Improving the Robustness of a Parallel Robot Using Predictive Functional Control (PFC) Tools

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.

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.

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 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.

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.

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.

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.

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.

Predictive functional control (PFC) is a highly successful strategy within industry, but for cases with challenging dynamics the most effective tuning approaches are still an active research area. This paper shows how one can deploy some insights from the more traditional model predictive control literature in order to enable systematic tuning and in particular, to ensure that the key PFC tuning parameter, that is the desired closed-loop time constant, is effective. In addition to enabling easier and more effective tuning, the proposed approach has the advantage of being simple to code and thus retaining the simplicity of implementation and tuning that is a key selling point of PFC. This paper focuses on design for open-loop unstable and also processes with significant under-damping in their open-loop behaviour.

Because the predictive models do not match the practical process, the traditional predictive functional control could not acquire better performance. Through real-time database acquires the process parameters, the characteristic model of process can be obtained using the identification method. The characteristic model, as the predictive model of predictive functional control, downlink the control system. Consider the predictive model came from the on-line identification, it can match practical process. The experimental results prove that using the method we can acquire better performance. On-line identification uses NLJ method which can improve the identification speed.

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.

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

Automatic control is crucial to the efficiency and reliability of wind power generation systems. Predictive functional control for doubly fed induction machine is proposed. The doubly fed induction machine is used in generating mode for large-scale grid-connected variable speed wind turbines. In order to control the active power and reactive power exchanged between the stator and the grid, the rotor is fed by a bi-directional power converter. A dynamic model of doubly fed induction generator (DFIG) written in a d-q reference frame synchronized with the stator flux is established to synthesize controllers and to investigate simulations. The DFIG is controlled by a predictive functional controller whose performances are compared with a traditional proportional integral controller in terms of reference tracking, sensibility to perturbations and robustness. Simulation results verify the effectiveness of the proposed methods.