Two-layer Control Architecture Research Articles

A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.

Hierarchical distributed model predictive control based on fuzzy negotiation

This work presents a hierarchical distributed model predictive control approach for multiple agents with cooperative negotiations based on fuzzy inference. Specifically, a fuzzy-based two-layer control architecture is proposed. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network. The resulting pairwise control sequences are sent to a coordinator in the upper control layer, which merges them to compute the final ones. Furthermore, conditions to guarantee feasibility and stability in the closed-loop system are provided. The proposed control algorithm has been tested on an eight-coupled tank plant via simulation.

Highly-scalable traffic management of autonomous industrial transportation systems

In this paper, we present a novel method for highly-scalable coordination of free-ranging automated guided vehicles in industrial logistics and manufacturing scenarios. The primary aim of this method is to enhance the current industrial state-of-the-art multi-vehicle transportation systems, which, despite their long presence on the factory floor and significant advances over the last decades, still rely on a centralized controller and predetermined network of paths. In order to eliminate the major drawbacks of such systems, including poor scalability, low flexibility, and the presence of a single point of failure, in the proposed control approach vehicles autonomously execute their assigned pick-up and delivery operations by running a fully decentralized control algorithm. The algorithm integrates path planning and motion coordination capabilities and relies on a two-layer control architecture with topological workspace representation on the top layer and state-lattice representation on the bottom layer. Each vehicle plans its own shortest feasible path toward the assigned goal location and resolves conflict situations with other vehicles as they arise along the way. The motion coordination strategy relies on the private-zone mechanism ensuring reliable collision avoidance, and local negotiations within the limited communication radius ensuring high scalability as the number of vehicles in the fleet increases. We present experimental validation results obtained on a system comprising six Pioneer 3DX robots in four different scenarios and simulation results with up to fifty vehicles. We also analyze the overall quality of the proposed traffic management method and compare its performance to other state-of-the-art multi-vehicle coordination approaches.

Fuel-Efficient Driving Strategies for Heavy-Duty Vehicles: A Platooning Approach Based on Speed Profile Optimization

A method for reducing the fuel consumption of a platoon of heavy-duty vehicles (HDVs) is described and evaluated in simulations for homogeneous and heterogeneous platoons. The method, which is based on speed profile optimization and is referred to as P-SPO, was applied to a set of road profiles of 10 km length, resulting in fuel reduction of 15.8% for a homogeneous platoon and between 16.8% and 17.4% for heterogeneous platoons of different mass configurations, relative to the combination of standard cruise control (for the lead vehicle) and adaptive cruise control (for the follower vehicle). In a direct comparison with MPC-based approaches, it was found that P-SPO outperforms the fuel savings of such methods by around 3 percentage points for the entire platoon, in similar settings. In P-SPO, unlike most common platooning approaches, each vehicle within the platoon receives its own optimized speed profile, thus eliminating the intervehicle distance control problem. Moreover, the P-SPO approach requires only a simple vehicle controller, rather than the two-layer control architecture used in MPC-based approaches.

PMP-Based Set-Point Optimization and Sliding-Mode Control of Vehicular Platoons

This paper investigates the problem of set-point optimization and speed tracking control for fuel–time-efficient platooning of vehicles on freeways. A two-layered control architecture is presented for vehicular platooning systems: a set-point optimization layer and a vehicle tracking control layer. In the first layer, a speed-planning algorithm is derived to calculate the speed set-point for the platoon by averaging the optimal speed of each vehicle, which is obtained by solving a fuel–time optimization problem based on Pontryagin’s minimum principle. The second layer contains a set of distributed sliding-mode controllers for vehicle tracking control, which can guarantee string stability of the vehicular platoon with a desired intervehicle spacing. The effectiveness of the presented method is demonstrated via simulations.

Direct Force-Reflecting Two-Layer Approach for Passive Bilateral Teleoperation With Time Delays

We propose a two-layer control architecture for bilateral teleoperation with communication delays. The controller is structured with an (inner) performance layer and an (outer) passivity layer. In the performance layer, any traditional controller for bilateral teleoperation can be implemented. The passivity layer guarantees that, from the operator and environment perspective, the overall teleoperator is passive: The amount of energy that can be extracted from the teleoperator is bounded from below and the rate of increase of the stored energy in the teleoperator is bounded by (twice) the environment and operator supplied power. Passivity is ensured by modulating the performance layer outputs and by injecting a variable amount of damping via an energy-based logic that follows the innovative principle of energy duplication and takes into account the detrimental effects of time delays. In contrast to the traditional teleoperation approach, in which the master and slave controllers implement an as-stiff-as-possible coupling between the master and slave devices, our scheme is specifically designed for direct force-reflecting bilateral teleoperation: The slave controller mimics the operator action, whereas the master controller reflects the slave-environment interaction. We illustrate the performance of the two-layer approach in a challenging experiment with a round-trip communication delay of 300 ms while making and breaking contact with a stiff aluminum environment. Finally, we also compare our controller with the state of the art.

Retrofit Self-Optimizing Control: A Step Forward Toward Real Implementation

After 15 year development, it is still hard to find any real application of the self-optimizing control (SOC) strategy, although it can achieve optimal or near optimal operation in industrial processes without repetitive real-time optimization. This is partially because of the misunderstanding that the SOC requires to completely reconfigure the entire control system, which is generally unacceptable for most process plants in operation, even though the current one may not be optimal. To alleviate this situation, this paper proposes a retrofit SOC methodology aiming to improve the optimality of operation without change of existing control systems. In the new retrofitted SOC systems, the controlled variables (CVs) selected are kept at constant by adjusting setpoints of existing control loops, which therefore constitutes a two-layer control architecture. CVs made from measurement combinations are determined to minimize the global average losses. A subset measurement selection problem for the global SOC is solved though a branch and bound algorithm. The standard testbed Tennessee Eastman process is studied with the proposed retrofit SOC methodology. The optimality of the new retrofit SOC architecture is validated by comparing two state of art control systems by Ricker and Larsson et al., through steady-state analysis as well as dynamic simulations.

Real-Time Optimization of Gold Cyanidation Leaching Process in a Two-Layer Control Architecture Integrating Self-Optimizing Control and Modifier Adaptation

The gold cyanidation leaching process (GCLP) has been prevalent in the hydrometallurgical industry. To better control and optimize the plant operation, mechanistic models have been established which capture the physical behaviors of the GCLP. However, due to various disturbances and uncertainties, an optimized operation based on the nominal model is practically suboptimal. To perform real-time optimization (RTO) of the GCLP, this paper proposes a two-layer control architecture integrating the self-optimizing control (SOC) and modifier adaptation (MA), both of which are useful RTO approaches with distinct advantages. In the lower layer of the proposed control system, the SOC is implemented with measurement combinations as the controlled variables that are tracked at optimally insensitive setpoints to account for parametric disturbances. In the upper layer, the setpoints of self-optimizing controlled variables are further optimized in a tailored MA framework, such that the structural plant–model mismatch is...

Second-order sliding mode attitude controller design of a small-scale helicopter

In this paper, the attitude control of a small-scale helicopter is investigated. The main rotor flapping dynamics is explicitly explored to improve the control performance. A two-layer control architecture is adopted: the inner loop controller is designed combining second-order sliding mode control with extended state observer to control the angular rates and yield good robustness properties with respect to model uncertainties; the outer loop controller is used to control the attitude. Experimental results show that the proposed controller yields excellent performance and robustness.

Two-Level Nonlinear Tracking Control of a Quadrotor Unmanned Aerial Vehicle

This study presents a nonlinear tracking control design for a quadrotor unmanned aerial vehicle (UAV) with a two-layer control architecture. In order to facilitate the control design, a decoupled dynamic model is derived for the quadrotor UAV. Backstepping method is employed to stabilize the closed-loop system and to achieve output tracking. The control structure has a high-level layer for producing the guiding state trajectories, and a low-level layer for tracking in both altitude and attitude dynamics. The designed control scheme can well handle the model nonlinearities as well as the drag effects, and can be directly implemented to produce the PWM control signals. Simulation results are provided to verify the efficiency of the proposed control scheme.

Energy-Efficient SLA Guarantees for Virtualized GPU in Cloud Gaming

Both power consumption and SLA guarantees are important concerns for cloud gaming. Recently, various approaches have been developed to effectively reduce GPU power consumption by making GPU run at low frequencies. However, virtual machines (VMs) running on the same physical GPU with virtualization technology are correlated, because the change of GPU frequencies will affect the SLA performance of all the VMs. In fact, both reducing power consumption and guaranteeing SLA should work together under the considerations of their correlations. This paper proposes a novel two-layer control architecture called energy-efficient SLA guarantees for virtualized GPU (EvGPU) based on well-established feedback control techniques. The first control loop adopts a proportional-integral (PI) controller to ensure SLA guarantees, which in particular are measured at a predefined level of the frames per second (FPS) for each online game. The secondary power control loop then adjusts GPU frequency through dynamic voltage/frequency scaling (DVFS) to reduce power consumption based on the current FPS achieved by the first loop. Empirical results demonstrate that the proposed solution can effectively reduce GPU power consumption, while achieving the required SLA performance in virtualized GPU for cloud gaming.

Cooperative Look-Ahead Control for Fuel-Efficient and Safe Heavy-Duty Vehicle Platooning

The operation of groups of heavy-duty vehicles at a short inter-vehicular distance, known as platoon, allows one to lower the overall aerodynamic drag and, therefore, to reduce fuel consumption and greenhouse gas emissions. However, due to the large mass and limited engine power of trucks, slopes have a significant impact on the feasible and optimal speed profiles that each vehicle can and should follow. Maintaining a short inter-vehicular distance, as required by platooning, without coordination between vehicles can often result in inefficient or even unfeasible trajectories. In this paper, we propose a two-layer control architecture for heavy-duty vehicle platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon. Here, the layers are responsible for the inclusion of preview information on road topography and the real-time control of the vehicles, respectively. Within this architecture, dynamic programming is used to compute the fuel-optimal speed profile for the entire platoon and a distributed model predictive control framework is developed for the real-time control of the vehicles. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios that suggest a possible fuel saving of up to 12% for follower vehicles compared with the use of standard platoon controllers.

Performance-oriented communication topology design for distributed control of interconnected systems

Communication networks provide a larger flexibility for the control design of interconnected systems by allowing the information exchange between the local controllers of the subsystems which can be used to improve the overall system performance. However, the interconnected systems may become unstable due to permanent communication link failures. This article presents a novel two-layer control architecture that allows to jointly improve the system performance which is the decay rate and guarantee the stability of the interconnected system under permanent communication link failures. As a novelty, the design of communication topology between the local controllers is also taken into account. On the other hand, it is still not well understood how significant the role of each possible communication link is in improving the system performance. Another novelty of this article is thus to propose a method based on eigenvalue sensitivity analysis in order to characterize the influence of each possible communication link in improving the performance of the overall system. In addition, for a special class of systems and physical interconnection topology, explicit solutions on communication topology design are derived for the first time. The solutions provide some insights into how the heterogeneity of the subsystem local dynamics, the strength of interconnection and the size of the network affect the optimal communication topology.

Flocking Multiple Microparticles With Automatically Controlled Optical Tweezers: Solutions and Experiments

This paper presents an efficient approach to achieve microparticles flocking with robotics and optical tweezers technologies. All particles trapped by optical tweezers can be automatically moved toward a predefined region without collision. The main contribution of this paper lies in the proposal of several solutions to the flocking manipulation of microparticles in microenvironments. First, a simple flocking controller is proposed to generate the desired positions and velocities for particles' movement. Second, a velocity saturation method is implemented to prevent the desired velocities from exceeding a safe limit. Third, a two-layer control architecture is proposed for the motion control of optical tweezers. This architecture can help make many robotic manipulations achievable under microenvironments. The proposed approach with these solutions can be applied to many bioapplications especially in cell engineering and biomedicine. Experiments on yeast cells with a robot-tweezers system are finally performed to verify the effectiveness of the proposed approach.

A new method to control heat and mass transfer to work piece in a GMAW process

It is proposed to employ melting rate, heat input, and detaching droplet diameter as controlled variables to control heat and mass transfer to work piece in a gas metal arc welding process. A two-layer architecture with cascade configuration of PI and MPC controllers is implemented to incorporate existing constraints on the process variables, improve transient behavior of the closed-loop responses and reduce interaction level. Computer simulation results are presented to indicate usefulness of the proposed controlled variables selection and applying two-layer control architecture to control heat and mass transfer to work piece.

Flexible and Energy-Saving Platooning Control Using a Two-Layer Controller

This paper proposes a method for controlling the behavior of vehicles with respect to platooning techniques such as forming and splitting a platoon and switching lateral control between vehicle-following mode and road-following mode. The proposed control architecture comprises two layers: a status-control layer and a motion-control layer. This two-layer control architecture improves the behavior of individual vehicles and the platoon as a whole without increasing the complexity of the system. Moreover, the proposed control method allows for the formation of a platoon in accordance with a rule designed to promote energy-saving maneuvering at the time of the platoon’s formation. The proposed control method is validated by conducting experiments using small electric vehicles.

Hierarchical control of automotive electric power steering system and anti-lock brake system: theory and experiment

This paper studies integrated control of chassis control systems to achieve the goal of functional integration of the control systems. A two-layer hierarchical control architecture is developed for integrated control of Electric Power Steering (EPS) system and Anti-Lock Brake System (ABS). The upper-layer controller is designed to coordinate the interactions between the EPS system and the ABS. In the lower layer, the two controllers including the EPS system and the ABS are designed independently to achieve their local control objectives. Both a simulation investigation and an on-vehicle experimental study are performed to demonstrate the effectiveness of the proposed hierarchical control system. Simulation results show that the system is able to improve the lateral stability of the vehicle, and at the same time ensure the steering agility and braking performance of the vehicle. Moreover, on-vehicle experiment results conform to the simulation results.

PARTIC: Power-Aware Response Time Control for Virtualized Web Servers

Both power and performance are important concerns for enterprise data centers. While various management strategies have been developed to effectively reduce server power consumption by transitioning hardware components to lower power states, they cannot be directly applied to today's data centers that rely on virtualization technologies. Virtual machines running on the same physical server are correlated because the state transition of any hardware component will affect the application performance of all the virtual machines. As a result, reducing power solely based on the performance level of one virtual machine may cause another to violate its performance specification. This paper proposes PARTIC, a two-layer control architecture designed based on well-established control theory. The primary control loop adopts a multi-input multi-output control approach to maintain load balancing among all virtual machines so that they can have approximately the same performance level relative to their allowed peak values. The secondary performance control loop then manipulates CPU frequency for power efficiency based on the uniform performance level achieved by the primary loop. Empirical results demonstrate that PARTIC can effectively reduce server power consumption while achieving required application-level performance for virtualized enterprise servers.

Power-Steering Control Architecture for Automatic Driving

The unmanned control of the steering wheel is, at present, one of the most important challenges facing researchers in autonomous vehicles within the field of intelligent transportation systems (ITSs). In this paper, we present a two-layer control architecture for automatically moving the steering wheel of a mass-produced vehicle. The first layer is designed to calculate the target position of the steering wheel at any time and is based on fuzzy logic. The second is a classic control layer that moves the steering bar by means of an actuator to achieve the position targeted by the first layer. Real-time kinematic differential global positioning system (RTK-DGPS) equipment is the main sensor input for positioning. It is accurate to about 1 cm and can finely locate the vehicle trajectory. The developed systems are installed on a Citroe/spl uml/n Berlingo van, which is used as a testbed vehicle. Once this control architecture has been implemented, installed, and tuned, the resulting steering maneuvering is very similar to human driving, and the trajectory errors from the reference route are reduced to a minimum. The experimental results show that the combination of GPS and artificial-intelligence-based techniques behaves outstandingly. We can also draw other important conclusions regarding the design of a control system derived from human driving experience, providing an alternative mathematical formalism for computation, human reasoning, and integration of qualitative and quantitative information.

Research on vacuum gripper based on fuzzy control for micromanipulators

This paper presents a vacuum gripper (as an actuator of an intelligent micromanipuktor) for micro objects (with a diameter of 100 ∼ 300µm) assembly tasks. The gripper is composed of a vacuum unit and a control unit. The vacuum unit with a proportional valve and a pressure sensor, and the control unit with a PC+MCU two-layered control architecture are designed. The mechanical structure, workflow and major programs of the micro-gripper are presented.