Rule-based Control Algorithm Research Articles

Supervisory Control of Parallel Hybrid Electric Vehicles for Fuel and Emission Reduction

Past research on hybrid electric vehicles (HEVs) focused primarily on improving their fuel economy. Emission reduction is another important performance attribute that needs to be addressed. When emissions are considered for hybrid vehicles with a gasoline engine, horizon-based optimization methodologies should be used because the light-off of the three-way catalytic converter heavily depends on the warming-up of catalyst temperature. In this paper, we propose a systematic design method for a cold-start supervisory control algorithm based on the dynamic programming (DP) methodology. First, a system-level parallel HEV model is developed to efficiently predict tailpipe emissions as well as fuel economy. The optimal control problem for minimization of cold-start emissions and fuel consumption is then solved via DP. Since DP solution cannot be directly implemented as a real-time controller, more useful control strategies are extracted from DP solutions over the entire state space via the comprehensive extraction method. The extracted DP results indicate that the engine on/off, gear-shift, and power-split strategies must be properly adjusted to achieve fast catalyst warm-up and low cold-start tailpipe emissions. Based on DP results, we proposed a rule-based control algorithm that is easy to implement and adjust while achieving near-optimal fuel economy and emission performance.

Development of integrated controller for a compound hybrid excavator

This paper presents an integrated control algorithm for a compound hybrid excavator. Both conventional hydraulic excavator and compound hybrid excavator models are developed to compute fuel consumption. The excavator simulation models, which consist of engine, pump and electric components have been validated with test data. To design the hybrid system’s integrated control algorithm which determines the output power of two motor/generators and ultra capacitor, a rule-based control algorithm to improve fuel economy is proposed. Simulation studies have been conducted for a typical digging process to investigate the performance of the proposed rulebased controller. Simulation results show that 26% reduction of fuel consumption can be obtained using the proposed controller to the compound hybrid excavator compared to the conventional hydraulic excavator.

Model-based predictive control of vehicle dynamics

This paper proposes a new model-based hierarchical control strategy for the application of brake torque distribution to vehicle stability control. A three-layer control architecture is adopted, and the focus of the paper is on the intermediate layer which employs a form of model predictive control. To achieve this a non-linear vehicle model is linearised at successive time instants about non-equilibrium operating points. Simulations show that the new controller is more efficient (uses reduced brake authority) compared to a conventional rule-based control algorithm. It also has the advantage of being highly flexible for future application in fully integrated chassis systems.

Forming Shaped/Molded Structures by Integrating Meltblowing and Robotic Technologies

A novel system is described that forms three-dimensional (3D) molded nonwoven structures through proper integration of a laboratory scale meltblown unit with a small die and a six-axis robot. The 3D fiberweb structures can be formed by deposition of fibers from the die of the meltblown unit, which is manipulated by the robot, on any desired 3D mold. The mold rotational and surface speeds can be controlled by an additional external axis. The die is connected by two flexible hoses to the melt extruder of the meltblown unit and a hot air supply system. This system directly sprays fibers onto a 3D mannequin mold to produce structures from polypropylene polymers. With varying degrees of success. several robot manipulation algorithms of fiber deposition on the mold are developed to accurately control the basis weight uniformity the fiberwebs. A rule-based control algorithm using a linear variable differential transducer to map the mold contour results in the greatest fiberweb basis weight uniformity.

New controller for functional electrical stimulation systems

A novel, self-contained controller for functional electrical stimulation systems has been designed. The development was motivated by the need to have a general purpose, easy to use controller capable of stimulating many muscle groups, thus restoring complex motor functions (e.g. standing, walking, reaching, and grasping). The designed controller can regulate the frequency, pulse duration, and charge balance on up to 16 channels, and execute pre-programmed and sensory-driven control operations. The controller supports up to eight analog and six digital sensors, and comprises a memory block for including history of the sensory data (time series). Five independent timers provide the basis for the multi-modal and multi-level control of movement. The PC compatible interface is realised via an IR serial communication channel. The PC based software is user friendly and fully menu driven. This paper also presents a case study where the controller was implemented to restore walking in a paraplegic subject. The assistive system comprised the novel controller, the power and output stages of an eight-channel FES system (IEEE Trans Rehabil Eng, TRE-2 (1994) 234), ankle-foot orthoses, and a rolling walker. Stimulation was applied with surface electrodes positioned over the motoneurons that innervate muscles responsible for the hip and knee flexion and extension. The sensory system included goniometers at knee and hip joints, force-sensing resistors built in the shoe insoles, and digital accelerometers at the hips. A rule-based control algorithm was generated following a two-step procedure: (1) simulation and (2) machine learning as described in earlier studies (IEEE Trans Rehab Eng, TRE-7 (1999) 69). The paraplegic subject walked faster, and with less physiological effort, when automatic control was applied as compared to hand-control. This case study, as well as a previous one for assisting grasping (The design and testing of a new programmable electronic stimulator. N. Fisekovic, MS thesis. University of Belgrade, Belgrade, 2000) indicate that the novel control unit is effectively applicable to FES systems.

Temperature control for food in pots on cooking hobs

A control system for the temperature of food in a cooking pot is introduced. The cooking pot is heated on a consumer cooking hob. The temperature of the pot side is used as input for the controller. It is measured by an infrared detector. The infrared detector is a thermopile. The sensor signal is evaluated by a rule-based control algorithm. The system is simple, reliable, and very user friendly. An exact temperature settlement in a consumer hob is available for the first time. The system is used for automatic cooking.

Neural networks for the identification of the aluminium electrolysis process

The proper operation of an aluminium electrolysis cell relies strongly on the ability of the control system to devise an appropriate alumina feeding sequence and on the ability to overcome anodic effects. This complex system is normally controlled using a rule-based control algorithm that stems from the process knowledge acquired over many years of operation. This paper first considers the use of feedforward neural networks to predict, 15 min in advance, two important variables that are directly used in the rule-based control algorithm: the cell resistance and the cell fast dynamic trend indicator. The addition of these two predictions greatly enhanced the operability of the cell. The linear vector quantization (LVQ) neural network was then used to classify the current operating state of the electrolysis cell in view of adapting the rule-based control strategy to each particular state. This classification procedure allowed to devise more appropriate control strategies and to estimate the alumina concentration of the cell.

A Study of Parallelism of Fuzzy Control Algorithms for Neutron Power Control

This paper presents a study on the feasibility to process in parallel some of the stages of fuzzy rule based control algorithms (FRBC). Three FRBC’s are considered for the control of neutron power in a research nuclear reactor, the first FRBC is a two-input one-output fuzzy controller, the second one is a three-input one-output FRBC, the third controller is a two-input one-output FRBC in which a boolean selection of the output membership functions (OMF) is made according to three different zones of the reactor power level. Computer simulations determine the time required by each of the FRBC’s to process the input to give an adequate ouput (a controller cycle), under different degrees of parallelism. Comparisons of the main features of the parallel and non-parallel (sequential) FRBC’s are presented.

Fuzzy logic in anaesthesia

The development of on-line drug administration strategies in operating theatres represents a highly safety-critical situation. The usefulness of different levels of simulation prior to clinical trials has been shown in previous studies in anaesthesia. Thus, in earlier work on pole-placement self-tuning for muscle relaxation, a dual computer real-time simulation was undertaken, subsequent to algorithm validation via off-line simulation. In the present work, supervised fuzzy rule-based control algorithms are being used. The control software was implemented on the actual machine to be used in theatre, while another computer acted as a real-time patient simulator. It provides a fast design and simulation environment for safety-critical validation. The resultant validated controllers are now being used clinically.

Rule-Based Control Algorithm for Active Tuned Mass Dampers

A rule-based control algorithm for active tuned mass dampers (TMDs) is proposed. First, perturbation solutions of the linear quadratic regulator (LQR) feedback gains for the active TMD system are derived. Using these solutions, interaction of the TMD and the actuator force is discussed in detail. Then, an intelligent rule-based control scheme is constructed based on this discussion along with a previously developed semiactive control method. Fuzzy logic is incorporated with the rule bases to implement analytical and/or intuitive knowledge about the TMD into control rules. The algorithm consists of two parts: (1) a variable gain displacement feedback control; and (2) a variable TMD damping control. The first one is applied when the TMD amplitude is small to make the TMD more effective, and the second one is applied when the TMD amplitude is large to dissipate the energy. Treatment of constraints on control force capacity and TMD displacement is emphasized in construction of the controller. Performance of the proposed method is demonstrated by numerical studies of continuous structures with widely spaced natural frequencies subject to seismic excitation.

The Potential of Real Time Control to reduce Combined Sewer Overflow

The potential of Real Time Control (RTC) to reduce the overflow volume from a combined sewer system has been investigated for a catchment in Copenhagen, named Strandvaenget. The results have been compared with the alternative of increasing the storage volume of the system. The RTC strategy has been derived using a mathematical optimization and a rule based method. As the only objective is to reduce the Combined Sewer Overflow (CSO), the developed rule based control algorithm can achieve almost the same results as the optimization method. From the results of time series calculations, it can be concluded that the introduction of RTC appears very promising when the mean yearly CSO volume has to be reduced. In this research, the potential of RTC is investigated for an increasing amount of storage volume and for an increasing demand for reduction of CSO volume.

A self-learning rule-based control algorithm for chamferless part mating

Abstract In this paper a new active assembly algorithm is proposed for chamferless pprecision parts mating. The motivation of the development is based upon the observation that present assembly tasks require an extremely high positional accuracy and a good knowledge of mating parts. However, use of conventional assembly methods alone makes it difficult to achieve satisfactory assembly performance because of the complexity and the uncertainties of the process and its environments such as imperfect knowledge of the parts being assembled as well as the limitations of the devices performing the assembly. To cope with these problems, a self-learning rule-based control algorithm for precision assembly is proposed by integrating fuzzy set theory and neural networks. In this algorithm, fuzzy set theory copes with the complexity and the uncertainties of the assembly process, while a neural network enhances the assembly scheme so as to learn fuzzy rules from experience and adapt to changes in environment of uncertainty and imprecision. The performance of the proposed control algorithm is evaluated through a series of experiments. The results show that the self-learning fuzzy control scheme can be effectively applied to chamferless precision part mating.

Anaesthesia simulators for the design of supervisory rule-based control in the operating theatre

The development of online drug administration strategies in operating theatres represents a highly safety-critical situation. The usefulness of different levels of simulation prior to clinical trials has been shown in previous studies in muscle relaxant anaesthesia. Thus, in earlier work on pole-placement self-tuning for muscle relaxation a dual computer real-time simulation was undertaken, subsequent to algorithm validation via off-line simulation. In the present approach a supervised rule-based control algorithm is used. The control software was implemented on the actual machine to be used in theatre, while another computer acted as a real-time patient simulator. This set-up has further advantages of providing accurate timing and also finite data accuracy via the ADC/DAC interface, or the equivalent digital lines. Also, it provides for the controller design fast simulation studies compared with the real-time application.

Recent Trends in Distributed Process Control Computers

Advances in computer based control systems have closely followed developments in computer technology. The advent of powerful 32 bit microcomputers, high resolution colour graphic displays and local area networks in computer technology will lead to implementation of sophisticated control algorithms for controlling individual control loops, user friendly display systems, menu driven software selection and integration of distributed computer control in global hierarchical plant control. Distributed computer control systems of the future will be easy to specify, design, install and use. Systems will be designed with better reliability and safety. Developments in expert systems will lead to some heuristic rule based control algorithms.

Matrix Quadratic Solutions

Matrix quadratic equation solution derivation applied in finding steady state solutions of Riccati differential equations with constant coefficients