Composite Predictive Functional Control Strategies, Application to Positioning Axes

This paper presents an efficient application of a model based predictive control in parallel mechanisms. A predictive functional control strategy based on a simplified dynamic model is implemented. Experimental results are shown for the H4 robot, a fully parallel structure providing 3 degrees of freedom (dof) in translation and 1 dof in rotation. Predictive functional control, computed torque control and PID control strategies are compared in complex machining tasks trajectories. The tracking performances are enlightened.

In this paper, an improved predictive functional control (PFC) scheme for permanent magnet synchronous motor (PMSM) control system is proposed, on account of the standard PFC method cannot provides a satisfying disturbance rejection performance in the case of strong disturbances. The PFC-based method is first introduced in the control design of speed loop, since the good tracking and robustness properties of the PFC heavily depend on the accuracy of the internal model of the plant. However, in orthodox design of prediction model based control method, disturbances are not considered in the prediction model as well as the control design. A minimumorder observer (MOO) is introduced to estimate the disturbances, which structure is simple and can be realized at a low computational load. This paper adopted the MOO to observe the load torque, and the observations are then fed back into PFC model to rebuild it when considering the influence of perturbation. Therefore, an improved PFC strategy with torque compensation, called the PFC+MOO method, is presented. The validity of the proposed method was tested via simulation and experiments. Excellent results were obtained with respect to the speed trajectory tracking, stability, and disturbance rejection.

When controlling difficult industrial processes characterised by open-loop instability and/or poor damping, predictive functional control (PFC) practitioners often face two critical design challenges. The first one arises due to the intrinsic simplicity of the PFC control algorithm that, instead of optimising the future control trajectory in real time like other predictive controllers, simply assumes constant control action, thereby producing unreliable model predictions in the long range and thus inconsistent closed-loop performance. The second issue is related to the controller tuning, which may become ambiguous and unsystematic due to the existence of an inconsistent relationship between the controller parameters and closed-loop behaviour. This paper presents a dual-loop control strategy that aims at mitigating both weaknesses simultaneously by combining the concepts of pre-stabilisation and relative tuning within the framework of predictive functional control. Two challenging industrial case studies have been analysed through computer simulations that successfully validate the efficacy of the proposal under various real world scenarios.

In recent years, Japanese society has been aging, engendering a labor shortage of young workers. For a robot that is often in contact with people and which must provide safety and flexibility in nursing and welfare, the development of a soft, lightweight, and compact actuator has been sought. Particularly robots that are intended for use in fields of medical care and welfare should be safe when functioning around humans because they often come into contact with people. Therefore, a tendon-driven balloon actuator (balloon actuator) has been developed for a robot hand to be used in such environments. For this study, we developed a stroke control system for the balloon actuator using a predictive functional control (PFC). The PFC is one of model predictive control (MPC) schemes, which predicts the future outputs of the actual plant over the prediction horizon and computes the control effort over the control horizon at every sampling instance. This paper reports the PFC control performance of the balloon actuator. We compared the control performance for the actuator with that of the PFC and a PID control system.

Predictive functional control (PFC) is a cheap and simplified model predictive controller, which competes with PID in price and performance. While the tuning process in PFC for simple dynamics is well established and straightforward, it becomes far more ambiguous and often less effective for processes exhibiting challenging behaviour, such as poor damping, instability and/or non-minimum phase characteristics. In this paper, we present a relative PFC algorithm that, when implemented with pre-stabilised prediction dynamics if needed, simplifies performance tuning to merely adjusting one parameter. Furthermore, it provides far superior closed-loop control in practical scenarios, where the conventional PFC and PID fail to perform, as demonstrated with three simulation case studies.

A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time‐varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow‐up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law.

A new strategy of adaptive predictive function control based on Laguerre model is developed. The parameters of Laguerre model are identified by the recursive least square method with forgetting factor. Based on the identified result, the adaptive predictive function controller (PFC) is designed. The result of simulation shows the performance of the derived control strategy.

In heating, ventilating and air-conditioning (HVAC) systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection.

This paper considers a class of predictive functional control problems for nonlinear systems based on multi-LPV models. First, according to the piece-wise embedding principle, multiple Linear Parameters-Varying (LPV) models have been applied to approximate describe the nonlinear system. For each LPV model, a predictive functional controller based on a linear combination of known basis functions is designed. Then a switching law between each controller is designed, to guarantee the stability of the whole system. Compared with traditional state feedback controller, the predictive functional controller has better control effect and robustness. Therefore, the proposed algorithm has less computation and less conservation. Finally, a simulation example is given to verify the effectiveness of the proposed algorithm.

A hybrid control method based on sliding mode observer and predictive functional control is proposed to improve the performance of the linear motor servo system, which is usually characterized by nonlinearity, uncertainty and disturbances. Firstly, the mathematical model of linear motor servo system is established. Then, the sliding mode observer is designed to compensate for the parameter changes and external disturbances, and the disturbance information is fed back to the predictive function controller, and a prediction model with disturbances is established, so as to optimize the control method and improve the control effect of servo system. The simulation results show that the controller based on the sliding mode observer and predictive functional controller can effectively suppress the disturbances and improve the rapid response and steady-state accuracy of the system. As the controller shows strong robustness and anti-disturbance, it can be used to effectively control the servo systems.

Predictive functional control (PFC) applied to an unstable system. An industrial application

Nowadays, electro-hydraulic actuator (EHA) is widely applied in industries. This study presents the modeling and development of a predictive functional control (PFC) algorithm for position control of EHA. System identification (SI) approach is used to obtain the linear transfer function of the system in discrete form. PFC is proposed based on its ability to predict the future outputs of the actual plant over the predictive horizon and computes the control effort over the control horizon at every sampling instance. Numerical simulation and real-time experiment are conducted to study the PFC performance with respect to an optimized PID controller tuned by using particle swarm optimization (PID-PSO) for several position tracking inputs. The result shows that the PFC algorithm has better performance in term of overshoot and integral absolute error (IAE) as compared to the optimized PID.

This paper proposes a novel modification to the predictive functional control (PFC) algorithm to facilitate significant improvements in the tuning efficacy. The core concept is the use of an alternative parameterisation of the degrees of freedom in the PFC law. Building on recent insights into the potential of Laguerre functions in traditional MPC (Rossiter, Wang, & Valencia-Palomo, 2010; Wang, 2009), this paper develops an appropriate framework for PFC and then demonstrates that these functions can be exploited to allow easier and more effective tuning in PFC as well as facilitating strong constraint handling properties. The proposed design approach and the associated tuning methodology are developed and their efficacy is demonstrated with a number of numerical examples.

The recent use of hybrid renewable energy systems (HRESs) is considered one of the most reliable ways to improve energy access to decentralized communities because of their techno-economic and environmental benefits. Many distant locales, such as camps in war-torn nations, lack basic necessities like power. This study proposes a remedy for power outages in these areas; by designing an HRES and a control system for monitoring, distributing, and managing the electrical power from sustainable energy sources to supply the load. Hence, providing affordable, reliable, and clean energy for all (Sustainable Development Goal 7). In this study, the feasibility and techno-economic performance of an HRES for a refugee camp was evaluated under load following (LF), cycle charging (CC), and predictive control strategy (PS). The optimization results revealed that the PS was the most suitable, as it had the lowest cost and was more eco-friendly and energy-efficient. The predictive control strategy had a 48-h foresight of the load demand and resource potential and hence could effectively manage the HRES. The total net present cost (NPC) for the electrification of this refugee camp was $3,809,822.54, and the cost of electricity generated for every kWh is $0.2018. Additionally, 991,240.32 kg of emissions can be avoided annually through the hybridization of the diesel generator under the PS.