Hill-climbing Control Research Articles

Primary-Side Indirect Control of the Battery Charging Current in a Wireless Power Transfer Charger Using Adaptive Hill-Climbing Control Technique

This paper addresses the control task of a wireless power transfer (WPT) charger designed for electric vehicles (EVs). The challenge is to maintain a constant battery charging current when the WPT is controlled on the ground side. Indeed, the intermittent latency involved in the wireless data communication between the ground and vehicle sides leads to system instability. To overcome this issue, a new control approach has been proposed in this paper. The proposed technique ensures indirect control of the battery charging current through control of the current on the ground side. The control technique relies on an adaptive hill-climbing algorithm in conjunction with a PI-based controller. The adaptive parameter is adjusted online, during the operation of the charger, only when a new measure of the battery charging current is received on the primary side. This makes it possible to avoid the need for real-time wireless data communication. It should be noted that this aspect is crucial in ensuring the controller’s robustness and stability of the system regardless of potential delays in wireless communication and large misalignments between the coils. The validity of the proposed control technique has been confirmed through simulation. In addition, experimental validation, using a laboratory test bed, demonstrated satisfactory results.

MPPT of Magnus Wind System with DC Servo Drive for the Cylinders and Boost Converter

This paper presents an algorithm MPPT (Maximum Power Point Tracking) for a Magnus wind system with a DC servo drive system (DC drive and BLDC motor) to rotate the turbine cylinders. The optimal cylinders rotation is the one to deliver the maximum power extracted from the wind tracked by fixed and adaptive step HCC (Hill Climbing Control) acting on the servo drive. The proposed wind system consists of a PMSG (Permanent Magnet Synchronous Generator), a three-phase diode rectifier, a DC/DC (boost) converter, and a resistive load. Furthermore, the boost converter acts with the fixed step HCC algorithm to track the maximum power operating point. Therefore, the MPPT for a Magnus wind system requires both tracking for the optimal cylinder speed and the optimal generator speed.

Hill-Climbing for Plantwide Control to Economic Optimum

The application of hill-climbing control to “seek” and drive the unconstrained set point of controlled variables (CVs) to their economic optimum is proposed for economic plantwide control. Its application is demonstrated on a reactor-column recycle process for energy efficiency maximization at given throughput (Mode I) and also for maximizing process throughput (Mode II). In Mode I, a one degree-of-freedom hill-climbing feedback controller on top of the regulatory layer is shown to reduce reboiler duty by 3.7% for a 25% throughput increase compared to that of a constant set point operation. Similarly, in Mode II, a one degree-of-freedom hill-climber achieves 3.0% throughput increase compared to that of a constant set point operation. These results highlight the effectiveness of hill-climbing for economic plantwide control.

Development and simulation of sensor-based irrigation control strategies for cotton using the VARIwise simulation framework

Feedback control systems offer opportunities to accommodate spatial and temporal differences in crop water requirement and to improve the automated irrigation of field crops via real-time data from in-field plant, soil–water and evaporation sensing. This paper describes two sensor-based strategies applied to irrigation control, ‘Iterative Learning Control’ (ILC) and custom-designed ‘Iterative Hill Climbing Control’ (IHCC), implemented in the control simulation and evaluation framework ‘VARIwise’. Simulation of an irrigated cotton crop using soils and merged 1999–2004 weather data of SE Queensland, Australia, and represented by the performance of the well-validated cotton growth and production model OZCOT, permitted the relative performance of differing sensor data types and availability to be evaluated (both as alternatives and in combination) in meeting the requirement to optimise either crop yield or water use efficiency. These simulations indicated that ILC would perform better at maintaining soil–water deficit, whilst IHCC would be better at maximising crop yield when plant and soil sensors were utilised in combination. This work demonstrates that the optimal choice of field sensor(s) and control strategy will be a function of the irrigation objective and the spatial and temporal availability and type of field measurements.

Hill-Climbing for Economic Plantwide Control

The application of hill-climbing control in the plantwide control context for driving the setpoint corresponding to an unconstrained degree of freedom to the economic optimum steady state is demonstrated for a reactor-separator-recycle process. Optimality via hill-climbing is sought for two operation modes. In Mode I, the throughput is fixed and hill-climbing maximizes the process energy efficiency. In Mode II, hill-climbing is used to maximize process throughput. Rigorous dynamic plantwide simulation results show that hill climbing control reduces steam consumption by ~3.7% and increases plant throughput by ~3.0%, compared to constant setpoint operation.

411 木の影を通過するソーラーヴィークルにおける太陽電池発電制御回路の試作と走行実験(新エネルギー(3))

In this study, the authors use an electric motorcycle with photovoltaic modules as an example of solar vehicles. It is a problem that solar irradiance is changing frequently and unequally while the motorcycle is running. We designed and made three different prototypes of photovoltaic power controller (MPPT ; maximum power point tracker). They use conventional algorithm of hill-climbing control method, and named 'mono-system', 'multi-system' and 'enhanced mono-system'. To evaluate these power controllers against changing irradiance, driving experiments under the shadow of trees have done on oval track; we discussed each efficiency of photovoltaic system.

Energy capture by a small wind-energy conversion system

Furling is the most common method used by the small wind-turbine industry to control the aerodynamic power extraction from the wind. A small wind-turbine with furling mechanism and its resulting dynamics are modelled using Matlab/Simulinkplatform in this paper. The model simulates regulating the speed of the wind-turbine via a load-control method. Tip-speed ratio and hill-climbing control methods for maximum-power extraction from a small wind-turbine are investigated. Two dynamic controllers are designed and their behaviours simulated. In the first method, the controller uses the wind-speed and rotor speed information to control the load in order to operate the wind-turbine at its optimal tip-speed ratio. In the second method, the controller compares the output power of the turbine with the previous power, and controls the load based on the power difference. In order to determine a suitable control strategy for the small wind-energy conversion system, several tests are performed. Wind-speed versus power-curve and annual energy capture of the system for each control method are determined for wind conditions in St. John’s, Newfoundland. The annual energy-capture is determined using the bin’s power-curve method. Wind-speed data and Rayleigh distribution of St. John’s, Newfoundland are used to determine the annual energy-capture. The results of the simulations indicate that the energy capture of a wind-turbine depends not only on the control strategy but on the wind-speed and Rayleigh distribution. The results of the investigation lead to the conclusion that the hill-climbing method of control results in a greater annual energy-output.

Bootstrap learning of foundational representations

To be autonomous, intelligent robots must learn the foundations of commonsense knowledge from their own sensorimotor experience in the world. We describe four recent research results that contribute to a theory of how a robot learning agent can bootstrap from the ‘blooming buzzing confusion’ of the pixel level to a higher level ontology including distinctive states, places, objects, and actions. This is not a single learning problem, but a lattice of related learning tasks, each providing prerequisites for tasks to come later. Starting with completely uninterpreted sense and motor vectors, as well as an unknown environment, we show how a learning agent can separate the sense vector into modalities, learn the structure of individual modalities, learn natural primitives for the motor system, identify reliable relations between primitive actions and created sensory features, and can define useful control laws for homing and path-following. Building on this framework, we show how an agent can use self-organizing maps to identify useful sensory features in the environment, and can learn effective hill-climbing control laws to define distinctive states in terms of those features, and trajectory-following control laws to move from one distinctive state to another. Moving on to place recognition, we show how an agent can combine unsupervised learning, map-learning, and supervised learning to achieve high-performance recognition of places from rich sensory input. Finally, we take the first steps toward learning an ontology of objects, showing that a bootstrap learning robot can learn to individuate objects through motion, separating them from the static environment and from each other, and can learn properties useful for classification. These are four key steps in a larger research enterprise on the foundations of human and robot commonsense knowledge.

Developing navigation behavior through self-organizing distinctive-state abstraction

A major challenge in reinforcement learning research is to extend the methods that have worked well on discrete, short-range, low-dimensional problems to continuous, high-diameter, high-dimensional problems, such as robot navigation using high-resolution sensors. Self-organizing distinctive-state abstraction (SODA) is a new, generic method by which a robot in a continuous world can better learn to navigate, by learning a set of high-level features and building temporally extended actions to carry it between distinctive states based on those features. A SODA agent first uses a self-organizing feature map to develop a set of high-level perceptual features while exploring the environment with primitive, local actions. The agent then builds a set of high-level actions composed of generic trajectory-following and hill-climbing control laws that carry it between the states at local maxima of feature activations. In an experiment on a simulated robot navigation task, the SODA agent learns to perform a task requiring 300 small-scale, local actions using as few as nine new, temporally extended actions, significantly improving learning time over navigating with the local actions.

The Spatial Semantic Hierarchy

The Spatial Semantic Hierarchy is a model of knowledge of large-scale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and as a method for robot exploration and map-building. The multiple levels of the SSH express states of partial knowledge, and thus enable the human or robotic agent to deal robustly with uncertainty during both learning and problem-solving. The control level represents useful patterns of sensorimotor interaction with the world in the form of trajectory-following and hill-climbing control laws leading to locally distinctive states. Local geometric maps in local frames of reference can be constructed at the control level to serve as observers for control laws in particular neighborhoods. The causal level abstracts continuous behavior among distinctive states into a discrete model consisting of states linked by actions. The topological level introduces the external ontology of places, paths and regions by abduction to explain the observed pattern of states and actions at the causal level. Quantitative knowledge at the control, causal and topological levels supports a “patchwork map” of local geometric frames of reference linked by causal and topological connections. The patchwork map can be merged into a single global frame of reference at the metrical level when sufficient information and computational resources are available. We describe the assumptions and guarantees behind the generality of the SSH across environments and sensorimotor systems. Evidence is presented from several partial implementations of the SSH on simulated and physical robots.

A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations

Kuipers, B. and Byun, Y.T., A robot exploration and mapping strategy based on a sematic hierarchy of spatial representations, Robotics and Autonomous System, 8 (1991) 47–63. We have developed a robust qualitative method for robot exploration, mapping, and navigation in large-scale spatial environments. Experiments with a simulated robot in a variety of complex 2D environments have demonstrated that our qualitative method can build an accurate map of a previously unkown environment in spite of substantial random and systematic sensorimotor error. Most current approaches to robot exploration and mapping analyze sensor input to build a geometrically precise map of the environment, then extract topological structure from the geometric description. Our approach recognizes and exploits qualitative properties of large-scale before relatively error-prone geometrical properties. [ sensorimotor ↔ control] → topology → geometry At the control level, distinctive places and distinctive travel edges are identified based on the interaction between the robot's control strategies, its sensorimotor system, and the world. A distinctive place is defined as the local maximum of a distinctiveness measure appropriate to its immediate neighborhood, and is found by a hill-climbing control strategy. A distinctive travel edge, similarly, is defined by a suitable measure and a path-following control strategy. The topological network description is created by linking the distinctive places and travel edges.Metrical information is then incrementally assimilated into localgeometric descriptions of places and edges, and finally merged into a global geometric map. Topological ambiguity arising from sensorily indistinguishable places can be resolved at the topological level by the exploration strategy. With this representation, successful navigation is not critically dependent on the accuracy, or even the existence, of the geometrical description. We present examples demonstrating the process by which the robot explores and builds a map of a complex environment, including the effect of sensory errors. We also discuss new research directions that are suggested by this approach.

Hill-climbing control system simulation†

The simulation, of a third-order control system which is optimized by a hill-climbing controller is described. Two of the control system inner loops are optimized using the ‘successive variation in parameter method’ of hill-climbing. The system operates using an unusual performance criterion which was developed because of practical difficulties encountered with the more conventional criteria. Two new features embodied in the system are the use of a compound criterion and adaptive perturbation. These allow the optimum operating point to be varied within a set range, and also for the perturbation magnitude to be varied with the size of the performance criterion.

IEE South Midland Centre Electronics & Control Section: Chairman's address. Hill-climbing control systems

IEE South Midland Centre Electronics & Control Section: Chairman's address. Hill-climbing control systems

Development and application of self-optimising control to coal-fired steam-generating plant

The objects of the experimental programme, which is the subject of the paper, were to assess the technical feasibility and economic return that could be expected from applying an automatic self-optimising control system to coal-fired steam-generating plant. The work centred on the control of combustion, this being directly exposed to disturbances arising from variability of the fuel quality. A hill-climbing control system was used which maximised combustion efficiency by manipulating the air/fuel ratio. As efficiency could not be measured directly, an inferential method based on changes in boiler pressure or turbine output was adopted. Two control strategies, utilising, respectively, sinusoidal and step perturbations of air flow, were investigated. The paper contains estimates of the disturbance spectrum, based on measurements of both coal-quality and boiler-output fluctuations, and shows this to be equivalent to the addition of coloured noise to the system input. The methods of obtaining optimum discrimination against this noise for each of the two strategies are discussed. Experimental data obtained by using the control system to track movements of the peak of the combustion hill are given, a range of 2% excess oxygen being observed. It is concluded that, although the factors governing this movement have not been fully identified, the fineness of grinding is of great importance in determining the optimum combustion conditions. It is also concluded that this form of control is feasible and could lead to an improvement in efficiency of about 0.25%.

Autonomic Control of Respiration

Two aspects of the biological processes controlling respiration in man are discussed in this paper. The first describes how the properties of the blood are used as the basis for a hill-climbing type control system. Deviations from the optimum are corrected by changes in the rate of ventilation of the lungs so that ventilation keeps pace with metabolism. The second aspect considered is the minimization of the energy expenditure for breathing. This involves the neuromuscular part of the respiratory system which selects, for each breath, both the duration and the volume change in the lungs, as well as specifying which muscles are activated.