System identification and predictive functional control for electro-hydraulic actuator system

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.

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.

According to the analysis of papermaking process, combined with the principle of cross-directional basis weight(BW) detecting and controlling, predictive function control based on CARMA(Controlled Auto Return Integral Moving Average Model) model is proposed to solve the problem of time delay, nonlinearity and uncertainty of the multi-dimension system. Simulations show that multivariable predictive functional control algorithm for fast tracking velocity, good dynamic performance, and it can effectively reduce the mutual coupling effects between variables. The method has the feasibility of applying to practical papermaking process.

This paper presents a model and controller design for a pneumatic actuator system. Three control strategies of position, force and compliance control are proposed. From the compliance control, stiffness characteristic of the pneumatic actuator will be presented in real-time system. Model of the pneumatic actuator system (transfer function) is obtained from System Identification (SI) method. Next, Predictive Functional Control (PFC) with observer design is selected as the control strategy for the pneumatic system. Performance assessment of the controller is performed in MATLAB and validated through real-time experiment. Result shows that the PFC controller is adapted to the system and able to control successfully in both simulation and experiment.

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 central challenge in the control of complex industrial processes like heat exchangers is the persistent disconnection between global offline optimization and real-time online adaptation, leading to suboptimal performance under dynamic conditions. To address this, this paper proposes a coordinated hierarchical control strategy, CB-BPP, a three-layer framework integrating Chaotic Particle Swarm Optimization (CFPSO), a Backpropagation Neural Network (BPNN) identifier, and a Predictive Functional Control (PFC)-based PID controller. At the top layer, CFPSO performs offline global optimization of the BPNN's initial weights and establishes safe bounds for controller parameters. The middle layer employs the BPNN for online system identification and real-time adaptation of PID parameters. The bottom layer executes high-precision control. This tiered architecture decouples offline optimization from online adaptation, enabling synergistic control. Comprehensive simulations demonstrate the superiority of CB-BPP, reducing the Integral of Absolute Error (IAE) by up to 45% and improving recovery speed by 35% compared with advanced baseline methods. Furthermore, extensive robustness analysis against sensor noise and parameter drift, alongside a successful Hardware-in-the-Loop (HIL) implementation, validates its practical applicability. The results confirm the proposed method's high precision, strong robustness, and real-time feasibility, providing an advanced control solution for complex industrial processes.

Active disturbance rejected predictive functional control for space vehicles with RCS

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.

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.

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.

Improved Adaptive PSO-based Gain Tuning for PID Controllers in Utility Boilers

An advanced control concept, Predictive Functional Control (PFC), is applied for temperature control of a bench-scaled batch reactor equipped with monofluid heating/cooling system. First principles process models are developed. Based on achieved models, significant process variables, which are difficult or impossible to measure online, are estimated from easily measured variables, and cascade PFC control strategy has been projected and implemented in Matlab R14. The dynamics of individual subunits is explicitly taken into consideration by internal model in the control algorithms, and model uncertainty, various process disturbances are compensated by modification of internal model. The experimental results present an excellent capability of tracking the set point, and the success of PFC technique as a process control paradigm is illustratively demonstrated.

Heuristically, it was claimed in the literature that increasing the time-constant 'Tref' of the reference trajectory in predictive functional control (PFC) improves the closed-loop stability because the controller will be able to tolerate model mismatch which is difficult for a zero time-constant reference trajectory (a pure set-point) to tolerate. On the other hand, PFC uses 'Tref' as the main tuning parameter. Smaller time constants demand more aggressive control, while larger time constants result in less aggressive action. One may start with 'Tref' equal to the open-loop time constant of the control variable, then refine the tuning based on performance/robustness trade-offs. In this paper, the author investigates these issues analytically for an H4 robot. The analysis done shows that the system obtained is of type MGS defined in Proceedings of ACC 2006 for which it is possible by using PFC tools to find analytically a stable equilibrium point for the system and track at the same time a reference trajectory with a gain margin that increases as 'Tref' increases, improving that way the relative stability of the closed-loop system. The author notes that, the study done in this paper for the H4 robot could be applied to any robot whose linearised model is of MGS type. The author notes as well that the plots illustrated in this paper were obtained by simulation using Matlab/Simulink