so-called hybrid method for finding the solution of the inverse kinematics (IK) problem, including the adaptive neural network and fuzzy inference system with subsequent iterative refinement of numerical solution by the Newton --- Raphson method. The influence of the training sample size on the quality of the obtained initial approximation for the neural network part of the algorithm is described in the paper. The results of experimental studies of the developed hybrid algorithm are presented in comparison with the iterative and neural network methods for three-, five- and eight-link manipulator structures. Paper presents the main steps of the control system synthesis for kinematically redundant industrial manipulator, including the description for developed algorithms for finding an IK solution of multilink structures. The structure of a multi-level hierarchical manipulator control system, based on a programmable logic controller and electric stepping motors with the possibility of integration into the production system at various levels, is presented.

Hierarchical adaptive control of safety buffers in manufacturing

Buffer management is an important part of manufacturing planning and control where the safety buffers provide robustness in relation to uncertainty in demand and supply. Hierarchical control of safety buffers highlights the significance of decisions made at different levels and for different purposes with inter-level feedback and intra-level adaptation. A period-based view of time complements the hierarchical control by identifying inter-period and intra-period aspects of managing uncertainty and thereby promoting a proactive dominated or reactive dominated way of managing uncertainty. By combining these two inter vs. intra based concepts, a hierarchical approach to adaptive safety buffer control in manufacturing planning and control is outlined.

Potential field–based hierarchical adaptive cruise control for semi-autonomous electric vehicle

Adaptive cruise control, as a driver assistant system for vehicles, can adjust the vehicle speed to keep the appropriate distance from other vehicles, which highly increases the driving safety and driver’s comfort. This paper presents hierarchical adaptive cruise control system that could balance the driver’s expectation, collision risk, and ride comfort. In the adaptive cruise control structure, there are two controllers to achieve the function. The one is the upper controller which is established based on the model predictive control theory and used to calculate the desirable longitudinal acceleration. The collision risk is described by the Gaussian distribution. A quadratic cost function for model predictive control is formulated based on the potential field method through the contradictions between the tracking error, collision risk, and the longitudinal ride comfort. The other one is the lower optimal torque vectoring controller which is constructed based on the vehicle longitudinal dynamics. And it can generate the desired acceleration considering the anti-wheel slip limitations. Several simulations under different road conditions demonstrate that the proposed adaptive cruise control has significant performance on balancing the tracking ability, collision avoidance, ride comfort, and adhesion utilization. It also maintains vehicle stability for the complex road conditions.

DNA-Based Multiconstituent Dynamic Networks: Hierarchical Adaptive Control over the Composition and Cooperative Catalytic Functions of the Systems.

The information encoded in the base sequences of nucleic acids is used to construct [3 × 2] or [3 × 3] constitutional dynamic networks (CDNs) composed of six or nine constituents, respectively. In the presence of appropriate triggers, the adaptive and hierarchical reconfiguration of the CDNs is demonstrated. The reconfiguration of the CDNs, which involves the triggered stabilization and upregulation of a specific constituent is accompanied by the upregulation of the constituents that do not share component connectivities with the trigger-stabilized constituent, and by the concomitant downregulation of the constituents sharing component connectivities with the trigger-stabilized constituent. Using a set of different triggers, a series of reconfigured networks-in-networks are demonstrated. The operation and reconfiguration of the CDNs are based on the following motives: (i) Each of the constituents in the [3 × 2] or [3 × 3] CDNs is composed of a supramolecular structure consisting of two duplex-bridged double-loop quasi-circle units. The hybridization of a single-stranded trigger with the double-loop domain stabilizes the respective constituent, and this results in the reconfiguration of the CDNs. (ii) To each of the constituents is conjugated a Mg2+-ion-dependent DNAzyme that cleaves a sequence-specific fluorophore/quencher substrate. The time-dependent fluorescence changes upon the cleavage of the different substrates by the reporter DNAzymes, and the use of appropriate calibration curves, provide means to quantitativelyevaluate the contents of all constituents in the different CDNs. The triggered transitions of the parent [3 × 2] or [3 × 3] CDNs into three different CDNs using three different triggers, respectively, are exemplified. In addition, the transitions of the parent [3 × 2] or [3 × 3] CDNs using sequential triggers are demonstrated. Adaptive equilibrated reconfiguration of the CDNs subjected to triggers is demonstrated, and hierarchical adaptive dynamic transitions of the CDNs, subjected to two sequential triggering signals are highlighted. In addition, emerging catalytic functions driven by the adaptive and hierarchical transitions across different equilibrated states of the [3 × 3] CDNs are demonstrated.

An Adaptive Hierarchical Trajectory Following Control Approach of Autonomous Four-Wheel Independent Drive Electric Vehicles

This paper deals with the trajectory following control problem of a class of autonomous vehicles with parametric uncertainties, external disturbances, and over-actuated features. A novel adaptive hierarchical control framework is proposed to supervise the lateral motion of autonomous four-wheel independent drive electric vehicles. First, an adaptive sliding mode high-level control law with the linear matrix inequality-based switching surface is designed to produce a vector of front steering angle and external yaw moment, in which the uncertain term and the switching control gain are adaptively regulated by the fuzzy logic technique, to further moderate the chattering phenomenon, an adaptive boundary layer is introduced. Second, a pseudo-inverse low-level control allocation algorithm is presented to optimally allocate the external yaw moment via coordinating and reconstructing the tire longitudinal forces. Finally, numerical simulation and experimental results demonstrate that the proposed adaptive control approach has outstanding tracking performance.

Adaptive Optimal Control of Wave Energy Converters

This paper proposes a hierarchical adaptive optimal control framework for wave energy converters (WECs) to improve their energy conversion efficiency and reduce the required modeling effort to facilitate the control design. Since the WEC dynamics vary significantly at various operation scenarios with different sea states, an efficient adaptive parameter estimation (APE) algorithm is employed to online update several critical WEC model parameters (e.g. the radiation force and excitation force generation coefficients). Based on the updated model, the predesigned non-causal optimal controller can maintain its efficacy. Thus the proposed method combines the strength of optimal control for generating maximum energy output and APE in coping with model parameter variations. Simulation results demonstrate that the proposed optimal control with APE can cope with the model mismatch and variations effectively.

Nonlinear hierarchical control of a quad tilt‐wing UAV: An adaptive control approach

SummaryIn this paper, a nonlinear hierarchical adaptive control framework is proposed for the control of a quad tilt‐wing unmanned aerial vehicle (UAV). An outer loop model reference adaptive controller with robustifying terms creates required forces to be able to move the UAV on a reference trajectory, and an inner loop nonlinear adaptive controller realizes the required attitude angles to achieve these forces. A rigorous stability analysis is provided showing the boundedness of all the signals in this cascaded controller structure. The development and the stability analysis of the controller do not use any linearizations and use the full nonlinear UAV dynamics. The controller is implemented on a high‐fidelity nonlinear tilt‐wing quadrotor model in the presence of uncertainties, wind disturbances, and measurement noise as well as actuator and structural failures. In this work, in addition to earlier modeling studies, the effect of wing‐angle variations, actuator failures, and structural failures and their effect on the center of gravity of the UAV are rigorously and systematically investigated and reflected in the model. Simulation results showing the performance of the proposed controller and a comparison with the fixed controller used in earlier studies are presented in the paper.

Adaptive hierarchical control for output feedback systems

ABSTRACTIn this paper, we address the problem of adaptive hierarchical control for a class of so-called uncertain output feedback systems. The proposed approach is to design an adaptive output interface dynamic by estimating the uncertainties. With the interface connected to the uncertain nonlinear system and a linear abstract system, the system could track approximately the abstraction. Finally, two examples are presented to illustrate our approach.