Model-based Predictive Control Algorithm Research Articles

Effective Nonlinear Predictive and CTC-PID Control of Rigid Manipulators

Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. The main proposal of the paper are model-based predictive control (MPC) algorithms, with nonlinear state-space models and most recent disturbance attenuation technique. This technique makes controller design and calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is an investigation of influence of sampling period length and computational delay time on performance of the algorithms, which is practically important when using model-based algorithms and fast sampling.

An energy management strategy to reduce the comprehensive cost of hybrid energy storage systems in electric vehicles

A real-time adaptive energy management strategy (EMS) used a model-based predictive control algorithm that continuously adapted to the changing driving patterns and traffic conditions. With battery degradation in an operation being considered, the algorithm was designed to minimize the total cost of electricity consumed by vehicles with hybrid energy storage systems (HESSs) while ensuring that the battery and supercapacitor cell were not overcharged or overdischarged. First, the objective function was taken as the instantaneous minimization of the comprehensive cost. Second, a hierarchical instantaneous optimal control EMS (HIOC-EMS) was suggested to solve the optimal power coupling coefficient of the supercapacitor that satisfied the constraints at any moment. Third, the HIOC-EMS was proven to be an efficient and robust method for optimizing the energy management system of HESSs. The experimental results of three different driving cycles showed that the HIOC-EMS, when compared to the particle swarm-optimized fuzzy EMS (PFZY-EMS), achieved reductions in battery losses of 18.41%, 13.94%, and 20.37% and comprehensive cost reductions of 11.16%, 7.37%, and 9.61%, respectively, in the three cycles. Furthermore, compared to the dynamic programming EMS (DP-EMS), the HIOC-EMS resulted in increased battery losses of 14.87%, 10.77%, and 4.87% and increased comprehensive costs of 8.48%, 2.98%, and 1.55%, respectively. These results proved the effectiveness of the HIOC-EMS in reducing the usage cost of electric vehicles with HESSs.

TRACKING OF NON-STANDARD TRAJECTORIES USING MPC METHODS WITH CONSTRAINTS HANDLING ALGORITHM

In recent decades, a Model-Based Predictive Control (MPC) has revealed its dominance over other control methods such as having an ability of constraints handling and input optimization in terms of the value function. However, the complexity of the realization of the MPC algorithm on real mechatronic systems remains one of the major challenges. Traditional predictive control approaches are based on zero regulation or a step change. Nevertheless, more complicated systems still exist that need to track setpoint trajectories.
 Currently, there is an active development of robotics and the creation of transport networks of movement without human participation. Therefore, the issue of programming the given trajectories of vehicles is relevant. In this article, authors reveal the alternative solution for tracking non-standard trajectories in spheres such as robotics, IT in mechatronics, etc., that could be used in self-driving cars, drones, rockets, robot arms and any other automized systems in factories.
 The ability of Model-Based Predictive Control (MPC) such as the constraints handling and optimization of input in terms of the value function makes it extremely attractive in the industry. Nevertheless, the complexity of implementation of MPC algorithm on real mechatronic systems remains one of the main challenges.
 Secondly, common predictive control algorithms are based on the regulation approach or a simple step shift. However, there exist systems that are more complicated where a setpoint to be tracked is given in the form of trajectories. In this project, there were made several modifications in order to improve an MPC algorithm to make better use of information about the trajectories.

World’s first self-driving amphibious bus

Amphibious buses are extensively used worldwide for transporting people to and from tourist attractions across water and land. Although numerous studies on self-driving technologies have been reported, research on the automatic operation and navigation of an amphibious vehicle has been sparse; moreover, owing to the size of the amphibious vehicles, automatic transport of multiple people is not possible. Therefore, in this study, we attempted to realize unmanned operation of a sightseeing amphibious bus for 45 passengers. The bus was outfitted with a by-wire system. On the vessel side, an actuator, similar to that used in JOY cars, was installed to turn the captain’s steering wheel. We also developed a software for the automatic operation and navigation of the bus. The relationship between the car’s steering-wheel angle and the front-tire angles is linear, whereas that between the captain’s steering-wheel angle and the vessel’s rudder-plate angle is not (and was approximated with a sixth-order polynomial). Furthermore, Autoware—a leading autonomous-driving software utilizing model-based predictive control algorithms to control the steering wheel of automobiles—was employed in this work. These algorithms were altered using Nomoto’s KT vessel model equation to improve the accuracy of vessel-path tracking. To the best of our knowledge, till date, no studies have documented the functioning of self-driving vessels using predictive controls based on Nomoto’s KT vessel model equation. In accordance with the vessel navigation rules based on the Autoware obstacle avoidance logic, LiDAR, cameras, and sonars were employed to detect obstructions and give-way paths. Thus, we successfully demonstrated the working of world's first self-driving amphibious bus, with automated controls for entering/exiting water and during give-way operations.

Central Non-Linear Model-Based Predictive Vehicle Dynamics Control

Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially or fully automated driving functions, the occupants’ perception of safety and ride comfort changes. Both aspects are focused even more and have to be enhanced. In general, research on vehicle dynamics control systems is a field that has already been well researched. With regard to the mentioned aspects, however, a central control structure features sufficient potential by exploiting synergies. Furthermore, a predictive mode of operation can contribute to achieve these objectives, since the vehicle can act in a predictive manner instead of merely reacting. Consequently, this contribution presents a central predictive control system by means of a non-linear model-based predictive control algorithm. In this context, roll, self-steering and pitch behavior are considered as control objectives. The active roll stabilization demonstrates an excellent control quality with a root mean squared error of 7.6953×10−3 rad averaged over both validation maneuvers. Compared to a vehicle utilizing a conventional control approach combined with a skyhook damping, pitching movements are reduced by 19.75%. Furthermore, an understeering behavior is maintained, which corresponds to the self-steering behavior of the passive vehicle. In general, the central predictive control, thus, increases both ride comfort and safety in a holistic way.

Multi-zone field study of rule extraction control to simplify implementation of predictive control to reduce building energy use

Model-based predictive control (MPC) has been promoted as a software-based energy saving approach for buildings since the 2000s. However, there has not been significant industry adoption of MPC using current approaches. This paper proposes and tests an approach to MPC using rule extraction (RE) that can be easily implemented in building controllers to override sub-optimal control. The resulting decision trees could be implemented by building control programmers to save energy when ambient conditions are predicted to satisfy the thermal requirements of the spaces. A detailed MPC algorithm using inverse models was implemented in 27 rooms of an institutional building to provide data for a classification learning approach. Cooling and heating season decision trees were generated based on the inputs and outputs of the detailed MPC algorithm. An ensemble and sample randomization were used to generalize the trees across rooms and prevent overfitting to individual rooms. Cooling season MPC and RE energy savings were 42% and 27% respectively and heating season MPC and RE energy savings were 18% and 33% respectively. Both algorithms also reduced the difference between the temperature setpoint and measured indoor air temperature compared to the reactive controls.

Model-based Predictive Control of an HVAC System

Abstract This paper presents the application of two model-based predictive control (MPC) algorithms on the cooling system of an office building. The two strategies discussed are a simple MPC, and an adaptive MPC algorithm connected to a model predictor. The cooling method used represents the air-conditioning unit of an HVAC system. The temperature of the building’s three rooms is controlled with fan coil units, based on the reference temperature and with different constraints applied. Furthermore, the building model is affected by dynamically changing interior and exterior heat sources, which we introduced into the controller as disturbances.

Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing

This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.

Experimental application of classification learning to generate simplified model predictive controls for a shared office heating system

The application of model-based predictive control (MPC) to commercial buildings has been a topic of increasing interest in the literature. Despite significant energy saving potential, commercial adoption has been limited. The literature suggests that the limited proliferation of MPC in buildings is due to a mismatch between the mathematical complexity of MPC and the expertise of practitioners. Accordingly, simplified MPC (sMPC) approaches may improve uptake. This paper applies an approach to MPC where rules are extracted from a detailed MPC algorithm to produce an algebraic model of fewer than 50 lines of code. The goal is to produce an sMPC that will be more accepted by practitioners. Testing in a shared office was executed over a 5-week period of the heating season from March 11 to April 14. The detailed MPC algorithm presented in this paper reduced heating energy consumption by 23% compared with conventional reactive control when tested under similar weather conditions. The sMPC algorithm achieved 95% of the heating energy savings of the detailed MPC algorithm with significantly less complexity. Based on this favorable result for a single case, further work should be conducted with larger sample sizes to conclusively determine if rule extraction can produce an effective sMPC.

Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes.

Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise. Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm. The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model (P < .001) and the standard MSO (P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity. Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

Fault-Tolerant Temperature Control Algorithm for IoT Networks in Smart Buildings

The monitoring of the Internet of things networks depends to a great extent on the availability and correct functioning of all the network nodes that collect data. This network nodes all of which must correctly satisfy their purpose to ensure the efficiency and high quality of monitoring and control of the internet of things networks. This paper focuses on the problem of fault-tolerant maintenance of a networked environment in the domain of the internet of things. Based on continuous-time Markov chains, together with a cooperative control algorithm, a novel feedback model-based predictive hybrid control algorithm is proposed to improve the maintenance and reliability of the internet of things network. Virtual sensors are substituted for the sensors that the algorithm predicts will not function properly in future time intervals; this allows for maintaining reliable monitoring and control of the internet of things network. In this way, the internet of things network improves its robustness since our fault tolerant control algorithm finds the malfunction nodes that are collecting incorrect data and self-correct this issue replacing malfunctioning sensors with new ones. In addition, the proposed model is capable of optimising sensor positioning. As a result, data collection from the environment can be kept stable. The developed continuous-time control model is applied to guarantee reliable monitoring and control of temperature in a smart supermarket. Finally, the efficiency of the presented approach is verified with the results obtained in the conducted case study.

Torque-Ripple Reduction and Fast Torque Response Strategy for Predictive Torque Control of Induction Motors

This paper proposes a simple control method of induction motors (IMs) with improved dynamic response and steady-state performance by using a model-based predictive control algorithm. The predictive torque control (PTC) method analyzes the relationship between the electrical torque and stator voltage of IMs by using the stator voltage vector magnitude to obtain the reference voltage vector angle and accurately control the electrical torque. However, the conventional PTC method has a fixed reference voltage vector magnitude and thus has a large torque ripple in the steady state and slow dynamics in the transient state. The proposed PTC method is based on mathematical equations, which allow minimization of torque error and calculation of a relevant reference voltage to establish a better dynamic response in the transient state and accurate control performance in the steady state. Excellent steady-state performance and a quick dynamic response of the proposed method are proved by the simulation and experimental results.

RBF-ARX model-based robust MPC for nonlinear systems with unknown and bounded disturbance

Considering the unknown and bounded disturbance, a RBF-ARX model-based Robust Predictive Control (RBF-ARX-RPC) algorithm for output tracking control without relying on steady state knowledge is proposed for a class of smooth nonlinear systems. From the identified RBF-ARX model, a local linearization state-space model that considers the modeling error and bounded uncertain disturbance is obtained to represent the current behavior of the nonlinear system, and a polytopic uncertain linear parameter varying (LPV) state-space model is built to represent the future system's nonlinear behavior between the output deviation and input increment. Based on the two state-space models, a quasi-min–max robust MPC algorithm is designed for output tracking control of the nonlinear system under the unknown and bounded disturbance, which does not rely on steady state knowledge. Optimization problem of the RBF-ARX-RPC algorithm is finally converted to a convex linear matrix inequalities (LMIs) optimization problem, and the stability is guaranteed by the use of parameter-dependent Lyapunov function and feasibility of the LMIs. The comparative experiments demonstrate the effectiveness of the proposed RBF-ARX-RPC strategy on a continuously stirred tank reactor (CSTR) simulator.

The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems

This paper introduces the Intelligent MBPC (IMBPC) HVAC system, a complete solution to enable Model-Based Predictive Control (MBPC) of existing HVAC installations in a building. The IMPBC HVAC minimizes the economic cost needed to maintain controlled rooms in thermal comfort during the periods of occupation. The hardware and software components of the IMBPC system are described, with a focus on the MBPC algorithm employed.The installation of IMBPC HVAC solution in a University building is described, and the results obtained in terms of economical savings and thermal comfort obtained are compared with standard, temperature regulated control.

Generalized model-based predictive weather control for the control of free cooling by enhanced night-time ventilation

Free cooling by enhanced night-time ventilation could be an efficient technique for decreasing the energy demand for the cooling of buildings. Such systems use the ambient cold of surrounding air, which is transported into buildings by mechanical fan-driven ventilation systems. Only carefully designed and operated systems can be efficient enough to compete with other free-cooling techniques, such as evaporative cooling or even compressor-driven mechanical cooling systems. The efficiency of free cooling by enhanced night-time ventilation could be significantly improved if a model-based predictive weather control algorithm is used for operation control. The aim of this article is to present a generalized model-based predictive weather control (G-MPWC) algorithm that was developed with a detailed short time step numerical simulation of the thermal response of the building and free cooling system and simplified weather forecast data. The result of the G-MWPC is a set of control matrixes that includes data on the forecast required night-time air exchange rate and forecast daily coefficient of the performance of the free cooling system regarding the forecast daily average ambient air temperature and amplitude and the pre-set free cooling system on/off temperature difference. An additional matrix that includes data on forecast maximum indoor air temperature is developed for the case of the free cooling system being unable to fulfil pre-set thermal comfort requirements. A unique set of G-MWPS control matrixes must be developed for specific buildings and building operation conditions; afterwards, that control matrix can be used for the predicted controlling of the free cooling system by night time ventilation for the whole range of summer time meteorological conditions. In this article, the method and numerical model for developing a G-MPWC algorithm is presented as well as an example of a control matrix developed for a typical office room. The G-MPWC algorithm was validated for the case of a free-cooled office room.

Model predictive energy control of ventilation for underground stations

Smart building systems are opening up new markets, nevertheless the implementation of these novel technologies still lacks suitable and proven whole engineering solutions in complex buildings. This paper presents a detailed approach for the ventilation control of an underground space, as an example of application of the developed solution to a very harsh environment but also highly demanding in terms of energy consumption. The underground spaces are characterized by a particular thermal behavior, because of the continuous and huge thermal exchange they have with the outside, via the openings and the ground surrounding the majority of the building. The main objective of the developed methodology is to reduce energy consumption of ventilation control while maintaining acceptable comfort levels: succeeding in achieving this twofold goal in a real station and the generalization of the approach are the most relevant contributions of the paper. The developed solution is based on a Model-based Predictive Control algorithm used together with a proper monitoring platform. The model predictive control is based on a Bayesian environmental prediction model, which works in cooperation with a weather forecast web service, schedule-based predictions about trains and external fans and an occupancy detection system to appraise the real amount of people. The prediction model develops scenarios useful to allow the controller acting in advance in order to adapt the system to the current and future conditions of use, taking profit of the knowledge of the real ventilation demand. Finally, the proposed control architecture is applied to the Passeig de Gràcia metro station in Barcelona as a case study, validating the usefulness of the proposed approach and obtaining more than 30% of energy savings in the ventilation system, while maintaining the pre-existing comfort levels. The saving percentage values estimated by simulation are confirmed by the direct measures continuously taken on site through energy-meters.

A fast MPC algorithm for reducing computation burden of MIMO

The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is proposed in this paper to solve this problem, in which real-time values are modulated to bit streams to simplify the multiplication. In addition, manipulated variables in the prediction horizon are deduced to the current control horizon approximately by a recursive relation to decrease the dimension of QR optimization. The simulation results demonstrate the feasibility of this fast algorithm for MIMO systems.

Adding Inverter Fault Detection to Model-Based Predictive Control for Flying-Capacitor Inverters

As inverters are often used in critical applications, reliability is an important issue. In particular, the power electronic switches and gate drivers, the most essential components of the inverter, are vulnerable parts in real live operation. Therefore, this paper focuses on open-switch fault detection for multilevel inverters. When a single-switch open-circuit fault occurs in one of the power electronic switches, the algorithm can detect the fault and the switch that is causing it. The detection is worked out for both a linear resistive inductive load and an induction motor. The proposed algorithm is an extension of an already available finite-set model-based predictive control algorithm. Therefore, no extra hardware or measurements are required. This paper also discusses a suggested method for reconfiguration after fault detection. Computer simulation and experimental verifications validate the proposed methods.

Novel Decoupling Model-Based Predictive Current Control Strategy for Flux-Switching Permanent-Magnet Synchronous Machines With Low Torque Ripple and Switching Loss

A flux-switching permanent-magnet synchronous machine (FSPMSM) has demonstrated merits of high torque density, strong mechanical robustness, good fault redundancy ability, etc., and begun to find great potential applications in electrical vehicle, airplane, ship, wind generation, and so on. However, by the inherited influence of double salient structure and high number of pole pairs, it suffers with comparatively large torque ripple and converter switching loss, which deteriorates the drive performance a little. Existing research mostly focuses on electromagnetic optimal design to reduce these defects, but little attention has been paid to advanced control strategies. In this paper, a novel model-based predictive current control algorithm with decoupling implementation is proposed to strengthen the drive performance of FSPMSM. One improved cost function has been used to find the best switching vector for the converter during each instant. Comprehensive theoretical analyses and simulations are carried out to validate the innovation feasibility.

Shortest-prediction-horizon model-based predictive control for individual offices

When employing model-based predictive control (MPC) for zone level heating and cooling systems, in many cases weather forecasts were imported to predict a thermal zone's temperature response over a time horizon. However, an office's thermal response is strongly influenced by an occupant's presence and behaviours. As illustrated through the analysis of an EnergyPlus simulation, propagation of the uncertainty introduced by an occupant's presence and behaviours into the temperature response of a thermal zone can result in suboptimal control decisions when the prediction time horizon extends beyond one hour. Results indicate that modest, yet robust to occupant behaviour, energy savings can be achieved by limiting the prediction time horizon to one hour in zone level MPC implementations. Choice of this prediction time horizon also eliminated the need for importing weather forecasts. In an effort to discuss the implementation challenges, this MPC algorithm has been implemented in a commercial controller to automate a ceiling radiant panel heater and a variable-air-volume (VAV) terminal unit serving to a west-facing office in Ottawa.