Discrete-time reference governors and the nonlinear control of systems with state and control constraints

Discrete-time, linear control systems with specified pointwise-in-time constraints, such as those imposed by actuator saturation, are considered. The constraints are enforced by the addition of a nonlinear ‘reference governor’ that attenuates, when necessary, the input commands. Because the constraints are satisfied, the control system remains linear and undesirable response effects such as instability due to saturation are avoided. The nonlinear action of the reference governor is defined in terms of a finitely determined maximal output admissible set and can be implemented on-line for systems of moderately high order. The main result is global in nature: if the input command converges to a statically admissible input and the initial state of the system belongs to the maximal output admissible set, the eventual action of the reference governor is a unit delay.     

Explicit reference governor for linear systems

The explicit reference governor is a constrained control scheme that was originally introduced for generic nonlinear systems. This paper presents two explicit reference governor strategies that are specifically tailored for the constrained control of linear time-invariant systems subject to linear constraints. Both strategies are based on the idea of maintaining the system states within an invariant set which is entirely contained in the constraints. This invariant set can be constructed by exploiting either the Lyapunov inequality or modal decomposition. To improve the performance, we show that the two strategies can be combined by choosing at each time instant the least restrictive set. Numerical simulations illustrate that the proposed scheme achieves performances that are comparable to optimisation-based reference governors.     

A new model reference control architecture: Stability,performance, and robustness

In this paper, we develop a new model reference control architecture to effectively suppress system uncertainties and achieve a guaranteed transient and steady‐state system performance. Unlike traditional robust control frameworks, only a parameterization of the system uncertainty given by unknown weights with known conservative bounds is needed to stabilize uncertain dynamical systems with predictable system performance. In addition, the proposed architecture's performance is not dependent on the level of conservatism of the bounds of system uncertainty. Following the same train of thought as adaptive controllers that modify a given reference system to improve system performance, the proposed method is inspired by a recently developed command governor theory that minimizes the effect of system uncertainty by augmenting the input signal of the uncertain dynamical and reference systems. Specifically, a dynamical system, called a command governor, is designed such that its output is used to modify the input of both the controlled uncertain dynamical and reference systems. It is theoretically shown that if the command governor design parameter is judiciously selected, then the controlled system approximates the given original, unmodified reference system. The proposed approach is advantageous over model reference adaptive control approaches because linearity of the uncertain dynamical system is preserved through linear control laws, and hence, the closed‐loop performance is predictable for different command spectrums. Additionally, it is shown that the architecture can be modified for robustness improvements with respect to high frequency content due to, for example, measurement noise. Modifications can also be made in order to accommodate actuator dynamics and retain closed‐loop stability and predictable performance. The main contribution of this paper is the rigorous analysis of the stability and performance of a system utilizing the command governor framework. A numerical example is provided to illustrate the effectiveness of the proposed architecture. Copyright © 2015 John Wiley & Sons, Ltd.     

An MPC-based reference governor approach for offset-free control of constrained linear systems

This paper presents a model predictive control (MPC) based reference governor approach for control of constrained linear systems. A nominal closed-loop system is first designed to guarantee that, in the unconstrained case, asymptotic zero-error regulation for (piecewise) constant reference signals is achieved. Then, a couple of exogenous signals are added to the reference signal and to the control variable and their value is determined by formulating a MPC problem in order to guarantee that (i) when the state and control constraints are not active, the nominal closed-loop system is recovered, (ii) in transient conditions the constraints are always satisfied and the difference of the performances between the real and the nominal closed-loop systems is minimised, and (iii) when the reference signal is infeasible, the output is brought to the nearest feasible value. A simulation example is reported to witness the potentialities of the approach.     

Resource-aware MPC for constrained linear systems: Two rollout approaches

In systems with resource constraints, such as actuation limitations in sparse control applications or limited bandwidth in networked control systems, it is desirable to use control signals that are either sparse or sporadically changing in time. Motivated by these applications, in this paper we propose two resource-aware MPC schemes for discrete-time linear systems subject to state and input constraints. The two MPC schemes exploit ideas from rollout strategies to determine simultaneously the new (continuous) control inputs and the (discrete) time instants at which the control actions are updated. The first scheme provides performance guarantees by design, in the sense that it allows the user to select a desired suboptimal level of performance, where the degree of suboptimality provides a trade-off between the guaranteed closed-loop control performance on the one hand and the utilization of (communication/actuation) resources on the other hand. The second scheme provides a guaranteed (average) resource utilization, while cleverly allocating these resources in order to maximize the control performance. By means of numerical examples, we demonstrate the effectiveness of the proposed strategies.     

The input amplitude saturation problem in QFT: A survey

In this work the input amplitude saturation problem is analysed in the Quantitative Feedback Theory (QFT) framework. This paper reviews previous works in the literature dealing with the input amplitude saturation problem in the presence of an uncertain plant in the frequency domain using QFT. The objective of this paper is to compare the different available approaches and summarize the design process for each case so that this paper can be used as a tutorial; there are six main approaches to this problem. Two of these approaches use the classical two degrees of freedom control scheme for QFT; in both of these, the design constraints of a linear QFT compensator are added in the loop shaping stage: they are added in the first approach to avoid excitation of the actuator saturation and in the second one to guarantee global stability. The other three techniques are considered as anti-windup (AW) approaches. Starting from a base design in QFT with two degrees of freedom, the first AW approach introduces a third degree of freedom that guarantees the stability of the system, allowing for base designs for high performance. The other two AW approaches also introduce a third degree of freedom, but they take simple stability considerations into account and focus on the performance of the system. The last solution consists of using a reference governor technique, which guarantees the computation of a reference signal for an inner control loop that is shaped using QFT in such a way that robust stability will be guaranteed. The reference governor technique is a time domain approach that implies the resolution of an optimization problem. The rest of the approaches are frequency domain techniques based on a loop shaping method in the traditional QFT sense.     

Reference governor for constrained nonlinear systems

This paper addresses the problem of satisfying pointwise-in-time input and/or state hard constraints in nonlinear control systems. The approach is based on conceptual tools of predictive control and consists of adding to a primal compensated nonlinear system a reference governor. This is a discrete-time device which online handles the reference to be tracked, taking into account the current value of the state in order to satisfy the prescribed constraints. The resulting hybrid system is proved to fulfil the constraints as well as stability and tracking requirements     

Fast reference governors for systems with state and control constraints and disturbance inputs

Reference governors are applied to closed-loop tracking systems that are linear and discrete time and have constraints on state and control variables. Earlier results are extended in significant ways. Disturbance inputs, whose values belong to a specified set, are allowed and a general class of reference governors is introduced. Each governor in the class guarantees constraint satisfaction for all reference and disturbance inputs. Moreover, if the reference input is ultimately confined to a neighbourhood of a constraint-admissible constant input, the eventual action of the reference governor reduces to a unit delay. By appropriately selecting reference governors from the allowed class it is possible to simplify significantly their implementation. The increase in on-line speed of operation overcomes prior limits on the practical application of reference governors. Algorithmic procedures are described which facilitate design of the reference governors. Several examples are presented. They illustrate the design process and the excellence of response to large inputs. Copyright © 1999 John Wiley & Sons, Ltd.     

Command governors for constrained nonlinear systems

A method is described for set-point tracking in nonlinear systems when pointwise-in-time input and/or state inequality constraints are to be enforced. It consists of adding to a primal compensated system a nonlinear device called command governor (CG) whose action is based on the current state, set-point, and prescribed constraints. The CG selects at any time the system input via a receding-horizon strategy from a virtual sequence amongst all possible command sequences by solving a constrained quadratic optimization problem. Provided that the initial state is admissible, the overall system is proved to fulfil the constraints and have desirable performance stability properties     

Reference governor for constrained systems with time-varying references

This paper proposes constructing a reference governor for constrained linear systems with time-varying references. The main feature of the constructed reference governor is to simultaneously consider fulfillment of state and control constraints, as well as tracking performance by appropriately managing the reference to be inputted. To achieve constraint fulfillment and to evaluate tracking performance, the reference management is reduced into a convex quadratic programming problem using the concept of a maximal output admissible set. The reference governor is finally obtained in the form of a piecewise affine function of state and reference variables by means of a multi-parametric programming technique. In addition, the effectiveness of the reference governor is demonstrated by numerical and experimental examples of a practical DC position servomechanism with the control constraint.     

线性参变过驱动系统鲁棒控制分配策略

针对一类具有不确定时变参量的线性参变(linear parameter-varying,LPV)过驱动系统的控制分配问题,考虑系统的不确定参量扰动和执行器物理约束,利用伪控指令分配误差和控制量误差的1--范数,建立了含有时变不确定因子的控制分配优化模型.根据鲁棒优化思想,采用矢量变换技术处理时变不确定因子,得到了一种基于有约束锥二次凸优化模型的鲁棒控制分配算法,实现对LPV过驱动系统伪控指令的在线优化分配.最后,对某4轮电动汽车时变二自由度转向过驱动控制系统的对比仿真实验表明,相比常规4WS和伪逆控制分配方法,本文的鲁棒控制分配算法有效地降低了系统参变量不确定扰动的影响,得到更合理的控制分配解,有效改善了车辆的操纵稳定性.     

Command governor for constrained switched systems with scheduled model transition dwell times

In this paper, a switched control architecture for constrained control systems is presented. The strategy is based on command governor ideas that are here specialized to ‘optimally’ schedule switching events on the plant dynamics for improving control performance at the expense of low computational burdens. The significance of the method mainly lies in its capability to avoid constraints violation and loss of stability regardless of any configuration change occurrence in the plant/constraint structure. To this end, the concept of model transition dwell time is used within the proposed control framework to formally define the minimum time necessary to enable a switching event under guaranteed conditions on the overall stability and constraint fulfilment. Simulation results on a simple linear system and on a Cessna 182 aircraft model show the effectiveness of the proposed strategy. Copyright © 2017 John Wiley & Sons, Ltd.     

Reference governor for constrained piecewise affine systems

We present a methodology for designing reference tracking controllers for constrained, discrete-time piecewise affine systems. The approach follows the idea of reference governor techniques where the desired set-point is filtered by a system called the “reference governor”. Based on the system current state, set-point, and prescribed constraints, the reference governor computes a new set-point for a low-level controller so that the state and input constraints are satisfied and convergence to the original set-point is guaranteed.In this note we show how to design a reference governor for constrained piecewise affine systems by using polyhedral invariant sets, reachable sets, multiparametric programming and dynamic programming techniques.     

Solution of non-linear optimization problems in power systems†

The application of differential dynamic programming or hybrid quasilinearization technique to the solution of non-linear optimization problems in power systems has encountered the problem of computational instability, particularly in higher order systems. This paper describes the application of a continuation procedure to alleviate this difficulty. Sixth order non-linear systems have been optimized with and without constraints on control variables. Both open-loop and, for the first time, closed-loop systems including both exciter and governor dynamics, are analysed. The studies presented show that this technique is quite effective in obtaining accurate solutions for non-linear boundary-value-problems in power systems.     

Time/fuel optimal control of constrained linear discrete systems

A unified approach to solving three common optimal control problems is presented, for linear systems under general constraints. The problems are: (1) the time optimal control problem; (2) the fuel optimal control problem in fixed time; (3) the time optimal control problem with a fuel constraint. A special purpose linear programming algorithm is used. State variable constraints are efficiently handled by a cutting plane algorithm. An example of a sixth order system with two inputs and two state variable constraints illustrates the method as implemented on a personal computer.     

Reduced order extended command governor

Extended command governors (ECGs) are add-on schemes that modify set-point commands as necessary to ensure that imposed state and control constraints are not violated by closed-loop systems designed for set-point tracking. In this paper, we propose a reduced order ECG for systems with dynamics decomposable into slow and fast state variables. We demonstrate that ECG implementation can be based on slow states only, thus reducing the computational complexity. This is achieved by introducing additional constraints, and by slightly tightening the original constraints. We demonstrate that the proposed ECG maintains the response properties of the conventional ECG, including the convergence to the nearest feasible command in finite time in the case of constant reference commands. The results are also shown to apply to conventional command governors. For the case when the reduced order state is not directly measured, a formulation of the result in the presence of a state observer is developed.     

Robust multi-parametric model predictive control for LPV systems with application to anaesthesia

We present a multi-parametric model predictive controller (mpMPC) for discrete-time linear parameter-varying (LPV) systems based on the solution of the mpMPC problem for discrete-time linear time-invariant (LTI) systems. The control method yields a controller that adapts to parameter changes of the LPV system. This is accomplished by an add-on unit to the implementation of the mpMPC for LTI systems. No modification of the optimal mpMPC solution for LTI systems is needed. The mpMPC for LPV systems is entirely based on simple computational steps performed on-line. This control design method could improve the performance and robustness of a mpMPC for LPV systems with slowly varying parameters. We apply this method to process systems which suffer from slow variation of system parameters due, for example, to aging or degradation. As an illustrative example the reference tracking control problem of the hypnotic depth during intravenous anaesthesia is presented: the time varying system matrix mimics an external disturbance on the hypnotic depth. In this example the presented mpMPC for LPV systems shows a reduction of approximately 60% of the reference tracking error compared to the mpMPC for LTI systems.     

Adaptive output rejection of unmatched input disturbances

To adaptively reject the effect of certain unmatched input disturbances on the output of a linear time-invariant system, a transfer function matching condition is needed. A lemma which presents a novel basic property of linear systems is derived to characterize system conditions for such transfer function matching. An adaptive disturbance rejection control scheme is developed for such systems with uncertain dynamics parameters and disturbance parameters. This adaptive control technique is applicable to control of systems with actuator failures whose failure values, failure time instants, and failure patterns are unknown. A solution is presented to this adaptive actuator failure compensation problem, which ensures closed-loop stability and asymptotic output tracking, in the presence of any up to m−1 uncertain failures of the total m actuators. Desired adaptive system performance is verified by simulation results.     

Nonlinear control of constrained linear systems via predictivereference management

A method based on conceptual tools of predictive control is described for solving set-point tracking problems wherein pointwise-in-time input and/or state inequality constraints are present. It consists of adding to a primal compensated system a nonlinear device, called command governor (CG), whose action is based on the current state, set-point, and prescribed constraints. The CG selects at any time a virtual sequence among a family of linearly parameterized command sequences, by solving a convex constrained quadratic optimization problem, and feeds the primal system according to a receding horizon control philosophy. The overall system is proved to fulfill the constraints, be asymptotically stable, and exhibit an offset-free tracking behavior, provided that an admissibility condition on the initial state is satisfied. Though the CG can be tailored for the application at hand by appropriately choosing the available design knobs, the required online computational load for the usual case of affine constraints is well tempered by the related relatively simple convex quadratic programming problem