Energy-efficient Controllers Research Articles

Energy-efficient heating control for smart buildings with deep reinforcement learning

Buildings account for roughly 40% of the total energy consumption in the world, out of which heating, ventilation, and air conditioning are the major contributors. Traditional heating controllers are inefficient due to lack of adaptability to dynamic conditions such as changing user preferences and outside temperature patterns. Therefore, it is necessary to design energy-efficient controllers that can improvise occupant thermal comfort (deviation from setpoint temperature) while reducing energy consumption. This research presents a Deep Reinforcement Learning (DRL)-based heating controller to improve thermal comfort and minimize energy costs in smart buildings. We perform extensive simulation experiments using real-world outside temperature data. The results show that the DRL-based smart controller outperforms a traditional thermostat controller by improving thermal comfort between 15% and 30% and reducing energy costs between 5% and 12% in the simulated environment. A second set of experiments is then performed for the case of multiple buildings, each having its own heating equipment. The performance is compared when the buildings are controlled centrally (using a single DRL-based controller) versus decentralized control, where each heater is controlled independently and has its own DRL-based controller. We observe that as the number of buildings and differences in their setpoint temperatures increase, decentralized control performs better than a centralized controller. The results have practical implications for heating control, especially in areas with multiple buildings such as residential complexes with multiple houses.

Improving Energy Efficiency of an Autonomous Bicycle with Adaptive Controller Design

A method is proposed to achieve lateral stability of an autonomous bicycle with only the rotation of the front wheel. This can be achieved with a classic controller. However, if the energy consumption of the bicycle also has to be minimized, this solution is not valid. To solve this problem, an adaptive controller has been designed, which modifies its gains according to the bicycle’s forward velocity, adapting its response with minimum energy consumption and satisfying the design specifications. The study demonstrates the efficiency of the proposed control, achieving an energy saving of 73 . 8 % in trajectory tracking with respect to a conventional proportional-integral ( P I ) controller. These results show the importance of designing energy-efficient controllers, not only for autonomous vehicles but also for any automatic system where the energy consumption can be minimized.

Towards Synthesizing Energy-efficient Controllers for Modern Production Systems from Scenario-based Specifications

Abstract Due to continuously rising electricity prices, the energy efficiency of production systems is an increasingly important factor in industrial manufacturing. One goal is therefore to reuse the braking energy of manipulator axes. The acceleration and deceleration phases of axes must be synchronized such that, ideally, the braking energy can be reused immediately within the system. In our current technique for the energy-efficient motion planning of concurrent movements of axes, we do not yet consider changing the discrete control logic to reorder movement sequences. This is difficult, since this reordering must not violate critical system requirements. In this paper, we outline a new technique for automatically synthesizing energy-efficient discrete controllers from a scenario-based specification of a production system, which we enrich with information about consumed and generated energy. Last, we provide an outlook on challenging open research problems.

An optimisation model for the design of energy efficient discrete event controllers

In this work, we propose a mixed integer programming model that can be applied to design the rule base matrix of a discrete event controller (DEC) in order to optimise energy consumption of the resources while adhering to meet budget and completion time constraints. The model is solved using a hybrid linear programming and genetic algorithm (GA) approach and the obtained optimised schedule is transformed into matrices as inputs to the supervisory controller DEC. The proposed methodology is implemented on an assembly project and yields encouraging results in generating a good quality schedule within an acceptable time. It is also evident that the proposed method arrives at optimal solution with better completion time as compared to MIP method.

Pathways for optimization-based drug delivery

An overview of our research results on the implementation of model predictive control (MPC) on-chip is presented, with central focus the development of small-size, energy efficient controllers suitable for drug delivery systems and devices. Profiling simulations coupled with codesign techniques are used in order to reveal algorithmic bottlenecks and to effectively customize implementation designs. Hardware in-the-loop simulations using a general purpose processor and a field programmable gate array, and emulations of an application specific processor are provided. The performance measurements and estimates illustrate that MPC is suitable for on-chip real-time optimal control of complex systems with fast dynamics.

Sensory Feedback Mechanism Underlying Entrainment of Central Pattern Generator to Mechanical Resonance

Rhythmic body motions observed in animal locomotion are known to be controlled by neuronal circuits called central pattern generators (CPGs). It appears that CPGs are energy efficient controllers that cooperate with biomechanical and environmental constraints through sensory feedback. In particular, the CPGs tend to induce rhythmic motion of the body at a natural frequency, i.e., the CPGs are entrained to a mechanical resonance by sensory feedback. The objective of this paper is to uncover the mechanism of entrainment resulting from the dynamic interaction of the CPG and mechanical system. We first develop multiple CPG models for the reciprocal inhibition oscillator (RIO) and examine through numerical experiments whether they can be entrained to a simple pendulum. This comparative study identifies the neuronal properties essential for the entrainment. We then analyze the simplest model that captures the essential dynamics via the method of harmonic balance. It is shown that robust entrainment results from a strong, positive-feedback coupling of a lightly damped mechanical system and the RIO consisting of neurons with the complete adaptation property.

Modelling and Simulation of Energy-Efficient Controllers for Pumps Within Process Industry

The submersible pump is one of the major energy-consuming devices on the utility side. This work considers the modelling, simulation, and control of induction motor-driven submersible pump. A nOnlinear fifth-order model for the motor and a second order for the submersible pump have been used to include nonlinearities such as saturation of magnetic core and efficiency of the pump when powered by sinusoidal and VSI-fed voltage source. The discharge of the submersible pump is very difficult to monitor under closed-loop conditions. Hence a discharge estimator is developed based on motor input voltage, line current, and frequency to predict the quantum of water flow under realistic conditions. A closed-loop discharge controller is designed to control discharge without actually monitoring it. A considerable energy savings is obtained in the closed-loop operation.

Direct product quality control for energy efficient climate controlled transport of agro-material

A (model-based) Product Quality Controller is presented for climate controlled operations involving agro-material, such as storage and transport. This controller belongs to the class of Model Predictive Controllers and fits in a previously developed hierarchical control structure. The new Product Quality Controller rejects disturbances and tracks the product quality by means of the product responses respiration and fermentation. To achieve an energy efficient operation the presented controller is closely linked with the (existing) local controllers. Local optimisation on the level of these local controllers allows (controlled) high-frequent climate fluctuations. This results in significant energy savings. The Product Quality Controller and the energy efficient local controllers are implemented in small-scale and full-scale industrial case studies on controlled atmosphere-container transport of apples. This yields direct control of product quality and a significant reduction in energy consumption.