A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

In this paper, a unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes is proposed. A feasible reference trajectory taking constraints into consideration is first generated offline by the symplectic pseudospectral optimal control method. Then, a trajectory tracking model predictive controller also based on the symplectic pseudospectral method is developed to track the reference trajectory. At each sampling instant, the trajectory tracking controller works by solving an open‐loop optimal control problem where linearized system dynamics is used instead to improve the computational efficiency. Since the symplectic pseudospectral optimal control method is the core algorithm for both offline trajectory planning and online trajectory tracking, constraints on state variables and control inputs can be easily imposed and hence theoretically guaranteed in solutions. By selecting proper weighted matrices on tracking error and control, the developed controller could achieve control objectives in both accurate trolley positioning and fast suppressing of residual swing angles. Simulations for 3D overhead crane systems in the presence of perturbations in initial conditions, an abrupt variation of system parameter, and various external disturbances demonstrate that the developed controller is robust and of excellent control performance.     

Limitations on maximal tracking accuracy

This paper studies optimal tracking performance issues pertaining to finite-dimensional, linear, time-invariant feedback control systems. The problem under consideration amounts to determining the minimal tracking error between the output and reference signals of a feedback system, attainable by all possible stabilizing compensators. An integral square error criterion is used as a measure for the tracking error, and explicit expressions are derived for this minimal tracking error with respect to step reference signals. It is shown that plant nonminimum phase zeros have a negative effect on a feedback system's ability to reduce the tracking error, and that in a multivariable system this effect results in a way depending on not only the zero locations, but also the zero directions. It is also shown that if unity feedback structure is used for tracking purposes, plant nonminimum phase zeros and unstable poles can together play a particularly detrimental role in the achievable tracking performance, especially when the zeros and poles are nearby and their directions are closely aligned. On the other hand, if a two parameter controller structure is used, the achievable tracking performance depends only on the plant nonminimum phase zeros     

Optimum H designs under sampled state measurements

We present the complete solution to the H^∞-optimal control problem when only sampled values of the state are available. For linear time-varying systems the optimum controller is characterized in terms of the solution of a particular generalized Riccati-differential equation, with the optimum performance determined by the conjugate point conditions associated with a family of generalized Riccati differential equations. For the infinite-horizon time-invariant problem, however, the optimum controller is characterized in terms of the solution of a particular generalized algebraic Riccati equation, and the performance is determined in terms of the conjugate-point conditions of a single generalized Riccati equation, defined on the longest sampling interval. If the distribution of the sampling times is also taken as part of the general design, uniform sampling turns out to be optimal for the infinite horizon case, while for the finite horizon problem a nonuniform sampling generally leads to a better performance.     

A rigorous solution of the infinite time interval LQ problem with constant state tracking

A linear quadratic constant state tracking problem is considered over an infinite time horizon. It is shown that the solution cannot be obtained as a limit from a finite time horizon problem, as in general the limiting problem is ill-posed. To obtain a rigorous solution, the problem is split in two natural well-posed subproblems. One optimal control problem addresses the transient and the other optimal control problem concerns the steady state behavior. It is shown that the transient problem and the steady state problem are solved by the same control law.     

Robust tracking problem for continuous time stochastic uncertain systems

We propose a finite‐horizon robust minimax tracking controller design method for time‐varying continuous time stochastic uncertain systems. The uncertainty in the system is characterized by a set of probability measures under which stochastic noises, driving the system, are defined. A minimax optimal tracking controller is derived from the solution of a risk‐sensitive linear quadratic Gaussian control problem. Also a numerical example is presented to illustrate the characteristics of proposed tracking controller. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptive control with optimal robust tracking

The problem of optimal robust tracking in two-parameter adaptive control systems under non-linear time-varying unmodelled dynamics is examined. A new robust stability criterion is derived for analysing the robustness of adaptive control systems with non-linear time-varying model errors. Based on the concept of excess robustness and the theory of the minimum H^∞norm, a simple and feasible design algorithm is presented to synthesize a two-parameter adaptive controller which ensures that adaptive control systems can achieve the object of optimal robust tracking in the presence of non-linear time-varying unmodelled dynamics. Simulation results that demonstrate features of the two-parameter adaptive controller with optimal robust tracking in the light of the design algorithm are included.     

基于滚动时域估计的带约束运动目标跟踪*

针对实际的运动目标跟踪问题中存在的各种物理约束,采用基于在线滚动优化原理的滚动时域估计方法,将跟踪滤波问题转换为带约束的有限时域优化问题,并通过引入到达代价函数,有效减少了优化问题求解所需的计算量。最后,对实际的目标跟踪问题进行了滚动时域估计仿真研究。Monte Carlo仿真结果表明,滚动时域估计能有效提高跟踪精度,并且能在采样周期之内完成求解,满足在线估计的需要。     

Robust steady-state tracking

This paper addresses the robust performance problem when the performance measure is the “steady-state” value of an error signal. Necessary and sufficient conditions are derived for robust steady-state tracking of fixed inputs in the presence of structured time-varying uncertainty are derived. These conditions are easily computable and fit well with existing conditions on stability robustness and performance robustness when the performance measure is the level of disturbance rejection. Using these conditions, it is shown that time-varying perturbations of a nominal linear time-invariant plant can result in large steady-state tracking errors to fixed inputs even if the nominal plant and controller give zero steady-state tracking errors. The derived expressions for the worst-case steady-state tracking error give insight into how the time variation in the plant affects tracking errors and suggest that certain transfer function norms should be minimized to reduce the effect of these perturbations on the steady-state value of error signals     

Minimization of the worst case peak-to-peak gain via dynamicprogramming: state feedback case

Considers the problem of designing a controller that minimizes the worst case peak-to-peak gain of a closed-loop system. In particular, we concentrate on the case where the controller has access to the state of a linear plant and it possibly knows the maximal disturbance input amplitude. We apply the principle of optimality and derive a dynamic programming formulation of the optimization problem. Under mild assumptions, we show that, at each step of the dynamic program, the cost to go has the form of a gauge function and can be recursively determined through simple transformations. We study both the finite horizon and the infinite horizon case under different information structures. The proposed approach allows us to encompass and improve earlier results based on viability theory. In particular, we present a computational scheme alternative to the standard bisection algorithm, or gamma iteration, that allows us to compute the exact value of the worst case peak-to-peak gain for any finite horizon. We show that the sequence of finite horizon optimal costs converges, as the length of the horizon goes to infinity, to the infinite horizon optimal cost. The sequence of such optimal costs converges from below to the optimal performance for the infinite horizon problem. We also show the existence of an optimal state feedback strategy that is globally exponentially stabilizing and derive suboptimal globally exponentially stabilizing strategies from the solutions of finite horizon problems     

An optimal stochastic multivariable PID controller: a direct output tracking approach

This paper deals with the design of an optimal stochastic controller possessing tracking capability of any reference output trajectory in the presence of measurement noise. We consider multi-input multi-output linear time-invariant systems and a proportional-integral-derivative (PID) controller. The system under consideration needs not be stable. A recursive algorithm providing optimal time-varying PID gains is proposed for the case where the number of inputs is larger than or equal to the number of outputs. The development of the proposed algorithm aims for per-time-sample minimisation of the mean-square output error in the presence of erroneous initial conditions, measurement noise, and process noise. Necessary and sufficient conditions are provided for the convergence of the output error covariance. In addition, convergence results are presented for discretised continuous-time plants. Simulation results are included to illustrate the performance capabilities of the proposed algorithm. Performance comparison with an optimal stochastic iterative learning control scheme, an optimal PID controller, an adaptive PID controller, and a recent optimal stochastic PID controller are also included.     

H∞ tracking of linear continuous‐time systems with stochastic uncertainties and preview

The problem of finite‐horizon H_∞ tracking for linear continuous time‐invariant systems with stochastic parameter uncertainties is investigated for both, the state‐feedback and the output‐feedback control problems. We consider three tracking patterns depending on the nature of the reference signal i.e. whether it is perfectly known in advance, measured on line or previewed in a fixed time‐interval ahead. The stochastic uncertainties appear in both the dynamic and measurement matrices of the system. In the state‐feedback case, for each of the above three cases a game theory approach is applied where, given a specific reference signal, the controller plays against nature which chooses the initial condition and the energy‐bounded disturbance. The problems are solved using the expected value of the standard performance index over the stochastic parameters, where, in the state‐feedback case, necessary and sufficient conditions are found for the existence of a saddle‐point equilibrium. The corresponding infinite‐horizon time‐invariant tracking problem is also solved for the latter case, where a dissipativity approach is considered. The output‐feedback control problem is solved as a max–min problem for the three tracking patterns, where necessary and sufficient condition are obtained for the solution. The theory developed is demonstrated by a simple example where we compare our solution with an alternative solution which models the tracking signal as a disturbance. Copyright © 2004 John Wiley & Sons, Ltd.     

Finite horizon quadratic optimal control and a separation principle for Markovian jump linear systems

In this note, we consider the finite-horizon quadratic optimal control problem of discrete-time Markovian jump linear systems driven by a wide sense white noise sequence. We assume that the output variable and the jump parameters are available to the controller. It is desired to design a dynamic Markovian jump controller such that the closed-loop system minimizes the quadratic functional cost of the system over a finite horizon period of time. As in the case with no jumps, we show that an optimal controller can be obtained from two coupled Riccati difference equations, one associated to the optimal control problem when the state variable is available, and the other one associated to the optimal filtering problem. This is a principle of separation for the finite horizon quadratic optimal control problem for discrete-time Markovian jump linear systems. When there is only one mode of operation our results coincide with the traditional separation principle for the linear quadratic Gaussian control of discrete-time linear systems.     

A probabilistic approach for designing nonlinear optimal robust tracking controllers for unmanned aerial vehicles

In this study, we propose a probabilistic approach for designing nonlinear optimal robust tracking controllers for unmanned aerial vehicles. The controller design is formulated in terms of a multi-objective optimization problem that is solved by using a bio-inspired optimization algorithm, offering high likelihood of finding an optimal or near-optimal global solution. The process of tuning the controller minimizes differences between system outputs and optimal specifications given in terms of rising time, overshoot and steady-state error, and the controller succeed in fitting the performance requirements even considering parametric uncertainties and the nonlinearities of the aircraft. The stability of the controller is proved for the nominal case and its robustness is carefully verified by means of Monte Carlo simulations.     

Computation of infimal H∞ norm over a finite horizon

In this paper we develop necessary conditions for a minimax problem involving control and exogenous inputs. The problem can be regarded as a finite horizon version of the H_∞ optimal control problem. We consider problems involving generalized cost functional and non-zero initial conditions. A criterion for the evaluation of the performance index is given in these cases. Our computational experience shows that the finite horizon performance is useful in computing the infimal H_∞ norm in the infinite horizon case, as the final time becomes large. Also, expressions are derived for the variation in performance in terms of system parameter variations. These linear expressions are useful in the evaluation of the robustness of the proposed control strategy.     

Optimal tracking performance of MIMO control systems with communication constraints and a code scheme

This paper investigates the issue of the optimal tracking performance for multiple-input multiple-output linear time-invariant continuous-time systems with power constrained. An H₂ criterion of the error signal and the signal of the input channel are used as a measure for the tracking performance. A code scheme is introduced as a means of integrating controller and channel design to obtain the optimal tracking performance. It is shown that the optimal tracking performance index consists of two parts, one depends on the non-minimum phase zeros and zero direction of the given plant, as well as the reference input signal, while the other depends on the unstable poles and pole direction of the given plant, as well as on the bandwidth and additive white noise of a communication channel. It is also shown that when the communication does not exist, the optimal tracking performance reduces to the existing normal tracking performance of the control system. The results show how the optimal tracking performance is limited by the bandwidth and additive white noise of the communication channel. A typical example is given to illustrate the theoretical results.     

Distributed discrete-time coordinated tracking with a time-varying reference state and limited communication

This paper studies a distributed discrete-time coordinated tracking problem where a team of vehicles communicating with their local neighbors at discrete-time instants tracks a time-varying reference state available to only a subset of the team members. We propose a PD-like discrete-time consensus algorithm to address the problem under a fixed communication graph. We then study the condition on the communication graph, the sampling period, and the control gain to ensure stability and give the quantitative bound of the tracking errors. It is shown that the ultimate bound of the tracking errors is proportional to the sampling period. The benefit of the proposed PD-like discrete-time consensus algorithm is also demonstrated through comparison with an existing P-like discrete-time consensus algorithm. Simulation results are presented as a proof of concept.     

PID控制器参数调整的滚动优化算法

A novel supervised receding horizon optimal scheme is presented for discrete time systems in the process control. In the employing level, PID controller is used, while the receding horizon approach is applied to the optimized level. The considered problem is to optimize the employing level PID controller parameters through minimizing a generalized predictive control criterion. Compared with a fixed parameters PID controller, the proposed algorithm provides well performance over a range of operating condition.     

Receding Horizon Optimization Approach to PID Controller Parameters Auto-tuning

A novel supervised receding horizon optimal scheme is presented for discrete time systems in the process control.In the employing level,PID controller is used,while the receding horizon approach is applied to the optimized level.The considered problem is to optimize the employing level PID controller parameters through minimizing a generalized predictive control criterion.Compared with a fixed parameters PID controller,the proposed algorithm provides well performance over a range of operating condition.     

未知初始跟踪条件的非线性系统预设性能有限时间有界$H_infty$控制

研究一类具有外部扰动的非线性系统在初始跟踪条件未知情况下的预设性能有限时间有界$H_\infty$控制问题.针对预设性能控制设计,提出一个新的误差转换思想,并据此设计新的预设性能函数,解决预设性能控制依赖于系统被约束量初始条件的问题.基于所提出预设性能函数、有限时间控制理论以及有界$H_\infty$的设计方法,获得系统无需初始跟踪条件的预设性能有限时间有界$H_\infty$控制器,同时解决非线性系统在有界稳定情况下难以设计$H_\infty$控制器的问题,保证跟踪误差以预先设定的动态性能在有限时间内收敛至平衡点附近的小邻域内,并对外部干扰有较强的鲁棒性能.     

Error‐constrained finite‐horizon tracking control with incomplete measurements and bounded noises

This paper is concerned with the finite‐horizon tracking control problem for discrete nonlinear time‐varying systems with state delays, bounded noises and incomplete measurement output. The exogenous bounded noises are unknown and confined to specified ellipsoidal sets. A deterministic measurement output model is proposed to account for the incomplete data transmission phenomenon caused by possible sensor aging or failures. The aim of the addressed tracking control problem is to develop an observer‐based control over a finite‐horizon such that, for the admissible time delays, nonlinearities and bounded noises, both the quadratic tracking error and the estimation error are not more than certain upper bounds that are minimized at every time step. A recursive linear matrix inequality approach is used to solve the problem addressed. The observer and controller parameters are characterized in terms of the solution to a convex optimization problem that can be easily solved by using the semi‐definite programme method. A simulation example is exploited to illustrate the effectiveness of the proposed design procedures. Copyright © 2011 John Wiley & Sons, Ltd.