Discretetime handsoff control by sparse optimization
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Abstract
Maximum handsoff control is a control mechanism that maximizes the length of the time duration on which the control is exactly zero. Such a control is important for energyaware control applications, since it can stop actuators for a long duration and hence the control system needs much less fuel or electric power. In this article, we formulate the maximum handsoff control for linear discretetime plants by sparse optimization based on the ℓ ^{1} norm. For this optimization problem, we derive an efficient algorithm based on the alternating direction method of multipliers (ADMM). We also give a model predictive control formulation, which leads to a robust control system based on a state feedback mechanism. Simulation results are included to illustrate the effectiveness of the proposed control method.
Keywords
Handsoff control Sparse optimization Discretetime control Optimal control ADMM Model predictive control Green control1 Introduction
Sparsity is one of the most important notions in recent signal/image processing [1], machine learning [2], communications engineering [3], and highdimensional statistics [4]. A wide range of applications is shown in works, such as [5].
Recently, sparsitypromoting techniques have been applied to control problems as stated below. Ohlsson et al. have proposed in [6] sumofnorms regularization for trajectory generation to obtain a compact representation of the control inputs. In [7], Bhattacharya and Başar have adapted compressive sensing techniques to state estimation under incomplete measurements. The sparsity notion is also applied to networked control for reduction of control data size using model predictive control (MPC) [8, 9, 10]. MPC is a very attractive research topic to which sparsity methods are applied; in [11, 12] Gallieri and Maciejowski have proposed ℓ _{asso}MPC to reduce actuator activity, and in [13] Aguilera et al. have discussed minimization of the number of active actuators subject to closedloop stability by using the ℓ ^{0} norm. Sparse MPC is further investigated based on selftriggered control in [14].
Motivated by these researches, the maximum handsoff control has been proposed in [15, 16] for continuoustime systems. This control maximizes the length of the time duration over which the control value is exactly zero. With such control, actuators can be stopped for a long duration, during which the control system requires much less fuel or electric power, emits less toxic gas such as CO2, and generates less noise. Therefore, the control is also called green control [17]. The optimization is described as a finitehorizon L ^{0}optimal control, which is discontinuous and highly nonconvex, and hence difficult to solve in general. In [15, 16], under a simple assumption of normality, the L ^{0}optimal control is proved to be equivalent to classical L ^{1}optimal (or fuel optimal) control, which can be described as a convex optimization. The proof of the equivalence theorem is mainly based on the “bangoffbang” property (i.e., the control takes values ±1 or 0 almost everywhere) of the L ^{1}optimal control. Moreover, based on the equivalence, the value function in the maximum handsoff control is shown to be continuous and convex in the reachable set [18], which can be used to prove the stability of an MPCbased closedloop system.
In this paper, we investigate the handsoff control in discrete time for energyaware green control. The main difference from the continuoustime handsoff control mentioned above is that the discretetime maximum handsoff control shows in many cases no “bangoffbang” property. Instead, we use the restricted isometry property (RIP), e.g., [3], for an equivalence theorem between ℓ ^{0} and ℓ ^{1}.
An associated ℓ ^{1}optimal control problem can be described via an ℓ ^{1} optimization problem with linear constraints. This can be equivalently written as a standard linear program, which can be “efficiently” solved by the interiorpoint method [19]. The efficiency of the interiorpoint method is true for small or middlescale problems with offline computation. However, for realtime control applications, problems arise. To improve computational efficiency in the current paper, we adapt the alternating direction method of multipliers (ADMM) to the control problem. ADMM was first introduced in [20] in 1976, and since then, the algorithm has been widely investigated in both theoretical and practical aspects; see the review [21] and the references therein. ADMM has indeed been proved to converge to the exact optimal value under mild conditions, but in some cases it shows quite slow convergence to the optimal value. On the other hand, ADMM often gives very fast convergence to an approximated value ([21], section 3.2). This property is desirable for realtime control application, since the approximation error can often be eliminated by relying upon robustness of the feedback control mechanism. In fact, ADMM has been applied to MPC with a quadratic cost function in [22, 23, 24]. In particular, an ADMM algorithm for ℓ ^{1}regularized MPC has been proposed in [25] without theoretical stability results.
1.1 Contributions
In this paper, we first analyze discretetime finitehorizon handsoff control, where we give a feasibility condition based on the system controllability, and also develop an equivalence theorem between ℓ ^{0} and ℓ ^{1}optimal controls based on the idea of RIP. These are different from the case of continuoustime handsoff control in [16], where the concept of normality for an optimal control problem was adopted. Unfortunately, normality cannot be used in the discretetime case. RIP is often used to prove equivalence theorems, e.g., [1] in signal processing, and we show in this paper that RIP is also useful for discretetime handsoff control.
To calculate discretetime handsoff control, we then propose to use ADMM, which is widely applied to signal/image processing [21], and we prove by simulation that ADMM is very effective in feedback control since it requires very few iterations. Finally, we prove a stability theorem for handsoff model predictive control, which has been never given in the literature except for the continuoustime case [18].
1.2 Outline
The paper is organized as follows: in Section 2, we formulate the discretetime maximum handsoff control, and prove the feasibility property and the ℓ ^{0} ℓ ^{1} equivalence based on the RIP. In Section 3, we briefly review ADMM, and give the ADMM algorithm for maximum handsoff control. The penalty parameter selection in the optimization is also discussed in this section. Section 4 proposes MPC with maximum handsoff control, and establishes a the stability result. We include simulation results in Section 5, which illustrate the advantages of the proposed method. Section 6 draws concluding remarks.
1.3 Notation
2 Discretetime handsoff control
where \(\boldsymbol {x}[\!k]\in {\mathbb {R}}^{n}\) is the state at time k, \(u[\!k]\in {\mathbb {R}}\) is the discretetime scalar control input, and \(A\in {\mathbb {R}}^{n\times n}\), \(\boldsymbol {b}\in {\mathbb {R}}^{n}\).
The control (sequence) {u[ 0],u[ 1],…,u[ N−1]} is chosen to drive the state x[ k] from a given initial state x[ 0]=ξ to the origin x[ N]=0 in N steps.
For the feasible control set \({\mathcal {U}}_{\boldsymbol {\xi }}\), we have the following lemma.
Lemma 1.
and N>n. Then \({\mathcal {U}}_{\boldsymbol {\xi }}\) is nonempty for any \(\boldsymbol {\xi }\in {\mathbb {R}}^{n}\).
Proof.
satisfies \(A^{N}\boldsymbol {\xi }+\Phi \tilde {\boldsymbol {u}}=\boldsymbol {0}\), and hence \(\tilde {\boldsymbol {u}}\in {\mathcal {U}}_{\boldsymbol {\xi }}\).
Lemma 2.
Assume that the pair (A,b) is reachable and N>n. Then, we have \({\mathcal {U}}_{\boldsymbol {\xi }} \cap \Sigma _{n} \neq \emptyset \).
Proof.
From the proof of Lemma 1, there exists a feasible control \(\tilde {\boldsymbol {u}}\in {\mathcal {U}}_{\boldsymbol {\xi }}\) that satisfies \(\\tilde {\boldsymbol {u}}\_{0} \leq n\); see (6). It follows that \(\tilde {\boldsymbol {u}}\in \Sigma _{n}\) and hence \(\tilde {\boldsymbol {u}}\in {\mathcal {U}}_{\boldsymbol {\xi }}\cap \Sigma _{n}\).
This lemma assures that the solution of the ℓ ^{0} optimization is at most nsparse. However, the optimization problem (7) is a combinatorial one, and requires heavy computational burden if n or N is large. This property is undesirable for realtime control systems, and we propose to relax the combinatorial optimization problem to obtain a convex one.
where \(\\boldsymbol {u}\_{1} \triangleq u[\!0]+u[\!1]+\dots +u[\!N1]\). The resulting optimization can be described as a linear program, and hence we can solve it efficiently by using numerical software such as CVX in MATLAB [26, 27]. Moreover, an accelerated algorithm is derived by the alternating direction method of multipliers (ADMM) [21]; see Section 3.
To justify the use of the ℓ ^{1} relaxation, we recall the restricted isometry property [1] defined as follows:
Definition 1.
Then, we have the following theorem.
Theorem 1.
Assume that the pair (A,b) is reachable and that N>n. Suppose that the ℓ ^{0} optimization (7) has a unique ssparse solution. If the matrix Φ given in (2) satisfies the RIP of order 2s with \(\delta _{2s}<\sqrt {2}1\), then the solution of the ℓ ^{1}optimal control problem (7) is equivalent to that of the ℓ ^{0}optimal control problem (8).
Proof.
Since u ^{∗} is ssparse, that is, u ^{∗}∈Σ _{ s }, we have σ _{ s }(u ^{∗})=0, and hence \(\hat {\boldsymbol {u}}=\boldsymbol {u}^{\ast }\).
3 Numerical optimization by ADMM
The optimization problem in (8) is convex and can be described as a standard linear program [19]. However, for realtime computation in control such as model predictive control discussed in section 4, a much more efficient algorithm is desired than the standard interior point method for the linear program. For this purpose, we propose to adopt ADMM [20, 21, 29], for the ℓ ^{1} optimization. Although ADMM generally only achieves very slow convergence to the exact optimal value, it is shown in ([21], Section 3.2) that ADMM often converges to modest accuracy within a few tens of iterations. This property is especially favorable in model predictive control, since the computational error generated by the ADMM algorithm can often be reduced by the feedback control mechanism; see the simulation results in Section 5.
3.1 Alternating direction method of multipliers (ADMM)
where ρ>0, \(\boldsymbol {y}[\!0]\in {\mathbb {R}}^{\mu }\), \(\boldsymbol {z}[\!0]\in {\mathbb {R}}^{\nu }\), and \(\boldsymbol {w}[\!0]\in {\mathbb {R}}^{\kappa }\) are given before the iterations.
Assuming that the unaugmented Lagrangian L _{0} (i.e., L _{ ρ } with ρ=0) has a saddle point, the ADMM algorithm is known to converge to a solution of the optimization problem (9) ([21], Section 3.2).
3.2 ADMM for ℓ^{1}optimal control
The operator S _{1/ρ } is also known as the proximity operator for the ℓ ^{1}norm term in the augmented Lagrangian L _{ ρ }. Note that if the pair (A,b) is reachable and N>n, then the matrix Φ is full row rank (see the proof of Lemma 1), and hence the matrix Φ Φ ^{⊤} is nonsingular. Note also that the matrix I−Φ ^{⊤}(Φ Φ ^{⊤})^{−1} Φ and the vector Φ ^{⊤}(Φ Φ ^{⊤})^{−1} A ^{ N } ξ in (13) can be computed before the iterations in (12), and hence the computation in (12) is very simple.
3.3 Selection of penalty parameter ρ
To use the ADMM algorithm in (12), we should appropriately determine the penalty parameter (or the step size) ρ. In general, if the penalty parameter is large, then the primal residual y[j]−z[j], or C y[j]+D z[j]−c[j] tends to be small, since it places a large penalty on violations of primal feasibility; see (10). On the other hand, a smaller ρ tends to give a sparser output from the definition of the soft thresholding operator S _{1/ρ }; see (14) or Fig. 1. For the selection of ρ, one should rely on trial and error by simulation. One may extend the idea of optimal parameter selection for quadratic problems [24, 30] to the ℓ ^{1} optimization (8), for which we do not have any optimal parameter selection method. Alternatively, one can adopt the varying penalty parameter ([21], Section 3.4), in which one may use possibly different penalty parameters ρ[j] for each iteration. See also [31, 32].
4 Model predictive control
Based on the finitehorizon ℓ ^{1}optimal control in (8), we here extend it to infinitehorizon control by adopting a model predictive control strategy. ^{1}
4.1 Control law
Since the control vector \(\hat {\boldsymbol {u}}[k]\) is designed to be sparse by the ℓ ^{1} optimization as discussed above, the first element, \(\hat {u}_{0}[\!k]\), will often be exactly 0, e.g., the vector shown in (6). A numerical simulation in Section 5 illustrates that the control will often be sparse, when using this model predictive control formulation.
4.2 Stability
We here discuss the stability of the closedloop system (17) with the model predictive control described above. In fact, we can show the stability of the closedloop control system by using a standard argument in the stability analysis of model predictive control with a terminal constraint (e.g., ([33], Chapter 6), ([34], Chapter 2), or ([35], Chapter 5)).
The following lemma shows the convexity, the continuity, and the positive definiteness of the value function V(ξ). These properties are useful to show the value function to be a Lyapunov function (see the proof of Theorem 2 below).
Lemma 3.
Assume that the pair (A,b) is reachable, A is nonsingular, and N>n. Then V(ξ) is a convex, continuous, and positive definite function on \({\mathbb {R}}^{n}\).
Proof.
Next, the continuity of V on \({\mathbb {R}}^{n}\) follows from the convexity and the fact that V(ξ)<∞ for any \(\boldsymbol {\xi }\in {\mathbb {R}}^{n}\), due to Lemma 1.
Finally, we prove the positive definiteness of V. It is easily seen that V(ξ)≥0 for any \(\boldsymbol {\xi }\in {\mathbb {R}}^{n}\), and V(0)=0. Assume V(ξ)=0. Then there exists \(\boldsymbol {u}^{\ast }\in {\mathcal {U}}_{\boldsymbol {\xi }}\) such that ∥u ^{∗}∥_{1}=0. This implies u ^{∗}=0 and hence \(\boldsymbol {0}\in {\mathcal {U}}_{\boldsymbol {\xi }}\). Since A is nonsingular, ξ should be 0.
By using the properties proved in Lemma 3, we can show the stability of the closedloop control system.
Theorem 2.
Suppose that the pair (A,b) is reachable, A is nonsingular, and N>n. Then the closedloop system with the model predictive control defined by (15) and (16) is stable in the sense of Lyapunov.
Proof.

V(0)=0.

V(ξ) is continuous in ξ.

V(ξ)>0 for any ξ≠0.
It follows that V is a Lyapunov function of the closedloop control system. Therefore, the stability is guaranteed by Lyapunov’s stability theorem.
We should note that if we use the first element of the sparse feasible control given in (6), then the MPC generates the allzero sequence, which obviously does not stabilize any unstable plants. This shows that not all feasible controls necessarily guarantee closedloop stability. It is also worth noting that continuity of the value function leads to favorable robustness properties of the closedloop system, see Section 5.
5 Simulation
For the discretetime plant model, we assume the initial state x[ 0]=[ 1,1,1]^{⊤} and the horizon length N=30. For the ADMM algorithm in (12), we set the penalty parameter ρ=2, which is chosen by trial and error. We also choose the number of iterations in ADMM as N _{iter}=2, so that the computation in (12) is much faster than the interiorpoint method (see below for details).
In this figure, the maximum handsoff control is sufficiently sparse (i.e., there are long time durations on which the control takes zero) while the L ^{2}optimal control is smoother but not sparse.
From the figure, the maximum handsoff control achieves significantly faster convergence to zero than the L ^{2}optimal control.
Finally, we compare the number of iterations between ADMM and the interiorpointbased CVX. The averaged number of the CVX iterations is 10.7, which is approximately five times larger than that of ADMM, N _{iter}=2. Note that the interiorpointbased algorithm needs to solve linear equations at each iteration, and hence computational times may be much longer than those for the ADMM, since the inverse matrix in (13) can be computed offline.
6 Conclusions
In this paper, we have introduced the discretetime maximum handsoff control that maximizes the length of time duration on which the control is zero. The design is described by an ℓ ^{0} optimization, which we have proved to be equivalent to convex ℓ ^{1} optimization using the restricted isometry property. The optimization can be efficiently solved by the alternating direction method of multipliers (ADMM). The extension to model predictive control has been examined and nominal stability has been proved. Simulation results have been shown to illustrate the effectiveness of the proposed method.
6.1 Future work
Here, we show future directions related to the maximum handsoff control. The maximum handsoff control has been proposed in this paper for linear timeinvariant systems. It is desired to extend it to timevarying and nonlinear networked control, such as Markovian jump systems as discussed in [36, 37, 38], to which “intelligent methods” have been applied in [39, 40]. We believe the sparsity method can be combined with fault detection and reliable control methods, as discussed in [41, 42]. Future work also includes an optimal selection method for the penalty parameter ρ in ADMM which takes into account control performance.
7 Endnote
^{1}It is desirable if one can use an infinitehorizon control like an H _{ ∞ } control as in e.g. [36]. However, for the maximum handsoff control discussed in this paper, there is no available methods to directly obtain infinitehorizon control, and model predictive control is a convenient way to extend a finitehorizon control to infinitehorizon.
Notes
Acknowledgements
The research of M. Nagahara was supported in part by JSPS KAKENHI Grant Numbers 16H01546, 15K14006, and 15H02668. The research of J. Østergaard was supported by VILLUM FONDEN Young Investigator Programme, Project No. 10095. The authors would like to thank the reviewers for pointing us to references [36, 37, 38, 39, 40, 41, 42].
Competing interests
The authors declare that they have no competing interests.
References
 1.YC Eldar, G Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University Press, Cambridge, 2012).CrossRefGoogle Scholar
 2.T Hastie, R Tibshirani, M Wainwright, Statistical Learning with Sparsity: The Lasso and Generalizations (CRC Press, Boca Raton, 2015).MATHGoogle Scholar
 3.K Hayashi, M Nagahara, T Tanaka, A user’s guide to compressed sensing for communications systems. IEICE Trans. Commun.E96B(3), 685–712 (2013).CrossRefGoogle Scholar
 4.C Giraud, Introduction to HighDimensional Statistics (CRC Press, Boca Raton, 2015).MATHGoogle Scholar
 5.I Rish, GA Cecchi, A Lozano, A NiculescuMizil, Practical Applications of Sparse Modeling (MIT Press, Massachusetts, 2014).Google Scholar
 6.H Ohlsson, F Gustafsson, L Ljung, S Boyd, in 49th IEEE Conference on Decision and Control (CDC). Trajectory generation using sumofnorms regularization, (2010), pp. 540–545.Google Scholar
 7.S Bhattacharya, T Başar, in Proc. Amer. Contr. Conf. Sparsity based feedback design: a new paradigm in opportunistic sensing, (2011), pp. 3704–3709.Google Scholar
 8.M Nagahara, DE Quevedo, in IFAC 18th World Congress. Sparse representations for packetized predictive networked control, (2011), pp. 84–89.Google Scholar
 9.M Nagahara, DE Quevedo, J Østergaard, Sparse packetized predictive control for networked control over erasure channels. IEEE Trans. Autom. Control. 59(7), 1899–1905 (2014).MathSciNetCrossRefGoogle Scholar
 10.H Kong, GC Goodwin, MM Seron, A costeffective sparse communication strategy for networked linear control systems: an SVDbased approach. Int. J. Robust Nonlinear Control.25(14), 2223–2240 (2015).MathSciNetCrossRefMATHGoogle Scholar
 11.M Gallieri, JM Maciejowski, in Proc. Amer. Contr. Conf. ℓ _{asso}. MPC: Smart regulation of overactuated systems, (2012), pp. 1217–1222.Google Scholar
 12.M Gallieri, JM Maciejowski, in Proc. 2015 European Control Conference (ECC). Model predictive control with prioritised actuators (Linz, 2015), pp. 533–538.Google Scholar
 13.RP Aguilera, RA Delgado, D Dolz, JC Aguero, Quadratic MPC with ℓ _{0}input constraint. IFAC World Congr.19(1), 10888–10893 (2014).Google Scholar
 14.E Henriksson, DE Quevedo, EGW Peters, H Sandberg, KH Johansson, Multiple loop selftriggered model predictive control for network scheduling and control. IEEE Trans. Control Syst. Technol.23(6), 2167–2181 (2015).CrossRefGoogle Scholar
 15.M Nagahara, DE Quevedo, D Nešić, in 52nd IEEE Conference on Decision and Control (CDC). Maximum handsoff control and L ^{1} optimality, (2013), pp. 3825–3830.Google Scholar
 16.M Nagahara, DE Quevedo, D Nešić, Maximum handsoff control: a paradigm of control effort minimization. IEEE Trans. Autom. Control. 61(3), 735–747 (2016).MathSciNetCrossRefGoogle Scholar
 17.M Nagahara, DE Quevedo, D Nešić, in SICE Control Division Multi Symposium 2014. Handsoff control as green control, (2014). http://arxiv.org/abs/1407.2377. Accessed 23 June 2016.
 18.T Ikeda, M Nagahara, Value function in maximum handsoff control for linear systems. Automatica. 64:, 190–195 (2016).MathSciNetCrossRefMATHGoogle Scholar
 19.S Boyd, L Vandenberghe, Convex Optimization (Cambridge University Press, Cambridge, 2004).CrossRefMATHGoogle Scholar
 20.D Gabay, B Mercier, A dual algorithm for the solution of nonlinear variational problems via finite elements approximations. Comput. Math. Appl.2:, 17–40 (1976).CrossRefMATHGoogle Scholar
 21.S Boyd, N Parikh, E Chu, B Peleato, J Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011).CrossRefMATHGoogle Scholar
 22.B O’Donoghue, G Stathopoulos, S Boyd, A splitting method for optimal control. IEEE Trans. Control Syst. Technol.21(6), 2432–2442 (2013).CrossRefGoogle Scholar
 23.JL Jerez, PJ Goulart, S Richter, GA Constantinides, EC Kerrigan, M Morari, Embedded online optimization for model predictive control at megahertz rates. IEEE Trans. Autom. Control. 59(12), 3238–3251 (2014).MathSciNetCrossRefGoogle Scholar
 24.AU Raghunathan, S Di Cairano, in Proc. 21st International Symposium on Mathematical Theory of Networks and Systems. Optimal stepsize selection in alternating direction method of multipliers for convex quadratic programs and model predictive control, (2014), pp. 807–814.Google Scholar
 25.M Annergren, A Hansson, B Wahlberg, in Decision and Control (CDC), 2012 IEEE 51st Annual Conference On. An ADMM algorithm for solving ℓ _{1} regularized MPC, (2012), pp. 4486–4491.Google Scholar
 26.M Grant, S Boyd, CVX: Matlab software for disciplined convex programming, version 2.1 (2014). http://cvxr.com/cvx. Accessed 23 June 2016.
 27.M Grant, S Boyd, in Recent Advances in Learning and Control, ed. by V Blondel, S Boyd, and H Kimura. Graph implementations for nonsmooth convex programs. Lecture Notes in Control and Information Sciences (SpringerLondon, 2008), pp. 95–110.CrossRefGoogle Scholar
 28.EJ Candes, The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique. 346(910), 589–592 (2008).MathSciNetCrossRefMATHGoogle Scholar
 29.J Eckstein, DP Bertsekas, On the DouglasRachford splitting method and proximal point algorithm for maximal monotone operators. Math. Program.55:, 293–318 (1992).MathSciNetCrossRefMATHGoogle Scholar
 30.E Ghadimi, A Teixeira, I Shames, M Johansson, Optimal parameter selection for the alternating direction method of multipliers (ADMM): quadratic problems. IEEE Trans. Autom. Control. 60(3), 644–658 (2015).MathSciNetCrossRefGoogle Scholar
 31.BS He, H Yang, SL Wang, Alternating direction method with selfadaptive penalty parameters for monotone variational inequalities. J. Optim. Theory Appl.106(2), 337–356 (2000).MathSciNetCrossRefMATHGoogle Scholar
 32.SL Wang, LZ Liao, Decomposition method with a variable parameter for a class of monotone variational inequality problems. J. Optim. Theory Appl.109(2), 415–429 (2001).MathSciNetCrossRefMATHGoogle Scholar
 33.JM Maciejowski, Predictive Control with Constraints (PrenticeHall, Essex, 2002).MATHGoogle Scholar
 34.JB Rawlings, DQ Mayne, Model Predictive Control Theory and Design (Nob Hill Publishing, Madison, 2009).Google Scholar
 35.L Grüne, J Pannek, Nonlinear Model Predictive Control (Springer, London, 2011).CrossRefMATHGoogle Scholar
 36.Y Wei, J Qiu, S Fu, Modedependent nonrational output feedback control for continuoustime semiMarkovian jump systems with timevarying delay. Nonlinear Anal. Hybrid Syst.16:, 52–71 (2015).MathSciNetCrossRefMATHGoogle Scholar
 37.Y Wei, J Qiu, HR Karimi, M Wang, \({\mathcal {H}}_{\infty }\) model reduction for continuoustime Markovian jump systems with incomplete statistics of mode information. Int. J. Syst. Sci.45(7), 1496–1507 (2014).MathSciNetCrossRefMATHGoogle Scholar
 38.Y Wei, J Qiu, HR Karimi, M Wang, Filtering design for twodimensional Markovian jump systems with statedelays and deficient mode information. Inf. Sci.269:, 316–331 (2014).MathSciNetCrossRefGoogle Scholar
 39.T Wang, Y Zhang, J Qiu, H Gao, Adaptive fuzzy backstepping control for a class of nonlinear systems with sampled and delayed measurements. IEEE Trans. Fuzzy Syst.23(2), 302–312 (2015).CrossRefGoogle Scholar
 40.T Wang, H Gao, J Qiu, A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans. Neural Netw. Learn. Syst.27(2), 416–425 (2016).MathSciNetCrossRefGoogle Scholar
 41.L Li, SX Ding, J Qiu, Y Yang, Y Zhang, Weighted fuzzy observerbased fault detection approach for discretetime nonlinear systems via piecewisefuzzy Lyapunov functions. IEEE Trans. Fuzzy Syst. (2016).Google Scholar
 42.J Qiu, SX Ding, H Gao, S Yin, Fuzzymodelbased reliable static output feedback \({\mathcal {H}}_{\infty }\) control of nonlinear hyperbolic PDE systems. IEEE Trans. Fuzzy Syst.24(2), 388–400 (2016).CrossRefGoogle Scholar
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